A hybrid OBL-based firefly algorithm with symbiotic organisms search algorithm for solving continuous optimization problems

The metaheuristic optimization algorithms are relatively new optimization algorithms introduced to solve optimization problems in recent years. For example, the firefly algorithm (FA) is one of the metaheuristic algorithms inspired by the fireflies' flashing behavior. However, its weakness in t...

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Published in:	The Journal of supercomputing Vol. 78; no. 3; pp. 3998 - 4031
Main Authors:	Goldanloo, Mina Javanmard, Gharehchopogh, Farhad Soleimanian
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.02.2022 Springer Nature B.V
Subjects:	Compilers Computer Science Convergence Exploration Heuristic methods Interpreters Iterative methods Optimization Optimization algorithms Processor Architectures Programming Languages Search algorithms Continuous optimization problem Symbiotic organisms search Firefly algorithm Optimization
ISSN:	0920-8542, 1573-0484
Online Access:	Get full text
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Abstract	The metaheuristic optimization algorithms are relatively new optimization algorithms introduced to solve optimization problems in recent years. For example, the firefly algorithm (FA) is one of the metaheuristic algorithms inspired by the fireflies' flashing behavior. However, its weakness in terms of exploration and early convergence has been pointed out. In this paper, two approaches were proposed to improve the FA. In the first proposed approach, a new improved opposition-based learning FA (IOFA) method was presented to accelerate the convergence and improve the FA's exploration capability. In the second proposed approach, a symbiotic organisms search (SOS) algorithm improved the exploration and exploitation of the first approach; two new parameters set these two goals, and the second approach was named IOFASOS. The purpose of the second method is that in the process of the SOS algorithm, the whole population is effective in the IOFA method to find solutions in the early stages of implementation, and with each iteration, fewer solutions are affected in the population. The experiments on 24 standard benchmark functions were conducted, and the first proposed approach showed a better performance in the small and medium dimensions and exhibited a relatively moderate performance in the higher dimensions. In contrast, the second proposed approach was better in increasing dimensions. In general, the empirical results showed that the two new approaches outperform other algorithms in most mathematical benchmarking functions. Thus, The IOFASOS model has more efficient solutions.
AbstractList	The metaheuristic optimization algorithms are relatively new optimization algorithms introduced to solve optimization problems in recent years. For example, the firefly algorithm (FA) is one of the metaheuristic algorithms inspired by the fireflies' flashing behavior. However, its weakness in terms of exploration and early convergence has been pointed out. In this paper, two approaches were proposed to improve the FA. In the first proposed approach, a new improved opposition-based learning FA (IOFA) method was presented to accelerate the convergence and improve the FA's exploration capability. In the second proposed approach, a symbiotic organisms search (SOS) algorithm improved the exploration and exploitation of the first approach; two new parameters set these two goals, and the second approach was named IOFASOS. The purpose of the second method is that in the process of the SOS algorithm, the whole population is effective in the IOFA method to find solutions in the early stages of implementation, and with each iteration, fewer solutions are affected in the population. The experiments on 24 standard benchmark functions were conducted, and the first proposed approach showed a better performance in the small and medium dimensions and exhibited a relatively moderate performance in the higher dimensions. In contrast, the second proposed approach was better in increasing dimensions. In general, the empirical results showed that the two new approaches outperform other algorithms in most mathematical benchmarking functions. Thus, The IOFASOS model has more efficient solutions.
Author	Gharehchopogh, Farhad Soleimanian Goldanloo, Mina Javanmard
Author_xml	– sequence: 1 givenname: Mina Javanmard surname: Goldanloo fullname: Goldanloo, Mina Javanmard organization: Department of Computer Engineering, Urmia Branch, Islamic Azad University – sequence: 2 givenname: Farhad Soleimanian orcidid: 0000-0003-1588-1659 surname: Gharehchopogh fullname: Gharehchopogh, Farhad Soleimanian email: bonab.farhad@gmail.com organization: Department of Computer Engineering, Urmia Branch, Islamic Azad University
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SubjectTerms	Compilers Computer Science Convergence Exploration Heuristic methods Interpreters Iterative methods Optimization Optimization algorithms Processor Architectures Programming Languages Search algorithms
Title	A hybrid OBL-based firefly algorithm with symbiotic organisms search algorithm for solving continuous optimization problems
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