Performance of the Bison Algorithm on Benchmark IEEE CEC 2017

Kazíková, Anežka; Pluháček, Michal; Šenkeřík, Roman

doi:10.1007/978-3-319-91189-2_44

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…The range of used evaluation limit reaches from 250 FEs [27], [28] over 10, 000 × D [29], to no limitation at all [30]. However, the benchmark test suites may serve as a nonnegligible inspiration source, and many publications adopt the recommended evaluation budgets from the benchmark definitions (see, e.g., [10]- [14], [31]). Table 1 provides an overview of the IEEE CEC optimization benchmark testbeds and the corresponding limits of objective function evaluations.…”

Section: Is There Any Standard Evaluations Practice?mentioning

confidence: 99%

How Does the Number of Objective Function Evaluations Impact Our Understanding of Metaheuristics Behavior?

2021

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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 paper 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.

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Section: Is There Any Standard Evaluations Practice?mentioning

confidence: 99%

How Does the Number of Objective Function Evaluations Impact Our Understanding of Metaheuristics Behavior?

2021

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“…The exploitation movement is based on the center of several fittest solutions, while other algorithms usually use only one best solution to move to. The algorithm was compared to other metaheuristics on IEEE CEC 2017 benchmark functions in [14][15][16]. and then moves all the solutions from the swarming group closer to the center if it improves their quality (Eq.…”

Section: Bison Algorithmentioning

confidence: 99%

“…Since the very first proposal of the Bison Algorithm, the mechanics of the running group evolved rapidly. In [13,14] the groups were divided solely by the quality of the found solution. Therefore the weaker solutions explored the search space, while the stronger ones managed the exploitation.…”

mentioning

confidence: 99%

Introducing the Run Support Strategy for the Bison Algorithm

Kazíková

Pluháček

Kadavý

et al. 2019

Lecture Notes in Electrical Engineering

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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 ensure, that the newly discovered solutions can affect the overall optimization process. In this paper, we propose a new Run Support Strategy for the Bison Algorithm, that should enhance the utilization of newly discovered solutions, and should be suitable for both continuous and discrete optimization.

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“…However, up to now research suggested, that the running group has rather a limited impact on the overall optimization process [7,8,9]. This discovery provoked a question of the actual influence on the population and whether the runners have, in fact, the potential to affect and improve the final solution.…”

Section: Introductionmentioning

confidence: 99%

Regarding the Behavior of Bison Runners Within the Bison Algorithm

Kazíková¹,

Pluháček²,

Šenkeřík³

2018

mendel

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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.

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Performance of the Bison Algorithm on Benchmark IEEE CEC 2017

Cited by 4 publications

References 8 publications

How Does the Number of Objective Function Evaluations Impact Our Understanding of Metaheuristics Behavior?

How Does the Number of Objective Function Evaluations Impact Our Understanding of Metaheuristics Behavior?

Introducing the Run Support Strategy for the Bison Algorithm

Regarding the Behavior of Bison Runners Within the Bison Algorithm

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