Tomas Kadavy

Tomas Kadavy
Tomas Bata University in Zlín · Department of Informatics and Artificial Intelligence

Master of Computer and Communication Systems

About

93
Publications
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267
Citations

Publications

Publications (93)
Article
Benchmarking various metaheuristics and their new enhancements, strategies, and adaptation mechanisms has become standard in computational intelligence research. Recently, many challenges and issues regarding fair comparisons and recommendations towards good practices for benchmarking of metaheuristic algorithms, have been identified. This paper is...
Article
Measuring the population diversity in metaheuristics has become a common practice for adaptive approaches, aiming mainly to address the issue of premature convergence. Understanding the processes leading to a diversity loss in a metaheuristic algorithm is crucial for designing successful adaptive approaches. In this study, we focus on the relation...
Chapter
The focus of this work is the deeper insight into arising serious research questions connected with the growing popularity of combining metaheuristic algorithms and chaotic sequences showing quasi-periodic patterns. This paper reports analysis on the performance of popular and CEC 2019 competition winning strategy of Differential Evolution (DE), wh...
Article
The primary aim of this original work is to provide a more in-depth insight into the relations between control parameters adjustments, learning techniques, inner swarm dynamics and possible hybridization strategies for popular swarm metaheuristic Firefly Algorithm (FA). In this paper, a proven method, orthogonal learning, is fused with FA, specific...
Article
This extended study presents a hybridization of particle swarm optimization (PSO) with complex network construction and analysis. A partial population restart is performed in certain moments of the run of the algorithm based on the information obtained from a complex network analysis. The complex network structure represents the communication in th...
Chapter
This paper is focused on the influence of boundary strategies for the popular swarm-intelligence based optimization algorithm: Self-organizing Migrating Algorithm (SOMA). A similar extensive study was already performed for the most famous representative of swarm-based algorithm, which is Particle Swarm Optimization (PSO), and showed the importance...
Chapter
The focus of this work is the deeper insight into arising serious research questions connected with the growing popularity of combining metaheuristic algorithms and chaotic sequences showing quasi-periodic patterns. This paper reports an analysis of population dynamics by linking three elements like distribution of the results, population diversity...
Chapter
The self-organizing migrating algorithm (SOMA) is a popular population base metaheuristic. One of its key mechanisms is a perturbation of the individual movement with a binary-valued perturbation (PRT) vector. The goal of perturbation is to improve the diversity of the population and exploration of the search space. In this paper, we study a varian...
Chapter
Many state-of-the-art optimization algorithms stand against the threat of premature convergence. While some metaheuristics try to avoid it by increasing the diversity in various ways, the Bison Algorithm faces this problem by guaranteeing stable exploitation – exploration ratio throughout the whole optimization process. Still, it is important to en...
Chapter
In this paper, a novel lightweight version of the Successful-History based Adaptive Differential Evolution (SHADE) is presented as the first step towards a simple, user-friendly, metaheuristic algorithm for global optimization. This simplified algorithm is called liteSHADE and is compared to the original SHADE on the CEC2015 benchmark set in three...
Chapter
This research represents a detailed insight into the modern and popular hybridization of unconventional quasiperiodic/chaotic sequences and evolutionary computation. It is aimed at the influence of different randomization schemes on the population diversity, thus on the performance, of two selected adaptive Differential Evolution (DE) variants. Exp...
Chapter
Currently, electromagnetic compatibility presents a severe problem for electric and electronic devices; therefore, the demand for protection has rapidly increased in recent years. Unfortunately, the design of a high-quality shield can involve different pitfalls, and it is impossible to explore and test every possible solution. Many times, the model...
Chapter
This paper presents the analysis of the difference in control parameter adaptation between jSO and DISH algorithms. The DISH algorithm uses a distance based parameter adaptation and therefore, is based on the distance between successful offspring and its parent solution rather than on the difference in their corresponding objective function values....
Chapter
Full-text available
The faithful reproduction and accurate prediction of the phenotypes and emergent behaviors of complex cellular systems are among the most challenging goals in Systems Biology. Although mathematical models that describe the interactions among all biochemical processes in a cell are theoretically feasible, their simulation is generally hard because o...
Chapter
This paper provides a closer insight into applicability and performance of the hybridization of symbolic regression open framework, which is Analytical Programming (AP) and Differential Evolution (DE) algorithm in the task of time series regression. AP can be considered as a robust open framework for symbolic regression thanks to its usability in a...
Chapter
Orthogonal learning strategy, a proven technique, is combined with hybrid optimization metaheuristic, which is based on Firefly Algorithm and Particle Swarm Optimization. The hybrid algorithm Firefly Particle Swarm Optimization is then compared, together with canonical Firefly Algorithm, with the newly created Orthogonal Learning Firefly Algorithm....
Chapter
This work studied a relationship between optimization qualities of Success-History based Adaptive Differential Evolution algorithm (SHADE) and its self-adaptive parameter strategy. Original SHADE with improvement based adaptation is compared to the SHADE with Distance based parameter adaptation (Db_SHADE) on the basis of the CEC2015 benchmark set f...
Chapter
This research deals with the hybridization of two computational intelligence fields, which are the chaos theory and evolutionary algorithms. Experiments are focused on the extensive investigation on the different randomization schemes for selection of individuals in differential evolution algorithm (DE).
Chapter
This research paper analyses an external archive of inferior solutions used in Success-History based Adaptive Differential Evolution (SHADE) and its variant with a linear decrease in population size L-SHADE. A novel implementation of an archive is proposed and compared to the original one on CEC2015 benchmark set of test functions for two distincti...
Article
In this study, we propose a repulsive mechanism for the Particle Swarm Optimization algorithm that improves its performance on multi-modal problems. The repulsive mechanism is further extended with a distance-based modification. The results are presented and tested for statistical significance. We discuss the observations and propose further direct...
Article
This research represents a detailed insight into the modern and popular hybridization of deterministic chaotic dynamics and evolutionary computation. It is aimed at the influence of chaotic sequences on the performance of four selected Differential Evolution (DE) variants. The variants of interest were: original DE/Rand/1/ and DE/Best/1/ mutation s...
Conference Paper
This research deals with the constrained industrial optimization task, which is the optimization of technological parameters for the waste processing batch reactor. This paper provides a closer insight into the performance of connection between constrained optimization and different strategies of Differential Evolution (DE). Thus, the motivation be...
Article
This paper proposes a simple, yet effective, modification to scaling factor and crossover rate adaptation in Success-History based Adaptive Differential Evolution (SHADE), which can be used as a framework to all SHADE-based algorithms. The performance impact of the proposed method is shown on the real-parameter single objective optimization (CEC201...
Conference Paper
This research paper presents an analysis of the population activity in Differential Evolution algorithm (DE) during the optimization process. A state-of-art DE variant – Success-History based Adaptive DE (SHADE) is used and the population activity is analyzed through Complex Network (CN) created from mutation, crossover and selection steps. The ana...
Article
In this paper, we use pseudo-random number generators based on six chaotic systems to enhance the performance of multiple-choice strategy for particle swarm optimisation (PSO). The multiple-choice strategy is a heterogeneous swarm-based method. We present the results of benchmark testing using the latest CEC’17 benchmark suite. The performance of t...
Chapter
In this paper, a proven technique, orthogonal learning, is combined with popular swarm metaheuristic Firefly Algorithm (FA). More precisely with its hybrid modification Firefly Particle Swarm Optimization (FFPSO). The performance of the developed algorithm is tested and compared with canonical FA and above mentioned FFPSO. Comparisons have been con...
Article
This paper provides an analysis of the population clustering in a novel Success-History based Adaptive Differential Evolution algorithm with Distance based adaptation (Db_SHADE) in order to analyze the exploration and exploitation abilities of the algorithm. The comparison with the original SHADE algorithm is performed on the CEC2015 benchmark set...
Chapter
This paper compares four different methods for handling the roaming behavior of fireflies in the firefly algorithm. The problems of boundary constrained optimization forces the algorithm to actively keep the fireflies inside the feasible area of possible solutions. The recent CEC’17 benchmark suite is used for the performance comparison of the meth...
Chapter
This research deals with the modern and popular hybridization of chaotic dynamics and evolutionary computation. It is aimed at the influence of chaotic sequences on the population diversity as well as the algorithm performance of the simple parameter adaptive Differential Evolution (DE) strategy: jDE. Experiments are focused on the extensive invest...
Chapter
This paper discusses the effect of distance based parameter adaptation on the population diversity of the Success-History based Adaptive Differential Evolution (SHADE). The distance-based parameter adaptation was designed to promote exploration over exploitation and provide better search capabilities of the SHADE algorithm in higher dimensional obj...
Conference Paper
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 o...
Conference Paper
In this paper, we are presenting an interesting method for controlling population diversity of the Firefly Algorithm (FA). Presented method is using the advantages of complex networks and their several characteristics, that can be helpful for the detailed analysis of metaheuristic algorithm inner dynamic. Through this work, we are trying to present...
Chapter
This chapter presents an proposal of methodology for converting the inner dynamics of PSO algorithm into complex network. The motivation is in the recent trend of adaptive and learning methods for improving the performance of evolutionary computational techniques. It seems very likely that the complex network and its statistical characteristics can...
Chapter
In this paper a modification to the adaptive mechanism in Success-History based Adaptive Differential Evolution (SHADE) with Linear decrease in population size (L-SHADE) is proposed in order to overcome the premature convergence, while optimizing higher dimensional problems. This modification can be also useful in constrained optimization, where th...
Article
In this paper, two modifications are proposed to the Progressive Random Walk (PRW) algorithm in order to address its potentially insufficient search space coverage. The first modification replaces the Pseudo-Random Number Generator (PRNG) with the uniform distribution by the chaotic map based PRNG for generating of the offset values and the second...
Conference Paper
In this study we compare the performance of two popular variants of PSO algorithm the diversity guided PSO and heterogeneous PSO. The IEEE CEC 2015 benchmark set is used to test and compare the performance of the methods. The results are statistically evaluated and discussed.
Conference Paper
In this preliminary study, the dynamic of continuous optimization algorithm Success-History based Adaptive Differential Evolution (SHADE) is translated into a Complex Network (CN) and the basic network feature, node degree centrality, is analyzed in order to provide helpful insight into the inner workings of this state-of-the-art Differential Evolu...
Conference Paper
This research deals with the hybridization of symbolic regression open framework, which is Analytical Programming (AP) and Differential Evolution (DE) algorithm in the task of time series prediction. This paper provides a closer insight into applicability and performance of the hybrid connection between AP and different strategies of DE. AP can be...
Conference Paper
This study presents a hybridization of Particle Swarm Optimization with a complex network creation and analysis. A partial population is performed in certain moments of the run of the algorithm based on the information obtained from a complex network structure that represents the communication in the population. We present initial results alongside...
Conference Paper
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 benchma...
Conference Paper
This preliminary study presents a hybridization of two research fields – evolutionary algorithms and complex networks. A network is created by the dynamic of an evolutionary algorithm, namely Success-History based Adaptive Differential Evolution (SHADE). Network feature, node degree centrality, is used afterward to detect potential design weaknesse...
Article
In this study, we construct a complex network from the inner dynamic of Particle Swarm Optimization algorithm. The subsequent analysis of the network promises to provide useful information for better understanding the dynamic of the swarm that is not acquirable by other means. We present several network visualizations and numerical analysis. We dis...
Article
In this paper, a visualization of Firework Algorithm (FWA) inner dynamics as an evolving complex network is presented. Recent research in unconventional controlling and simulation of metaheuristic dynamics shows that this kind of visualization technique has been utilized only for algorithms with some social communication or behavior leading to shar...