Michal Pluhacek

Michal Pluhacek
Tomas Bata University in Zlín · Regional Research Centre - CEBIA-Tech

Ph.D.

About

227
Publications
7,460
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1,223
Citations

Publications

Publications (227)
Article
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 possibl...
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...
Article
Full-text available
Comparing various metaheuristics based on an equal number of objective function evaluations has become standard practice. Many contemporary publications use a specific number of objective function evaluations by the benchmarking sets definitions. Furthermore, many publications deal with the recurrent theme of late stagnation, which may lead to the...
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...
Article
Full-text available
In this paper, it is presented a utilization of a novel tool for symbolic regression, which is analytic programming, for the purpose of the synthesis of a new feedback control law. This new synthesized chaotic controller secures the fully stabilization of selected discrete chaotic systems, which is the two-dimensional Lozi map. The paper consists o...
Article
Although metaheuristic optimization has become a common practice, new bio-inspired algorithms often suffer from a priori ill reputation. One of the reasons is a common bad practice in metaheuristic proposals. It is essential to pay attention to the quality of conducted experiments, especially when comparing several algorithms among themselves. The...
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...
Chapter
Meta-heuristic algorithms are reliable tools for modern optimization. Yet their amount is so immense that it is hard to pick just one to solve a specific problem. Therefore many researchers hold on known, approved algorithms. But is it always beneficial? In this paper, we use the meta-heuristics for the design of cascade PID controllers and compare...
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
Waste-to-Energy facility location with practical insights into its economic sustainability is assessed by two mathematical models. The first model minimising transportation and investment costs leads to a mixed-integer linear problem, for which commercial solvers perform very well. However, economic performance, which is needed for long-term projec...
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
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 paper studies the performance of a newly developed optimization algorithm inspired by the behavior of bison herds: the Bison Algorithm. The algorithm divides its population into two groups. The exploiting group simulates the swarming behavior of bison herds endangered by predators. The exploring group systematically runs through the search spa...
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 paper proposes a new swarm optimization algorithm inspired by bison behavior. The algorithm mimics two survival mechanisms of the bison herds: swarming into the circle of the strongest individuals and exploring the search space via organized run throughout the optimization process. The proposed algorithm is compared to the Particle Swarm Optim...
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...
Article
This paper proposes a modification of the Bison Algorithm’s running technique, which allows the running group to exploit the areas of discovered promising solutions. It also provides a closer examination of the successful running behavior and its impact on the overall optimization process. The new algorithm is then compared to other optimization al...
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 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
This chapter deals with the hybridization of the chaos driven heuristics concept and complex networks framework for meta-heuristic. This research 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...
Chapter
This chapter is a graphical overview - a gallery of selected networks that have been obtained during our experiments. The gallery contains samples coming from different algorithms with attention on its beauty (as we hope) and shall serve as the visual motivation-inspiration for new experiments. Visualization of those networks has been done on diffe...
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...
Conference Paper
In this research, evolutionary based algorithm Differential Evolution (DE) is compared with swarm based optimization technique PSO in the task of optimal evolutionary tuning of controller parameters for the stabilization of nonlinear chaotic oscillations of the two-dimensional Lozi map. The novelty of the approach is that the most utilized examples...