Michal Pluhacek

On the behavior and performance of chaos driven PSO algorithm with inertia weight

In this paper, the utilization of chaos pseudorandom number generators based on three different chaotic maps to alter the behavior and overall performance of PSO algorithm is proposed. This paper presents results of testing the performance and behavior of the proposed algorithm on typical benchmark functions that represent unimodal and multimodal problems. The promising results are analyzed and discussed.

Chaos PSO algorithm driven alternately by two different chaotic maps - An initial study

In this paper, a new approach for chaos driven PSO algorithm is proposed. Two different chaotic maps are alternately used as pseudorandom number generators and switched over during the run of chaos driven PSO algorithm. The motivation for this research came from the previous successful experiments with PSO algorithm driven by different chaotic maps. Promising results of this innovative approach are presented in the results section and briefly analyzed.

Particle swarm optimization algorithm driven by multichaotic number generator

In this paper, the utilization of different chaotic systems as pseudo-random number generators (PRNGs) for velocity calculation in the PSO algorithm are proposed. Two chaos-based PRNGs are used alternately within one run of the PSO algorithm and dynamically switched over when a certain criterion is met. By using this unique technique, it is possible to improve the performance of PSO algorithm as it is demonstrated on different benchmark functions.

Complex network analysis of differential evolution algorithm applied to flowshop with no-wait problem

This paper analyses the attributes of population dynamics of Differential Evolution algorithm using Complex Network Analysis tools. The population is visualised as an evolving complex network, which exhibits non-trivial features. Complex network attributes such as adjacency graph gives interconnectivity, centralities give the overview of convergence and stagnation, whereas cliques outlines the depth of interconnection and subgraphs within the population. The community graph plot gives an overview of the hierarchical grouping of the individuals in the population. These attributes give a clear description of the population during evaluation and can be utilised for adaptive population and parameter control.

Evolutionary algorithms dynamics and its hidden complex network structures

In this participation, we are continuing to show mutual intersection of two completely different areas of research: complex networks and evolutionary computation. Large-scale networks, exhibiting complex patterns of interaction amongst vertices exist in both nature and man-made systems (i.e., communication networks, genetic pathways, ecological or economical networks, social networks, networks of various scientific collaboration etc.) and are a part of our daily life. We demonstrate that dynamics of evolutionary algorithms, that are based on Darwin theory of evolution and Mendel theory of genetic heritage, can be also visualized as complex networks. Such network can be then analyzed by means of classical tools of complex networks science. Results presented here are currently numerical demonstration rather than theoretical mathematical proofs. We open question whether evolutionary algorithms really create complex network structures and whether this knowledge can be successfully used like feedback for control of evolutionary dynamics and its improvement in order to increase the performance of evolutionary algorithms.

Complex Network Analysis of Evolutionary Algorithms Applied to Combinatorial Optimisation Problem

This research analyses the development of a complex network in an evolutionary algorithm (EA). The main aim is to evaluate if a complex network is generated in an EA, and how the population can be evaluated when the objective is to optimise an NP-hard combinatorial optimisation problem. The population is evaluated as a complex network over a number of generations, and different attributes such as adjacency graph, minimal cut, degree centrality, closeness centrality, betweenness centrality, k-Clique, k-Club, k-Clan and community graph plots are analysed. From the results, it can be concluded that an EA population does behave like a complex network, and therefore can be analysed as such, in order to obtain information about population development.

Analytic programming in the task of evolutionary synthesis of a controller for high order oscillations stabilization of discrete chaotic systems

This paper deals with the utilization of a symbolic regression tool, which is Analytic Programming (AP), together with two evolutionary algorithms, the Self-Organizing Migrating Algorithm (SOMA) and Differential Evolution (DE), for the synthesis of a new control law. This synthesized chaotic controller secures the stabilization of higher periodic orbits, which represent oscillations between several values of three selected discrete chaotic systems. Selected examples were: an artificially evolutionary synthesized system, logistic equation and Henon map. The paper consists of the description of analytic programming as well as chaotic systems used, evolutionary techniques and the cost function.

Chaos particle swarm optimization with Eensemble of chaotic systems

Abstract In this study the Chaotic Particle Swarm Optimization (CPSO) algorithm with six simultaneously used chaotic pseudo-random number generators (CPRNG) is investigated. The implementation of chaotic sequences is detailed and discussed. The Ensemble learning approach is used for assigning CPRNGs to particles. The results of proposed algorithm on IEEE CEC′13 Real-Parameter Single Objective Optimization benchmark set are presented and compared with the SPSO-2011.

Do evolutionary algorithms indeed require randomness

Inherent part of evolutionary algorithms that are based on Darwin theory of evolution and Mendel theory of genetic heritage, are random processes. In this participation we discuss whether random processes really are needed in evolutionary algorithms. We use n periodic deterministic processes instead of random number generators and compare performance of evolutionary algorithms powered by those processes and by pseudo-random number generators. Deterministic processes used in this participation are based on deterministic chaos and are used to generate periodical series with different length. Results presented here are numerical demonstration rather than mathematical proofs. We propose that certain class of deterministic processes can be used instead of random number generators without lowering the performance of evolutionary algorithms.

Do Evolutionary Algorithms Indeed Require Random Numbers? Extended Study

An inherent part of evolutionary algorithms, that are based on Darwin theory of evolution and Mendel theory of genetic heritage, are random processes. In this participation, we discuss whether are random processes really needed in evolutionary algorithms. We use \(\mathcal{n}\) periodic deterministic processes instead of random number generators and compare performance of evolutionary algorithms powered by those processes and by pseudo-random number generators. Deterministic processes used in this participation are based on deterministic chaos and are used to generate periodical series with different length. Results presented here are numerical demonstration rather than mathematical proofs. We propose that a certain class of deterministic processes can be used instead of random number generators without lowering of evolutionary algorithms performance.