Adam Viktorin

Network Based Linear Population Size Reduction in SHADE

This research paper presents a new approach to population size reduction in Success-History based Adaptive Differential Evolution (SHADE). The current L-SHADE algorithm uses fitness function value to select individuals which will be deleted from the current population. Algorithm variant proposed in this paper (Net L-SHADE) is using the information from evolutionary process to construct a network of individuals and the ones which would be deleted are selected based on their degree of centrality. The proposed technique is compared to state-of-art L-SHADE on CEC2015 benchmark set and the results are reported.

Success-history based adaptive differential evolution algorithm with multi-chaotic framework for parent selection performance on CEC2014 benchmark set

This paper presents a novel multi-chaotic framework, which is used for the parent selection process in Success-History based Adaptive Differential Evolution (SHADE) algorithm. Created variant of the Differential Evolution (DE) algorithm was named MC-SHADE and its performance is tested on the CEC2014 benchmark set in order to participate in the CEC2016 competition on bound constrained single objective numerical optimization - single parameter-operator set based case.

Study on the Time Development of Complex Network for Metaheuristic

This work deals with the hybridization of the complex networks framework and evolutionary algorithms. The population is visualized as an evolving complex network, which exhibits non-trivial features. This paper investigates briefly the time development of complex network within the run of selected metaheuristic algorithm, which is Differential Evolution (DE). This paper also briefly discuss possible utilization of the complex network attributes such as adjacency graph, centralities, clustering coefficient and others. Experiments were performed for one selected DE strategy and one simple test function.

Study On Swarm Dynamics Converted Into Complex Network.

In this study it is presented a summarization of our research of possible ways of creating of complex networks from the inner dynamics of Swarm Intelligence based algorithms. The particle swarm optimization algorithm and the firefly algorithm are studied in this paper. Several methods of complex network creation are proposed and discussed alongside with possibilities for future research and application.

Creating Complex Networks Using Multi-swarm PSO

In this paper we discuss the possibility of generating complex networks by multi-swarm Particle Swarm Optimization algorithm. A methodology is proposed and visualizations of created networks are presented. Also we discuss the future possibility of employing advanced complex network analysis to improve the performance of the multi-swarm PSO.

On the influence of different randomization and complex network analysis for differential evolution

This research deals with the hybridization of the chaos driven heuristics concept and complex networks framework for evolutionary algorithms. This paper aims on the experimental investigations on the influence of different randomization types for chaos-driven Differential Evolution (DE) through the analysis of complex network as a record of population dynamics. The population is visualized 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, clustering coefficient gives diversity of population whereas other attributes like network density, average number of neighbors within the population shows efficiency of the network. Experiments were performed for two different DE strategies, four different randomization types and two simple test functions.

Hypersphere Universe Boundary Method Comparison on HCLPSO and PSO

In this paper, the hypersphere universe method is applied on Heterogeneous Comprehensive Learning Particle Swarm Optimization (HCLPSO) and a classical representative of swarm intelligence Particle Swarm Optimization (PSO). The goal is to the compare this method to the classical version of these algorithms. The comparisons are made on CEC’17 benchmark set functions. The experiments were carried out according to CEC benchmark rules and statistically evaluated using Friedman rank test.

A Review of Real-World Applications of Particle Swarm Optimization Algorithm

In this work, we present an overview of the various real-world application of Particle Swarm Optimization Algorithm. We argue that the PSO is showing superior performance on different optimization problems such as temperature prediction, battery storage optimization or leukemia diagnosis. The diversity of real-world applications covers the fields of electronic, informatics, energetics, medicine and many other areas of industry and research. This study should encourage new researchers for applying this method and take advantage of its unique inner dynamic and performance.

Hybridization of Multi-chaotic Dynamics and Adaptive Control Parameter Adjusting jDE Strategy

This research deals with the hybridization of several approaches for evolutionary algorithms, which are the adaptive control parameter adjusting strategy and multi-chaotic dynamics driving the selection of indices in Differential Evolution (DE). The novelty of the paper is given by the experiments with the multi-chaos-driven adaptive DE concept inside adaptive parameter adjusting DE strategies. These experiments are representing the investigations on the mutual influences of several different randomizations types together with adaptive DE strategies. The multi-chaotic concept is representing the adaptive switching between two different chaotic systems based on the progress of individuals within population. This paper is aimed at the embedding of discrete dissipative chaotic systems in the form of multi-chaotic pseudo random number generators for the jDE, which is the state of the art representative of simple adaptive control parameter adjusting strategy for DE. Repeated simulations for two different combinations of driving chaotic systems were performed on the IEEE CEC 13 benchmark set. Finally, the obtained results are compared with the canonical not-chaotic jDE.

On the Transforming of the Indices Selection Mechanism inside Differential Evolution into Complex Network

This research deals with complex networks framework for evolutionary algorithms. This paper aims on the experimental investigations on the time development and influence of different randomization types, different strategies for Differential Evolution (DE) through the analysis of complex network as a record of population dynamics and indices selection. The population is visualized as an evolving complex network, which exhibits non-trivial features such as adjacency graph, centralities, clustering coefficient and other attributes showing efficiency of the network. Experiments were performed for different DE strategies, several different randomization types and simple test function.