Orthogonal Learning Firefly Algorithm

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, specifically with its hybrid modification Firefly Particle Swarm Optimization (FFPSO). The parameters of the proposed Orthogonal Learning Firefly Algorithm are also initially thoroughly explored and tuned. The performance of the developed algorithm is examined and compared with canonical FA and above-mentioned FFPSO. Comparisons have been conducted on well-known CEC 2017 benchmark functions, and the results have been evaluated for statistical significance using the Friedman rank test. © 2020 The Author(s).Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under the Project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2020/001]; COST (European Cooperation in Science & Technology) under Action, Improving Applicability of NatureInspired Optimisation by Joining Theory and Practice (ImAppNIO) [CA15140]IGA/CebiaTech/2020/001; European Cooperation in Science and Technology, COST: CA15140; Ministerstvo Školství, Mládeže a Tělovýchovy, MŠMT: LO1303, MSMT-7778/2014; European Regional Development Fund, ERDF: CZ.1.05/2.1.00/03.008

Orthogonal learning firefly algorithm

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 conducted on well-known CEC 2017 benchmark functions, and the results have been evaluated for statistical significance using Friedman rank test. © Springer International Publishing AG, part of Springer Nature 2018.IC1406, COST, European Cooperation in Science and Technology; CA15140, COST, European Cooperation in Science and Technology; IGA/CebiaTech/2018/003; MSMT-7778/2014, MŠMT, Ministerstvo Školství, Mládeže a Tělovýchovy; LO1303, MŠMT, Ministerstvo Školství, Mládeže a Tělovýchovy; COST, European Cooperation in Science and Technology; CZ.1.05/2.1.00/03.0089, FEDER, European Regional Development FundMinistry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under the Project CEBIA- Tech [CZ.1.05/2.1.00/03.0089]; Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2018/003]; COST (European Cooperation in Science & Technology) under Action [CA15140

Firefly algorithm enhanced by orthogonal learning

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. Comparisons have been conducted on five selected basic benchmark functions, and the results have been evaluated for statistical significance using Wilcoxon rank-sum test. © 2019, Springer International Publishing AG, part of Springer Nature.IC1406, COST, European Cooperation in Science and Technology; CA15140, COST, European Cooperation in Science and Technology; IGA/CebiaTech/2018/003; MSMT-7778/2014, MŠMT, Ministerstvo Školství, Mládeže a Tělovýchovy; LO1303, MŠMT, Ministerstvo Školství, Mládeže a Tělovýchovy; CZ.1.05/2.1.00/03.0089, FEDER, European Regional Development Fund; COST, European Cooperation in Science and TechnologyMinistry of Education, Youth and Sports of the Czech Republic [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under the Project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2018/003]; COST (European Cooperation in Science Technology) [CA15140, IC1406]; A. I. Lab at the Faculty of Applied Informatics, Tomas Bata University in Zli