Boldizsar Tuu-Szabo

57191923548

Publications - 32

A highly accurate Mamdani fuzzy inference system for tennis match predictions

Boldizsar Tuu-Szabo L. T. Koczy

Publication Name: Fuzzy Optimization and Decision Making

Publication Date: 2025-03-01

Volume: 24

Issue: 1

Page Range: 99-127

Description:

This paper presents a Mamdani fuzzy inference system (FIS) designed for predicting tennis match outcomes with greater accuracy compared to existing models such as the Weighted Elo (WElo) ranking system. By integrating factors like historical performance, surface-specific proficiency, and recent form trends, the Mamdani FIS provides a nuanced approach to forecasting match results. Central to this method is the optimization of membership functions using a Bacterial Evolutionary Algorithm (BEA), which fine-tunes parameters to better model uncertainties inherent in sports analytics. This is the further development of Nawa and Furuhashi’s original approach of fuzzy system parameter discovery, which operates on the stricter conditions concerning the membership function shapes. The study demonstrates that the Mamdani FIS outperforms the traditional methods in both predictive accuracy and profitability of betting strategies. Through extensive validation, the model achieves higher accuracy and lower log loss metrics, indicating improved reliability in prediction outcomes. Additionally, the Mamdani FIS consistently yields higher returns on investment across various betting scenarios, showcasing its practical utility in sports betting applications. Overall, the proposed Mamdani FIS represents a robust tool for tennis match prediction, with potential extensions to other sports and predictive contexts. Future research may explore incorporating additional variables and applying this fuzzy inference approach to broader areas of sports analytics.

Open Access: Yes

DOI: 10.1007/s10700-025-09440-6

The Extension of the Triple Fuzzy Time Dependent Travelling Salesman Problem Model, with a Discrete Bacterial Memetic Optimization Algorithm

Boldizsar Tuu-Szabo Ruba Almahasneh L. T. Koczy

Publication Name: Acta Polytechnica Hungarica

Publication Date: 2025-01-01

Volume: 22

Issue: 5

Page Range: 71-91

Description:

The Traveling Salesman Problem (TSP) is one of the most often studied NP-hard graph search problems. There have been numerous publications in the literature that applied various approaches to find the optimum or semi optimum solution. Although the problem is computationally intractable, but the Time Dependent Traveling Salesman Problem (TD TSP) is one of the most realistic extensions of the original TSP problem. In the TD TSP, the costs of edges between nodes vary, namely, they are assigned higher costs if they crossed a predefined oblong shaped area (to represent the jam region in the city center). Realizing that the jam regions and the rush hours costs on a tour are uncertain and can never be accurately represented by concrete numbers, we introduced the novel 3FTD TSP (Triple Fuzzy Time Dependent Traveling Salesman Problem); a fully fuzzified model of the original TD TSP. The 3FTD TSP utilizes fuzzy values for determining the costs between any two nodes within the traffic jam regions and during the rush hours periods more precisely. In this paper, we extend the 3FTD TSP further and apply it on the biggest universal instances in the literature in pursuit of testing the generality and applicability of the 3FTD TSP on real-life scenarios. To support the claim of the model’s efficiency, we propose the application of the DBMEA (Discrete Bacterial Memetic Evolutionary Algorithm), as a meta-heuristic and the classic GA (Genetic Algorithm) enabling the reader to compare the accuracy and the speed of (quasi-) optimum solutions convergence.

Open Access: Yes

DOI: 10.12700/aph.22.5.2025.5.4

An Efficient Tour Construction Heuristic for Generating the Candidate Set of the Traveling Salesman Problem with Large Sizes

P. Földesi Boldizsar Tuu-Szabo L. T. Koczy

Publication Name: Mathematics

Publication Date: 2024-10-01

Volume: 12

Issue: 19

Page Range: Unknown

Description:

In this paper, we address the challenge of creating candidate sets for large-scale Traveling Salesman Problem (TSP) instances, where choosing a subset of edges is crucial for efficiency. Traditional methods for improving tours, such as local searches and heuristics, depend greatly on the quality of these candidate sets but often struggle in large-scale situations due to insufficient edge coverage or high time complexity. We present a new heuristic based on fuzzy clustering, designed to produce high-quality candidate sets with nearly linear time complexity. Thoroughly tested on benchmark instances, including VLSI and Euclidean types with up to 316,000 nodes, our method consistently outperforms traditional and current leading techniques for large TSPs. Our heuristic’s tours encompass nearly all edges of optimal or best-known solutions, and its candidate sets are significantly smaller than those produced with the POPMUSIC heuristic. This results in faster execution of subsequent improvement methods, such as Helsgaun’s Lin–Kernighan heuristic and evolutionary algorithms. This substantial enhancement in computation time and solution quality establishes our method as a promising approach for effectively solving large-scale TSP instances.

Open Access: Yes

DOI: 10.3390/math12192960

A Hybrid Discrete Memetic Algorithm for Solving Flow-Shop Scheduling Problems

Olivér Hornyák Károly Nehéz Boldizsar Tuu-Szabo Levente Fazekas L. T. Koczy

Publication Name: Algorithms

Publication Date: 2023-09-01

Volume: 16

Issue: 9

Page Range: Unknown

Description:

Flow-shop scheduling problems are classic examples of multi-resource and multi-operation scheduling problems where the objective is to minimize the makespan. Because of the high complexity and intractability of the problem, apart from some exceptional cases, there are no explicit algorithms for finding the optimal permutation in multi-machine environments. Therefore, different heuristic approaches, including evolutionary and memetic algorithms, are used to obtain the solution—or at least, a close enough approximation of the optimum. This paper proposes a novel approach: a novel combination of two rather efficient such heuristics, the discrete bacterial memetic evolutionary algorithm (DBMEA) proposed earlier by our group, and a conveniently modified heuristics, the Monte Carlo tree method. By their nested combination a new algorithm was obtained: the hybrid discrete bacterial memetic evolutionary algorithm (HDBMEA), which was extensively tested on the Taillard benchmark data set. Our results have been compared against all important other approaches published in the literature, and we found that this novel compound method produces good results overall and, in some cases, even better approximations of the optimum than any of the so far proposed solutions.

Open Access: Yes

DOI: 10.3390/a16090406

Experiments with the Discrete Bacterial Memetic Evolutionary Algorithm for Solving the Cumulative Capacitated Vehicle Routing Problem

P. Földesi Boldizsar Tuu-Szabo L. T. Koczy

Publication Name: Studies in Computational Intelligence

Publication Date: 2023-01-01

Volume: 1040

Issue: Unknown

Page Range: 87-92

Description:

In this paper we present our initial experiments with the Discrete Bacterial Memetic Evolutionary Algorithm for solving the Cumulative Capacitated Vehicle Routing Problem. The algorithm was tested on instances proposed in the literature. However our method was able to find the optimal solution for small (around 50 nodes) instances, but its convergence speed is low. In the last section some of our ideas to improve the performance of the algorithm were presented.

Open Access: Yes

DOI: 10.1007/978-3-031-07707-4_11

Effect of the initial population construction on the DBMEA algorithm searching for the optimal solution of the traveling salesman problem

Boldizsar Tuu-Szabo Ali Jawad Ibada L. T. Koczy

Publication Name: Infocommunications Journal

Publication Date: 2022-09-01

Volume: 14

Issue: 3

Page Range: 72-78

Description:

There are many factors that affect the performance of the evolutionary and memetic algorithms. One of these factors is the proper selection of the initial population, as it represents a very important criterion contributing to the convergence speed. Selecting a conveniently preprocessed initial population definitely increases the convergence speed and thus accelerates the probability of steering the search towards better regions in the search space, hence, avoiding premature convergence towards a local optimum. In this paper, we propose a new method for generating the initial individual candidate solution called Circle Group Heuristic (CGH) for Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA), which is built with aid of a simple Genetic Algorithm (GA). CGH has been tested for several benchmark reference data of the Travelling Salesman Problem (TSP). The practical results show that CGH gives better tours compared with other well-known heuristic tour construction methods.

Open Access: Yes

DOI: 10.36244/ICJ.2022.3.9

Analyzing the Performance of TSP Solver Methods

P. Földesi Boldizsar Tuu-Szabo L. T. Koczy

Publication Name: Studies in Computational Intelligence

Publication Date: 2022-01-01

Volume: 955

Issue: Unknown

Page Range: 65-71

Description:

In this paper we analyze the efficiency of three TSP solver methods: the best-performing exact Concorde algorithm, the state-of-the-art inexact Helsgaun’s Lin–Kernighan heuristic and our Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA). In our analysis the run time predictability was also taken into account, not only the tour quality and the run time properties. Three models (polynomial, exponential, square-root exponential) were fitted to the mean run times of VLSI (Very-large-scale integration) instances up to 20,000 nodes. The DBMEA produces the highest (close to 1) R2-values for each model. The Concorde algorithm shows very low run time predictability.

Open Access: Yes

DOI: 10.1007/978-3-030-88817-6_8

Comparison of Discrete Memetic Evolutionary Metaheuristics for TSP

P. Földesi Boldizsar Tuu-Szabo Gergő Fogarasi L. T. Koczy

Publication Name: Studies in Computational Intelligence

Publication Date: 2022-01-01

Volume: 955

Issue: Unknown

Page Range: 29-37

Description:

In our paper we compare discrete memetic evolutionary metaheuristics (and other algorithms) which are applicable (also) for the widely studied and industrially applied (symmetric, Euclidean) NP-hard combinatorial optimization problem called Traveling Salesman Problem (TSP) such as DBMEA (Discrete Bacterial Memetic Evolutionary Algorithm), DMTLBO (Discrete Memetic Teaching–Learning Based Optimization) not to mention DMSSA (Discrete Memetic Squirrel Search Algorithm) algorithms. The comparisons occurred under the same fixed conditions.

Open Access: Yes

DOI: 10.1007/978-3-030-88817-6_4

Local Binary Pattern-Based Fingerprint Matching

P. Földesi Boldizsar Tuu-Szabo Gábor Kovács L. T. Koczy Szilvia Nagy

Publication Name: Studies in Computational Intelligence

Publication Date: 2022-01-01

Volume: 959

Issue: Unknown

Page Range: 183-188

Description:

In this paper we propose an image-based fingerprint recognition system. The method is based on Local Binary Pattern features extracted from the region of the fingerprint image around the core point. The experiments on the FVC2002 fingerprint databases show the effectiveness of the proposed approach.

Open Access: Yes

DOI: 10.1007/978-3-030-74970-5_21

A new efficient tour construction heuristic for the Traveling Salesman Problem

Boldizsar Tuu-Szabo Ali Jawad Ibada L. T. Koczy

Publication Name: ACM International Conference Proceeding Series

Publication Date: 2021-04-10

Volume: Unknown

Issue: Unknown

Page Range: 71-76

Description:

The creation of the initial population is an essential part of the population based evolutionary algorithms. An appropriate initial population could lead to much faster convergence speed; in contrast, an inappropriate initial population could even cause getting stuck in a local optimum. In this paper, we will propose a new efficient heuristic method to create initial individuals for the Traveling Salesman Problem (TSP), which we will call Circle Group Heuristic (CGH). The results show that CGH creates better tours compared with other well-known heuristic tour construction methods.

Open Access: Yes

DOI: 10.1145/3461598.3461610

Optimization of the time-dependent traveling salesman problem using interval-valued intuitionistic fuzzy sets

P. Földesi Boldizsar Tuu-Szabo Ruba Almahasneh L. T. Koczy

Publication Name: Axioms

Publication Date: 2020-06-01

Volume: 9

Issue: 2

Page Range: Unknown

Description:

This study proposes a new model and approach for solving a realistic extension of the Time-Dependent Traveling Salesman Problem, by using the concept of distance between interval-valued intuitionistic fuzzy sets. For this purpose, we developed an interval-valued fuzzy degree repository based on the relations between rush hour periods and traffic regions in the "city center areas", and then we utilized the interval-valued intuitionistic fuzzy weighted arithmetic average to aggregate fuzzy information to be able to quantify the delay in any given trip between two nodes (cities). The proposed method is illustrated by a simple numerical example.

Open Access: Yes

DOI: 10.3390/AXIOMS9020053

Extension of the Time Dependent Travelling Salesman Problem with Interval Valued Intuitionistic Fuzzy Model Applying Memetic Optimization Algorithm

P. Földesi Boldizsar Tuu-Szabo Ruba Almahasneh L. T. Koczy

Publication Name: ACM International Conference Proceeding Series

Publication Date: 2020-03-21

Volume: Unknown

Issue: Unknown

Page Range: 111-118

Description:

The Time Dependent Traveling Salesman Problem (TD TSP) is an extension of the classic Traveling Salesman Problem towards more realistic conditions. TSP is one of the most extensively studied NP-complete graph search problems. In TD TSP, the edges are assigned different weights, depending on whether they are traveled in the traffic jam regions (such as busy city centers) and during rush hour periods, or not. In such circumstances, edges are assigned higher costs, expressed by a multiplying factor. In this paper, we introduce a novel and even more realistic approach, the Interval Intuitionistic Fuzzy Time Dependent Traveling Salesman Problem (IVIFTD TSP); which is a further extension of the classic TD TSP, with the additional notion of deploying interval valued intuitionistic fuzzy for describing uncertainties. The core concept employs interval valued intuitionistic fuzzy sets for quantifying the traffic jam regions, and the rush hour periods loss (those are additional costs of the travel between nodes), which are always uncertain in real life. Since type-2 (such as inter valued) fuzzy sets have the potential to provide better performance in modeling problems with higher uncertainties than the traditional fuzzy set, the new approach it may be considered as an extended, practically more applicable, extended version of the original abstract problem. The optimization of such a complex model is obviously very difficult; it is a mathematically intractable problem. However, the Discrete Bacterial Memetic Evolutionary Algorithm proposed earlier by the authors' team has shown sufficient efficiency, general applicability for similar type problems and good predictability in terms of problem size, thus it is applied for the optimization of the concrete instances.

Open Access: Yes

DOI: 10.1145/3396474.3396490

Fuzzy set based models comparative study for the td tsp with rush hours and traffic regions

P. Földesi Boldizsar Tuu-Szabo Ruba Almahasneh L. T. Koczy

Publication Name: Communications in Computer and Information Science

Publication Date: 2020-01-01

Volume: 1238 CCIS

Issue: Unknown

Page Range: 699-714

Description:

This study compares three fuzzy based model approaches for solving a realistic extension of the Time Dependent Traveling Salesman Problem. First, the triple Fuzzy (3FTD TSP) model, where the uncertain costs between the nodes depend on time are expressed by fuzzy sets. Second, the intuitionistic fuzzy (IFTD TSP) approach, where including hesitation was suitable for quantifying the jam regions and the bimodal rush hour periods during the day. Third, the interval-valued intuitionistic fuzzy sets model, that calculates the interval-valued intuitionistic fuzzy weighted arithmetic average (IIFWAA) of the edges’ confirmability degrees and non-confirmability degrees, was contributing in minimizing the information loss in cost (delay) calculation between nodes.

Open Access: Yes

DOI: 10.1007/978-3-030-50143-3_55

Quasi-Optimization of the Time Dependent Traveling Salesman Problem by Intuitionistic Fuzzy Model and Memetic Algorithm

P. Földesi Boldizsar Tuu-Szabo Ruba Almahasneh L. T. Koczy

Publication Name: Studies in Computational Intelligence

Publication Date: 2020-01-01

Volume: 872

Issue: Unknown

Page Range: 239-253

Description:

The Traveling Salesman Problem (TSP) is an NP-hard graph search problem. Despite having numerous modifications of the original abstract problem, Time Dependent Traveling Salesman Problem (TD TSP) was one of the most realistic extensions under real traffic conditions. In TD TSP the edges between nodes are assigned higher costs (weights), if they were traveled during the rush hour periods, or crossed the traffic jam regions, such as the city center(s). In this paper we introduce an even more real-life motivated approach, the Intuitionistic Fuzzy Time Dependent Traveling Salesman Problem (IFTD TSP), which is a further extension of the TSP, and also of the classic TD TSP, with the additional notion of using intuitionistic fuzzy sets for the definition of uncertain costs, time, and space of the rush hour—traffic jam region affecting graph sections. In IFTD TSP we use fuzzy memberships and non-memberships sets for estimating the vague costs between nodes in order to quantify the behavior of traffic jam regions, and the rush hour periods. Since intuitionistic fuzzy sets are generalizations of classic fuzzy sets, our approach may be considered an extension and substitution of the original abstract TD TSP problem, even, of the (classic) Fuzzy TD TSP. Lastly, DBMEA (Discrete Bacterial Memetic Evolutionary Algorithm) was applied on the IFTD TSP model, the results of the simulation runs based on some extensions of the benchmarks generated from the original TD TSP data set showed quite good and promising preliminary results.

Open Access: Yes

DOI: 10.1007/978-3-030-34409-2_14

A memetic version of the bacterial evolutionary algorithm for discrete optimization problems

P. Földesi Boldizsar Tuu-Szabo L. T. Koczy

Publication Name: Advances in Intelligent Systems and Computing

Publication Date: 2020-01-01

Volume: 945

Issue: Unknown

Page Range: 44-55

Description:

In this paper we present our test results with our memetic algorithm, the Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA). The algorithm combines the Bacterial Evolutionary Algorithm with discrete local search techniques (2-opt and 3-opt). The algorithm has been tested on four discrete NP-hard optimization problems so far, on the Traveling Salesman Problem, and on its three variants (the Traveling Salesman Problem with Time Windows, the Traveling Repairman Problem, and the Time Dependent Traveling Salesman Problem). The DBMEA proved to be efficient for all problems: it found optimal or close-optimal solutions. For the Traveling Repairman Problem the DBMEA outperformed even the state-of-the-art methods. The preliminary version of this paper was presented at the 3rd Conference on Information Technology, Systems Research and Computational Physics, 2–5 July 2018, Cracow, Poland [1].

Open Access: Yes

DOI: 10.1007/978-3-030-18058-4_4

An efficient evolutionary metaheuristic for the traveling repairman (Minimum latency) problem

P. Földesi Boldizsar Tuu-Szabo L. T. Koczy

Publication Name: International Journal of Computational Intelligence Systems

Publication Date: 2020-01-01

Volume: 13

Issue: 1

Page Range: 781-793

Description:

In this paper we revisit the memetic evolutionary family of metaheuristics, called Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA), whose members combine Furuhashi’s Bacterial Evolutionary Algorithm and various discrete local search techniques. These algorithms have proven to be efficient approaches for the solution of NP-hard discrete optimization problems such as the Traveling Salesman Problem (TSP) with Time Windows. This paper presents our results in solving the Traveling Repairman Problem (also called Minimum Latency Problem) with a DBMEA variant. The results are compared with state-of-the-art heuristics found in the literature. The DBMEA in most cases turned out to be faster than all other methods, and for the bigger benchmark instances it was also found to have better solutions than the former best-known results. Based on these test results we claim to have found the best approach and thus we suggest the use of the DBMEA for the Traveling Repairman Problem, especially for large instances.

Open Access: Yes

DOI: 10.2991/ijcis.d.200529.001

The discrete bacterial memetic evolutionary algorithm for solving the one-commodity pickup-and-delivery traveling salesman problem

P. Földesi Boldizsar Tuu-Szabo L. T. Koczy

Publication Name: Studies in Computational Intelligence

Publication Date: 2020-01-01

Volume: 819

Issue: Unknown

Page Range: 15-22

Description:

In this paper we propose a population based memetic algorithm, the Discrete Bacterial Memetic Evolutionary Algorithm for solving the one-commodity Pickup-and-Delivery Traveling Salesman Problem. The algorithm was tested on benchmark instances up to 100 nodes, and the results were compared with the state-of-the art methods in the literature. For all instances the DBMEA found optimal or close-optimal solutions.

Open Access: Yes

DOI: 10.1007/978-3-030-16024-1_3

Altered dynamics may drift pathological fibrillization in membraneless organelles

N. Duro Boldizsar Tuu-Szabo G. Hoffka M. Fuxreiter

Publication Name: Biochimica Et Biophysica Acta Proteins and Proteomics

Publication Date: 2019-10-01

Volume: 1867

Issue: 10

Page Range: 988-998

Description:

Protein phase transition can generate non-membrane bound cellular compartments, which can convert from liquid-like to solid-like states. While the molecular driving forces of phase separation have been largely understood, much less is known about the mechanisms of material-state conversion. We apply a recently developed algorithm to describe the weak interaction network of multivalent motifs, and simulate the effect of pathological mutations. We demonstrate that linker dynamics is critical to the material-state of biomolecular condensates. We show that linker flexibility/mobility is a major regulator of the weak, heterogeneous meshwork of multivalent motifs, which promotes phase transition and maintains a liquid-like state. Decreasing linker dynamics increases the propensity of amyloid-like fragments via hampering the motif-exchange and reorganization of the weak interaction network. In contrast, increasing linker mobility may compensate rigidifying mutations, suggesting that the meshwork of weak, variable interactions may provide a rescue mechanism from aggregation. Motif affinity, on the other hand, has a moderate impact on fibrillization. Here we demonstrate that the fuzzy framework provides an efficient approach to handle the intricate organization of membraneless organelles, and could also be applicable to screen for pathological effects of mutations.

Open Access: Yes

DOI: 10.1016/j.bbapap.2019.04.005

Modeling of Fuzzy Rule-base Algorithm for the Time Dependent Traveling Salesman Problem

P. Földesi Boldizsar Tuu-Szabo Ruba Almahasneh L. T. Koczy

Publication Name: IEEE International Conference on Fuzzy Systems

Publication Date: 2019-06-01

Volume: 2019-June

Issue: Unknown

Page Range: Unknown

Description:

The Traveling Salesman Problem (TSP) is one of the most extensively studied NP-hard graph search problems. In the literature, there have been numerous published attempts, applying various approaches in order to find the optimum (least cost) or semi optimum solution. Time Dependent Traveling Salesman Problem (TD TSP) is one of the most sufficient extensions and modifications of the original TSP problem. In TD TSP the costs of edges between nodes varies, they are assigned higher cost in the traffic jam region, such as city center or during the rush hour periods. In this paper, we introduce an even more realistic approach, the 3FTD TSP (Triple Fuzzy Time Dependent Traveling Salesman Problem); a fuzzified model of the original TD TSP. The 3FTD TSP presents a variation of the TD TSP utilizing fuzzy values in the cost between two nodes (shops, cities, etc.), the geographical areas of the traffic jam region, and also the rush hour period. The goal is to give a practically useful and realistic alternative of the basic TD TSP problem. In order to calculate the (quasi-) optimum solution, the Discrete Bacterial Memetic Evolutionary Algorithm was used, since it has been proven to be rather efficient (and predictably) in a wide range of NP-hard problems, including the original TSP and the TD TSP as well. The results from the runs based on the extensions of the family of benchmarks generated from the original TD TSP benchmark data set showed rather good and credible initial results.

Open Access: Yes

DOI: 10.1109/FUZZ-IEEE.2019.8858853

Identification and dynamic analysis of crime hot-spots in Hungary by a complex Computer Intelligence approach

P. Földesi Boldizsar Tuu-Szabo Gábor Kovács Gergő Fogarasi L. T. Koczy Szilvia Nagy

Publication Name: Ines 2019 IEEE 23rd International Conference on Intelligent Engineering Systems Proceedings

Publication Date: 2019-04-01

Volume: Unknown

Issue: Unknown

Page Range: 247-252

Description:

In the field of forensic science, crime maps are widely used. The representation of the data and analysis could offer some steps forward for crime prevention. Clustering is able to help identify criminal hot-spots and further analysis designate which require intervention. The aim of this study is to present a first step in the analysis of Hungary-related criminal information.

Open Access: Yes

DOI: 10.1109/INES46365.2019.9109437

Intuitionistic Fuzzy Model of Traffic Jam Regions and Rush Hours for the Time Dependent Traveling Salesman Problem

P. Földesi Boldizsar Tuu-Szabo Ruba Almahasneh L. T. Koczy

Publication Name: Advances in Intelligent Systems and Computing

Publication Date: 2019-01-01

Volume: 1000

Issue: Unknown

Page Range: 123-134

Description:

The Traveling Salesman Problem (TSP) is one of the most extensively studied NP-hard graph search problems. Many researchers published numerous approaches for quality solutions, applying various techniques in order to find the optimum (least cost) or semi optimum solution. Moreover, there are many different extensions and modifications of the original problem, The Time Dependent Traveling Salesman Problem (TD TSP) is a prime example. TD TSP indeed was one of the most realistic extensions of the original TSP towards assessment of traffic conditions [1]. Where the edges between nodes are assigned different cost (weight), considering whether they are traveled during the rush hour periods or they cross the traffic jam regions. In such conditions edges are assigned higher costs [1]. In this paper we introduce an even more realistic approach, the IFTD TSP (Intuitionistic Fuzzy Time Dependent Traveling Salesman Problem); which is an extension of the classic TD TSP with the additional notion of intuitionistic fuzzy sets. Our core concept is to employ intuitionistic fuzzy sets of the cost between nodes to quantify traffic jam regions, and the rush hour periods. Since the intuitionistic fuzzy sets are generalizations of the original fuzzy sets [2], then our approach is a usefully extended, alternative model of the original abstract problem. By demonstrating the addition of intuitionistic fuzzy elements to quantify the intangible jam factors and rush hours, and creating an inference system that approximates the tour cost in a more realistic way [3]. Since our motivation is to give a useful and practical alternative (extension) of the basic TD TSP problem, the DBMEA (Discrete Bacterial Memetic Evolutionary Algorithm) was used in order to calculate the (quasi-)optimum or semi optimum solution. DBMEA has been proven to be effective and efficient in a wide segment of NP-hard problems, including the original TSP and the TD TSP as well [4]. The results from the runs based on the extensions of the family of benchmarks generated from the original TD TSP benchmark data set showed rather good and credible initial results.

Open Access: Yes

DOI: 10.1007/978-3-030-21920-8_12

Crime “hot-spots” identification and analysis in Hungary by computational intelligence

P. Földesi Boldizsar Tuu-Szabo Gábor Kovács Gergő Fogarasi L. T. Koczy Szilvia Nagy

Publication Name: Acta Polytechnica Hungarica

Publication Date: 2019-01-01

Volume: 16

Issue: 10

Page Range: 137-155

Description:

In the constantly growing and widening field of forensic science, crime maps are used in versatile ways. The representation of the data and analysis could offer some steps toward crime prevention and helps understand patterns, in terms of a timely distribution of crime types. Clustering is able to help identify criminal hot-spots and additional analysis may determine which areas require intervention. The aim of this study is to present an analysis of criminal information related to Hungary, in annual and monthly breakdown.

Open Access: Yes

DOI: 10.12700/APH.16.10.2019.10.9

Statistical Analysis of the Performance of the State-of-the-Art Methods for Solving TSP Variants

P. Földesi Boldizsar Tuu-Szabo L. T. Koczy

Publication Name: Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

Publication Date: 2019-01-01

Volume: 11909 LNAI

Issue: Unknown

Page Range: 255-262

Description:

In this paper we analyze the efficiency of the state-of-the-art methods for solving two TSP variants, the Traveling Salesman Problem with Time Windows and one-commodity Pickup-and-Delivery Traveling Salesman Problem. Three models (polynomial, exponential, square-root exponential) were fitted to the mean run times of each method. The parameters of the curves, the R2-values and the RMSE values were compared.

Open Access: Yes

DOI: 10.1007/978-3-030-33709-4_23

A population based metaheuristic for the minimum latency problem

P. Földesi Boldizsar Tuu-Szabo L. T. Koczy

Publication Name: Studies in Computational Intelligence

Publication Date: 2019-01-01

Volume: 796

Issue: Unknown

Page Range: 113-121

Description:

In this paper we present a population based metaheuristic for solving the Minimum Latency Problem, which is the combination of bacterial evolutionary algorithm with local search techniques. The algorithm was tested on TSPLIB benchmark instances, and the results are competitive in terms of accuracy and runtimes with the state-of-the art methods. Except for two instances our algorithm found the best-known solution, and for the biggest tested instance it outperformed the best-known solution. The runtime was on average 30% faster than the most efficient method in the literature.

Open Access: Yes

DOI: 10.1007/978-3-030-00485-9_13

Enhanced discrete bacterial memetic evolutionary algorithm - An efficacious metaheuristic for the traveling salesman optimization

P. Földesi Boldizsar Tuu-Szabo L. T. Koczy

Publication Name: Information Sciences

Publication Date: 2018-09-01

Volume: 460-461

Issue: Unknown

Page Range: 389-400

Description:

In this paper we present a novel universal metaheuristic, Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA), which is based on the combination of the Bacterial Evolutionary Algorithm and local search techniques, used for solving NP-hard optimization problems. The algorithm was tested on a series of symmetric Traveling Salesman Problems (TSP) and Traveling Salesman Problem with time windows (TSPTW) benchmarks. The size of the symmetric TSP benchmarks went up to 5 000 cities. In all cases the DBMEA algorithm produced optimal or near-optimal solutions and the difference from the known best values was within 0.16%. While for large size problems it was much faster than the Concorde solver, it was found to be slower compared to the Helsgaun-Lin-Kernighan heuristic, which is the most efficient TSP solver method. With some slight modifications the same algorithm was also tested on TSP with time windows (TSPTW) benchmark instances. In most cases the DBMEA procedure found the known best solutions, and it was again the second fastest method compared with the state-of-the-art techniques for the TSPTW. DBMEA is called efficacious because it is a universal method. It can be efficiently applied to various NP-hard optimization problems and, as in all cases, it results in the optimal or a very near-optimal solutions, while its runtime is very predictable in terms of the size of the problem, and the topology of the instance does not affect its runtime significantly. Even though heuristics developed for a particular type of problem might perform better for that restricted class, our novel method proposed here is universally applicable and may be deployed successfully for optimizing other discreet NP-hard graph search and optimization problems as well.

Open Access: Yes

DOI: 10.1016/j.ins.2017.09.069

Discrete bacterial memetic evolutionary algorithm for the time dependent traveling salesman problem

P. Földesi Boldizsar Tuu-Szabo L. T. Koczy

Publication Name: Communications in Computer and Information Science

Publication Date: 2018-01-01

Volume: 853

Issue: Unknown

Page Range: 523-533

Description:

The Time Dependent Traveling Salesman Problem (TDTSP) that is addressed in this paper is a variant of the well-known Traveling Salesman Problem. In this problem the distances between nodes vary in time (are longer in rush hours in the city centre), Our Discrete Bacterial Evolutionary Algorithm (DBMEA) was tested on benchmark problems (on bier127 and on a self-generated problem with 250 nodes) with various jam factors. The results demonstrate the effectiveness of the algorithm.

Open Access: Yes

DOI: 10.1007/978-3-319-91473-2_45

Simulations of higher-order protein organizations using a fuzzy framework

Boldizsar Tuu-Szabo M. Fuxreiter L. T. Koczy

Publication Name: Complexity

Publication Date: 2018-01-01

Volume: 2018

Issue: Unknown

Page Range: Unknown

Description:

Spatiotemporal regulation of the biochemical information is often linked to supramolecular organizations proteins and nucleic acids, the driving forces of which have yet to be elucidated. Although the critical role of multivalency in phase transition has been recognized, the organization principles of higher-order structures need to be understood. Here, we present a fuzzy mathematical framework to handle the heterogeneity of interactions patterns and the resultant multiplicity of conformational states in protein assemblies. In this model, redundant binding motifs can establish simultaneous and partial interactions with multiple targets. We demonstrate that these multivalent, weak contacts facilitate polymer formation, while recapitulating the observed valency-dependence. In addition, the impact of linker dynamics and motif binding affinity, as well as the interplay between the two effects was studied. Our results support that fuzziness is a critical factor in driving higher-order protein organizations, and this could be used as a general framework to simulate different kinds of supramolecular assemblies.

Open Access: Yes

DOI: 10.1155/2018/6360846

An effective Discrete Bacterial Memetic Evolutionary Algorithm for the Traveling Salesman Problem

P. Földesi Boldizsar Tuu-Szabo L. T. Koczy

Publication Name: International Journal of Intelligent Systems

Publication Date: 2017-08-01

Volume: 32

Issue: 8

Page Range: 862-876

Description:

In recent years, a large number of evolutionary and other population-based heuristics were proposed in the literature. In 2009, we suggested to combine the very efficient bacterial evolutionary algorithm with local search as a new Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA) (Farkas et al., In: Towards intelligent engineering & information technology, Studies in Computational Intelligence, Vol 243. Berlin, Germany: Springer-Verlag; 2009. pp 607–625). The method was tested on one of Traveling Salesman Problem (TSP) benchmark problems, and a difference was found between the real optimum calculated by the new and the published result because the Concorde and the Lin–Kernighan algorithm use an approximation substituting distances of points by the closest integer values. We modified the Concorde algorithm using real cost values to compare with our results. In this paper, we systematically investigate TSPLIB benchmark problems and other VLSI benchmark problems (http://www.math.uwaterloo.ca/tsp/vlsi/index.html) and compare the following values: optima found by the DBMEA heuristic and by the modified Concorde algorithm with real cost values, run times of DBMEA, modified Concorde, and Lin–Kernighan heuristic. In this paper, for the evaluation of metaheuristic techniques, we suggest the usage of predictability of the successful run in addition to the accuracy of the result and the computational cost as third property. We will show that in the case of DBMEA, the run time is more predictable than in the case of Concorde algorithm, so we suggest the use of DBMEA heuristic as very efficient for the solution of TSP and other nondeterministic polynomial-time hard optimization problems.

Open Access: Yes

DOI: 10.1002/int.21893

A population based metaheuristic for traveling salesman type problems

P. Földesi Boldizsar Tuu-Szabo L. T. Koczy

Publication Name: 2017 International Conference on Fuzzy Theory and Its Applications Ifuzzy 2017

Publication Date: 2017-03-09

Volume: 2017-November

Issue: Unknown

Page Range: 1-5

Description:

In this paper we present a metaheuristic method, called DBMEA. It combines the bacterial evolutionary algorithm with local search techniques. Based on our test results it can be used for solving efficiently more discrete optimization problems. The algorithm was tested on Traveling Salesman Problem and Traveling Repairman Problem (TRP) benchmark instances found in the literature. In the case of TSP the DBMEA algorithm produced optimal or near-optimal solutions for all tested instances. Although the most efficient TSP solver method, the Helsgaun's Lin-Kernighan heuristic was faster than DBMEA, but in the case of DBMEA the runtime was more predictable than it the case of other methods. In the case of TRP the results are competitive in terms of accuracy and runtimes with the state-of-the art methods. Except two instances our algorithm found the best-known solutions, and for the biggest tested instance it found new best solution. The runtime was on average 30% faster than the most efficient heuristic in the literature.

Open Access: Yes

DOI: 10.1109/iFUZZY.2017.8311797

An efficient new memetic method for the traveling salesman problem with time windows

P. Földesi Boldizsar Tuu-Szabo L. T. Koczy

Publication Name: Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

Publication Date: 2017-01-01

Volume: 10607 LNAI

Issue: Unknown

Page Range: 426-436

Description:

In this paper we present a new memetic algorithm, which is called Discrete Bacterial Memetic Evolutionary Algorithm for solving the Traveling Salesman Problem with time windows (TSPTW). This method is the combination of bacterial evolutionary algorithm with 2-opt and 3-opt local searches. The algorithm was already tested on symmetric Traveling Salesman Problem (TSP) benchmark instances up to 5000 cities. It showed good properties in terms of tour lengths, runtimes and predictability of runtimes, so we decide to examine other variants of TSP with our algorithm. With some slight modifications our method was tested on TSP with time windows benchmark instances. Our test results were compared with the state-of-the art methods. In most cases our algorithm found the best-known solutions, and in terms of solution quality and runtime it is the second best method.

Open Access: Yes

DOI: 10.1007/978-3-319-69456-6_35

Improved discrete bacterial memetic evolutionary algorithm for the traveling salesman problem

P. Földesi Boldizsar Tuu-Szabo L. T. Koczy

Publication Name: Advances in Intelligent Systems and Computing

Publication Date: 2017-01-01

Volume: 532

Issue: Unknown

Page Range: 27-38

Description:

In recent years a large number of evolutionary and other population based heuristics were proposed in the literature for solving NP-hard optimization problems. In 2015 we presented a Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA) for The Traveling Salesman Problem. It provided results tested on series of TSP problems. In this paper we present an improved version of the DBMEA algorithm, where the local search is accelerated, which is the most time consuming part of the original DBMEA algorithm. This modification led to a significant improvement, the runtime of the improved DBMEA was 5– 20 times shorter than the original DBMEA algorithm. Our DBMEA algorithms calculate real value costs better than integer ones, so we modified the Concorde algorithm be comparable with our results. The improved DBMEA was tested on several TSPLIB benchmark problems and other VLSI benchmark problems and the following values were compared: - optima found by the improved DBMEA heuristic and by the modified Concorde algorithm with real cost values - runtimes of original DBMEA, improved DBMEA and modified Concorde algorithm. Based on the test results we suggest the use of the improved DBMEA heuristic for the more efficient solution of TSP problems.

Open Access: Yes

DOI: 10.1007/978-3-319-48517-1_3

A discrete bacterial memetic evolutionary algorithm for the traveling salesman problem

P. Földesi Boldizsar Tuu-Szabo L. T. Koczy

Publication Name: 2016 IEEE Congress on Evolutionary Computation CEC 2016

Publication Date: 2016-11-14

Volume: Unknown

Issue: Unknown

Page Range: 3261-3267

Description:

This paper presents a Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA) for the Traveling Salesman Problem. This algorithm combines the very efficient bacterial evolutionary algorithm with 2-opt and 3-opt local searches. Our approach was tested on TSPLIB and other VLSI benchmark problems. In this paper our computational results (minimal tour lengths, run times) are compared with other efficient TSP solver algorithms (Lin-Kernighan, Concorde). We will show that in significant number of the published benchmark problems the optimal tour was not found by the Concorde algorithm and the Lin-Kernighan heuristic because this approaches use an approximation substituting distances of points by the closest integer values. We suggest the substitution of the benchmark result set by the real optima calculated by the new DBMEA algorithm and the use of DBMEA heuristic as more precise for the solution of TSP and other NP-hard optimization problems.

Open Access: Yes

DOI: 10.1109/CEC.2016.7744202