A comparative study of social group optimization with a few recent optimization algorithms

Complex & Intelligent Systems https://doi.org/10.1007/s40747-020-00189-6 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 B Anima Naik Suresh Chandra Satapathy 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 algorithm. Evolutionary algorithms mimic concepts of evolution in nature. The genetic algorithm (GA) [2] is the best example of an evolutionary algorithm that simulates the concepts of Darwinian theory of evolution. After that several other evolutionary algorithms have been proposed such as evolutionary strategy (ES) [3], and evolutionary programming (EP) [4], gene expression programming (GEP) [5, 6], genetic programming (GP) [7], covariance matrix adaptation evolution strategy 123 Complex & Intelligent Systems CMA-ES) [8], differential evolution (DE) [9], biogeographybased optimization (BBO) algorithm [10]. Swarm intelligence algorithms mimic the intelligence of swarms. Each swarm consists of a group of creatures. So, these algorithms originate from the collective behaviour of a group of creatures in the swarm. Many swarm intelligence algorithms are seen in the literature. These are particle swarm optimization (PSO) [11] inspired by bird flocking, ant colony optimization (ACO) [12] inspired by Ants behaviour while collecting food, artificial bee colony (ABC) [13] mimicked by Honey bee for collecting nectar, etc. Additionally, there are many more algorithms such as bacterial foraging(BF) [14], bat algorithm (BA) [15], firefly algorithm (FFA) [16], krill herb (KB) [17], cuckoo search (CS) [18], monkey search (MS) [19], bee colony optimization (BCO) [20], cat swarm [21], wolf search (WS) [22], ant lion optimizer (ALO) [23], grey wolf optimization (GWO) [24], whale-optimization algorithm (WOA) [25], crow search algorithm (CSA) [26], Salp swarm algorithm (SSA) [27], grasshopper optimization algorithm (GOA) [28], butterfly optimization algorithm (BOA) [29], squirrel search algorithm (SSA) [30], Harris Hawks optimization (HHO) [31]. Human based algorithms are mainly inspired by behaviors of human. Some of the popular algorithms are teaching–learning-based optimization (TLBO) [32], harmony search (HS) [33], Tabu (Taboo) search (TS) [34–36], group search optimizer (GSO) [37, 38], imperialist competitive algorithm (ICA) [39], league championship algorithm (LCA) [40], firework algorithm [41], colliding bodies optimization (CBO) [42], interior search algorithm (ISA) [43], mine blast algorithm (MBA) [44], soccer league competition (SLC) algorithm [45], seeker optimization algorithm (SOA) [46], social-based algorithm (SBA) [47], exchange market algorithm (EMA) [48], and group counselling optimization (GCO) algorithm [49, 50], social emotional optimization (SEO) [51], ideology algorithm (IA) [52], social learning optimization (SLO) [53], social group optimization (SGO) [54, 55], election algorithm (EA) [56], cultural evolution algorithm (CEA) [57], cohort intelligence (CI) [58], anarchic society optimization (ASO) [59], volleyball premier league algorithm (VPL) [60], socio evolution and learning optimization algorithm (SELO) [61]. Physical and chemical based algorithms are inspired by physical rules and chemical reactions of the universe. Some popular algorithms are simulated annealing (SA) [62], gravitational local search (GLSA) [63], big-bang big-crunch (BBBC) [64], gravitational search algorithm (GSA) [65], charged system search (CSS) [66], central force optimization (CFO) [67], artificial chemical reaction optimization algorithm (ACROA) [68], black hole (BH) algorithm [69], ray optimization (RO) [70] algorithm, small-world optimization algorithm (SWOA) [71], galaxy-based-search algorithm (GbSA) [72], curved space optimization (CSO) [73], water 123 cycle algorithm(WCA) [74]. Spiral optimization (SO) [75], river formation dynamics (RFD) [76], sine cosine algorithm (SCA) [77], multi verse optimizer (MVO) [78], lightning attachment procedure optimization (LAPO) [79], golden ratio optimization method (GROM) [80]. Meta-heuristic algorithms are extensively recognized as effective approaches for solving large-scale optimization problems (LOPs). These alg (...truncated)