Zsolt Dányádi

36195554000

Publications - 4

Solution of a fuzzy resource allocation problem by various evolutionary approaches

P. Földesi Zsolt Dányádi L. T. Koczy

Publication Name: Proceedings of the 2013 Joint Ifsa World Congress and NAFIPS Annual Meeting Ifsa NAFIPS 2013

Publication Date: 2013-10-31

Volume: Unknown

Issue: Unknown

Page Range: 807-812

Description:

In this paper we present a fuzzy resource allocation and assignment problem and propose two types of biologically inspired optimization methods to solve it. The resources in question are used for the maintenance of a network of nodes, each with its specific maintenance demands over time. Our goal is to assign sufficient capacities to storage locations and transport the appropriate amount of resources to the nodes at specific times during the simulation, so that the total cost of storage, transportation and malfunction is kept to a minimum. We use fuzzy numbers to describe the parameters of all the scenarios a solution has to fit, such as the maintenance demands of each node, the additional expenditure that malfunctions bring, and also the varying cost of transportation between nodes and storage locations. The optimization methods we used were the bacterial evolutionary algorithm and the particle swarm algorithm, both with a plain and a memetic variant complemented with gradient-based local search. All of them had a version where they only worked with crisp values, and one with fuzzy solutions. We tested the effectiveness of these four approaches on four examples with varying network sizes and durations. © 2013 IEEE.

Open Access: Yes

DOI: 10.1109/IFSA-NAFIPS.2013.6608504

Fuzzy search space for correction of cognitive biases in constructing mathematical models

P. Földesi Zsolt Dányádi L. T. Koczy

Publication Name: 3rd IEEE International Conference on Cognitive Infocommunications Coginfocom 2012 Proceedings

Publication Date: 2012-12-01

Volume: Unknown

Issue: Unknown

Page Range: 585-589

Description:

In optimization the constructed mathematical models are very often idealized mappings of the actual problem. Considering human decision-making processes there is always a chance that cognitive biases occur when constructing the objective function and the constrains. Misrepresented human desires in the objective function or in the constrains result non-acceptable outcome for the decision-maker. To solve the problem of uncertainty concerning the search space we propose the use of fuzzy search space. Bacterial evolutionary algorithm is applied to demonstrate the difference between solutions with altering degree of satisfaction of the original constrains. By presenting the whole set of solutions to the human decision-maker the cognitive biases encoded into the mathematical model can be corrected. © 2012 IEEE.

Open Access: Yes

DOI: 10.1109/CogInfoCom.2012.6422047

Using multiple populations of memetic algorithms for fuzzy rule-base optimization

Krisztián Balázs Zsolt Dányádi L. T. Koczy

Publication Name: 11th IEEE International Symposium on Computational Intelligence and Informatics Cinti 2010 Proceedings

Publication Date: 2010-12-01

Volume: Unknown

Issue: Unknown

Page Range: 113-118

Description:

Evolutionary algorithms are an important branch of soft computing, being able to provide approximate solutions to problems in a reasonable amount of time. The underlying principle can be realized in an almost unlimited number of ways. This paper presents four main variants of evolutionary algorithms, and a method of running them in a topology consisting of multiple populations. The resources given to each population and migration are altered dynamically throughout the test, based on the effectiveness they show. Along with evolutionary methods, the solutions are also adjusted by gradient-based numerical optimization, in our case the Levenberg-Marquardt algorithm. These steps are added to the evolutionary processes as an extension, resulting in what are called memetic algorithms. The specific application for these methods here is optimizing fuzzy rule-bases, thereby making inference systems better at emulating a desired behavior, such as modeling a certain objective function. ©2010 IEEE.

Open Access: Yes

DOI: 10.1109/CINTI.2010.5672264

A comparative study of various evolutionary algorithms used for fuzzy rule-based inference and learning systems

Krisztián Balázs Zsolt Dányádi L. T. Koczy

Publication Name: Iccc Conti 2010 IEEE International Joint Conferences on Computational Cybernetics and Technical Informatics Proceedings

Publication Date: 2010-08-06

Volume: Unknown

Issue: Unknown

Page Range: 49-54

Description:

The goal of this paper is to provide an overview of a variety of evolutionary algorithms, comparing their efficiency on fuzzy rule-based inference and learning. Fuzzy rule-based inference can be used to model a desirable outward behavior of a system when given a specific input, which, in the case of this comparative study, is determined by a set of samples, generated by sufficiently complex objective functions. Optimizing a fuzzy rule-based inference system is a matter of finding a rule base that is as close to imitating the desired behavior as possible. While the specific applications of evolutionary methods are endless, the objective functions used here remain general in nature. © 2010 IEEE.

Open Access: Yes

DOI: 10.1109/ICCCYB.2010.5491228