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

Anima Naik,Suresh Chandra Satapathy

doi:10.1007/s40747-020-00189-6

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.

Highlights

The meta-heuristic optimization algorithm is a practical approach for solving global optimization problems
The best result is represented in boldface
From the Table, we get that the social group optimization (SGO) algorithm outperforms than all other algorithms

Summary

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. Evolutionary algorithms mimic concepts of evolution in nature. 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. 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]

Methods

Discussion

Conclusion