Impact of Boundary Control Methods on Bound-constrained Optimization Benchmarking

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F22%3A63549578" target="_blank" >RIV/70883521:28140/22:63549578 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/abstract/document/9878135" target="_blank" >https://ieeexplore.ieee.org/abstract/document/9878135</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TEVC.2022.3204412" target="_blank" >10.1109/TEVC.2022.3204412</a>

Result language
angličtina
Original language name
Impact of Boundary Control Methods on Bound-constrained Optimization Benchmarking
Original language description
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 aimed at an important issues in metaheuristics design and benchmarking, which are boundary strategies or boundary control methods (BCM). This work aims to investigate whether the choice of a BCM could significantly influence the performance of competitive algorithms. The experiments encompass the top three performing algorithms from IEEE CEC competitions 2017 and 2020 with six different boundary control methods. We provide extensive statistical analysis and rankings resulting in conclusions and recommendations for metaheuristics researchers and possibly also for the future direction of benchmark definitions. We conclude that the BCM should be considered another vital metaheuristics input variable for unambiguous reproducibility of results in benchmarking and for a better understanding of population dynamics, since the BCM setting could impact the optimization method performance.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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