Comparative analysis of optimization strategies by software complex “Metaheuristic nature-inspired methods of global optimization”

Abstract

This article discusses the comparative efficiency analysis results of algorithms for finding a global extrema for multivariable functions with parallelepiped constraints. Three metaheuristic nature-inspired optimization algorithms such as Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA) and Perch School Search (PSS) are presented. For algorithms comparative analysis the software complex was created, which allows visualizing a process of obtaining the approximate solution of standard generally accepted benchmark problems. The different examples of optimization algorithms results are given. Benchmark functions structure provides fairly estimating of compared optimization methods. The purpose of the paper is collecting and subsequent analysis of statistical results of algorithms accuracy and convergence pattern. Statistical data analysis makes it possible to choose the most suitable optimization method and to formulate valid recommendations to pick parameters for the most efficient solution of arbitrary objective function optimization problem.