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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Bibliographic Details
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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Summary: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.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-021-04015-9