Regarding the behavior of bison runners within the Bison algorithm

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 algorithms on the IEEE CEC 2017 benchmark solving continuous minimization problems. © 2018, Brno University of Technology. All rights reserved

Multiswarm PSO with supersized swarms - Initial performance study

In this paper it is discussed and briefly experimentally investigated the performance of multi-swarm PSO with super-sized swarms. The selection of proper population size is crucial for successful PSO using. This work follows previous promising research. © 2016 Author(s)

Spiral extrusion die design using modified differential evolution algorithm

In this work, a spiral extrusion die for industrial production of plastic foil has been designed using a modified differential evolution algorithm. The proposed method managed to provide a die design that was compliant with all demands of the foil manufacturer and lowered the production cost. Third-Party software is used to compute the die characteristics from the geometry designed by modified differential evolution. © 2019, Brno University of Technology. All rights reserved

Converting PSO dynamics into complex network - Initial study

In this paper it is presented the initial study on the possibility of capturing the inner dynamic of Particle Swarm Optimization algorithm into a complex network structure. Inspired in previous works there are two different approaches for creating the complex network presented in this paper. Visualizations of the networks are presented and commented. The possibilities for future applications of the proposed design are given in detail. © 2016 Author(s)

On the adaptivity and complexity embedded into differential evolution

This research deals with the comparison of the two modern approaches for evolutionary algorithms, which are the adaptivity and complex chaotic dynamics. This paper aims on the investigations on the chaos-driven Differential Evolution (DE) concept. This paper is aimed at the embedding of discrete dissipative chaotic systems in the form of chaotic pseudo random number generators for the DE and comparing the influence to the performance with the state of the art adaptive representative jDE. This research is focused mainly on the possible disadvantages and advantages of both compared approaches. Repeated simulations for Lozi map driving chaotic systems were performed on the simple benchmark functions set, which are more close to the real optimization problems. Obtained results are compared with the canonical not-chaotic and not adaptive DE. Results show that with used simple test functions, the performance of ChaosDE is better in the most cases than jDE and Canonical DE, furthermore due to the unique sequencing in CPRNG given by the hidden chaotic dynamics, thus better and faster selection of unique individuals from population, ChaosDE is faster. © 2016 Author(s)

Why tuning the control parameters of metaheuristic algorithms is so important for fair comparison?

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 comparisons should be fair and unbiased. This paper points to the importance of proper initial parameter configurations of the compared algorithms. We highlight the performance differences with several popular and recommended parameter configurations. Even though the parameter selection was mostly based on comprehensive tuning experiments, the algorithms’ performance was surprisingly inconsistent, given various parameter settings. Based on the presented evidence, we conclude that paying attention to the metaheuristic algorithm’s parameter tuning should be an integral part of the development and testing processes. © 2020, Brno University of Technology. All rights reserved

Chaos enhanced differential evolution in the task of evolutionary control of discrete chaotic Lozi map

In this paper, evolutionary technique Differential Evolution ( DE) is used for the evolutionary tuning of controller parameters for the stabilization of selected discrete chaotic system, which is the two-dimensional Lozi map. The novelty of the approach is that the selected controlled discrete dissipative chaotic system is used within Chaos enhanced heuristic concept as the chaotic pseudo-random number generator to drive the mutation and crossover process in the DE. The idea was to utilize the hidden chaotic dynamics in pseudo-random sequences given by chaotic map to help Differential evolution algorithm in searching for the best controller settings for the same chaotic system. The optimizations were performed for three different required final behavior of the chaotic system, and two types of developed cost function. To confirm the robustness of presented approach, comparisons with canonical DE strategy and PSO algorithm have been performed.Grant Agency of the Czech Republic [GACR P103/15/06700S]; Ministry of Education, Youth and Sports of the Czech Republic within National Sustainability Pro- gramme [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; Grant of SGS of VSB-Technical University of Ostrava, Czech Republic [SP2016/175]; Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2016/007

Numerical analysis of direct punch with a view to velocity and level of training

In this small scale study the possibility of numerical analysis of direct punch velocity is investigated. The aim was to distinguish the level of training of the participants based on the numerical analysis. The measuring devices are described and experiment designed. Different methods of data analysis are proposed and examined. The results are summarized and compared. Promising trends are highlighted. It is concluded that some aspect of the numerical analysis seem promising for future use for level of training classification

How does the number of objective function evaluations impact our understanding of metaheuristics behavior?

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 impression that continuing the optimization process could be a waste of computational capabilities. But is it? Recently, many challenges, issues, and questions have been raised regarding fair comparisons and recommendations towards good practices for benchmarking metaheuristic algorithms. The aim of this work is not to compare the performance of several well-known algorithms but to investigate the issues that can appear in benchmarking and comparisons of metaheuristics performance (no matter what the problem is). This article studies the impact of a higher evaluation number on a selection of metaheuristic algorithms. We examine the effect of a raised evaluation budget on overall performance, mean convergence, and population diversity of selected swarm algorithms and IEEE CEC competition winners. Even though the final impact varies based on current algorithm selection, it may significantly affect the final verdict of metaheuristics comparison. This work has picked an important benchmarking issue and made extensive analysis, resulting in conclusions and possible recommendations for users working with real engineering optimization problems or researching the metaheuristics algorithms. Especially nowadays, when metaheuristic algorithms are used for increasingly complex optimization problems, and meet machine learning in AutoML frameworks, we conclude that the objective function evaluation budget should be considered another vital optimization input variable.Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2021/001]; AI Laboratory, Faculty of Applied Informatics, Tomas Bata University in ZlinIGA/CebiaTech/2021/001; Univerzita Tomáše Bati ve Zlín

PSO algorithm enhanced with Lozi Chaotic Map - Tuning experiment

In this paper it is investigated the effect of tuning of control parameters of the Lozi Chaotic Map employed as a chaotic pseudo-random number generator for the particle swarm optimization algorithm. Three different benchmark functions are selected from the IEEE CEC 2013 competition benchmark set. The Lozi map is extensively tuned and the performance of PSO is evaluated