A comparative study of social group optimization with a few recent optimization algorithms
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ORIGINAL ARTICLE
A comparative study of social group optimization with a few recent optimization algorithms Anima Naik1 · Suresh Chandra Satapathy2 Received: 22 January 2020 / Accepted: 17 August 2020 © The Author(s) 2020
Abstract From the past few decades, the popularity of meta-heuristic optimization algorithms is growing compared to deterministic search optimization algorithms in solving global optimization problems. This has led to the development of several optimization algorithms to solve complex optimization problems. But none of the algorithms can solve all optimization problems equally well. As a result, the researchers focus on either improving exiting meta-heuristic optimization algorithms or introducing new algorithms. The social group optimization (SGO) Algorithm is a meta-heuristic optimization algorithm that was proposed in the year 2016 for solving global optimization problems. In the literature, SGO is shown to perform well as compared to other optimization algorithms. This paper attempts to compare the performance of the SGO algorithm with other optimization algorithms proposed between 2017 and 2019. These algorithms are tested through several experiments, including multiple classical benchmark functions, CEC special session functions, and six classical engineering problems etc. Optimization results prove that the SGO algorithm is extremely competitive as compared to other algorithms. Keywords Meta-heuristic · Benchmark functions · Optimization algorithms · Fitness evaluations
Abbreviations Pop_size Max_FEs Fes RS test
Population size Maximum number of function evaluations Function evaluations Wilcoxon’s rank-sum test
Introduction The meta-heuristic optimization algorithm is a practical approach for solving global optimization problems. It is mainly based on simulating nature and artificial intelligence tools, invokes exploration and exploitation search procedures to diversify the search all over the search space and intensify
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Anima Naik [email protected] Suresh Chandra Satapathy [email protected]
1
Department of CSE, KL University, Hyderabad, Telangana, India
2
School of Computer Engineering, KIIT Deemed To Be University, Bhubaneswar, Odisha, India
the search in some promising areas. Flexibility and gradientfree approaches are the two main characteristics that make meta-heuristic strategies exceedingly popular for optimization researchers. From 1960s till date, several meta-heuristic optimization algorithms have been proposed. According to no-free-lunch (NFL) [1] theorem for optimization, none of the algorithms could solve all classes of optimization problems. This motivated many researchers to design new algorithms or modify/hybridize existing algorithms to solve different problems or provide competitive results, as compared to the current algorithms. Meta-heuristic algorithms can be classified into mainly four categories: (a) evolutionary-based algorithm, (b) swarm intelligence-based algorithm, (c) human-based algorithm, and (d) physics and chemistry-based algorith
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