Ali Jawad Ibada

57209478020

Publications - 5

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 László T. Kóczy

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

A new efficient tour construction heuristic for the Traveling Salesman Problem

Boldizsar Tuu-Szabo Ali Jawad Ibada László T. Kóczy

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

The Circle Group Heuristic to Improve the Efficiency of the Discrete Bacterial Memetic Evolutionary Algorithm Applied for TSP, TRP, and TSPTW

Boldizsar Tuu-Szabo Ali Jawad Ibada László T. Kóczy

Publication Name: Symmetry

Publication Date: 2025-10-01

Volume: 17

Issue: 10

Page Range: Unknown

Description:

The quality of the initial population is a critical factor in the convergence speed and overall performance of an optimization algorithm. A well-structured initial population can significantly enhance the exploration capabilities of the algorithm, allowing it to more efficiently traverse the solution space and converge more quickly and reliably towards optimal or near-optimal solutions. In this paper, we present the Circle Group Heuristic (CGH), a spatially structured initialization method, for generating high-quality initial populations to enhance the convergence speed of the Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA) in solving the Traveling Salesman Problem (TSP) and related combinatorial optimization problems. This work extends the CGH beyond the TSP to a broader class of routing problems. The results show that the integration of CGH into DBMEA demonstrated consistent performance improvements on the TSP, the Traveling Repairman Problem (TRP), and the Traveling Salesman Problem with Time Window (TSPTW) instances of varying sizes. In particular, CGH provided high-quality starting points that accelerated convergence and reduced computational cost. In all tested scenarios, DBMEA enhanced with CGH and consistently preserved the best-known solution quality while reducing execution time.

Open Access: Yes

DOI: 10.3390/sym17101683

Enhancement of Discrete Bacterial Memetic Evolutionary Algorithm for Solving the Travelling Repairman Problem

Boldizsar Tuu-Szabo Ali Jawad Ibada László T. Kóczy

Publication Name: Studies in Computational Intelligence

Publication Date: 2026-01-01

Volume: 1222

Issue: Unknown

Page Range: 163-171

Description:

The Traveling Repairman Problem (TRP) is concerned with repairing a set of locations rather than visiting them. In this paper, we propose an enhanced version of the Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA) to solve TRP. DBMEA is combining with a new method for generating the initial individual candidate solution which is called Circle Group Heuristic (CGH). CGH is constructed with the help of Genetic Algorithm (GA). The enhanced version of DBMEA with CGH has been tested for several benchmark reference data of TRP. The results show that the enhanced version has a faster and better solutions for most cases in comparison to state-of-the-art heuristics mentioned in the literature. Furthermore, for larger benchmark instances, it provided better solutions than the previously best-known results. These test results support the claim that the DBMEA with CGH is the most effective approach and recommend its use for the Traveling Repairman Problem, particularly for large instances.

Open Access: Yes

DOI: 10.1007/978-3-031-97879-1_18

Metaheuristics in Hierarchical Nested Structure

Boldizsar Tuu-Szabo Ali Jawad Ibada László T. Kóczy

Publication Name: Cinti 2025 IEEE 25th International Symposium on Computational Intelligence and Informatics Proceedings

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: 547-550

Description:

Metaheuristic algorithms have become indispensable tools for solving complex combinatorial optimization problems. However, their performance often depends critically on the selection of internal parameters, which are frequently tuned in an ad hoc manner. This paper investigates the hierarchical nested structure of the metaheuristic algorithm and its impact on optimization performance, where parameters of one metaheuristic are optimized using another, resulting in a multi-level optimization framework. We demonstrate this concept using a four-tier architecture: the Genetic Algorithm (GA) optimizes the Radius (R) parameter in the Circle Group Heuristic (CGH), which in turn constructs high-quality initial populations for the Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA). The DBMEA itself is a memetic algorithm that integrates global evolutionary mechanisms from Bacterial Evolutionary Algorithm (BEA) and with local search strategies (2-OPT and 3- OPT), thus comprising two inherent levels. Together, this nesting creates a four-level metaheuristic hierarchy. The DBMEA is then applied to solve variants of NP-Hard problems such as Traveling Salesman Problem (TSP). Our experiments on benchmark datasets show that this nested structure not only improves convergence speed and solution quality but also demonstrates the potential of deeply nested metaheuristic designs for scalable, robust optimization.

Open Access: Yes

DOI: 10.1109/CINTI67731.2025.11311852