A comparative study on prominent nature inspired algorithms for function optimization

Abstract

Optimization includes finding best available values of some objective function given a defined domain. Function optimization (FO) is the well-studied continuous optimization task which aim is to find best suited parameter values to get optimal value of a function. A number of techniques have been investigated in last few decades to solve FO and recently Nature Inspired Algorithms (NIAs) become popular to solve it. The objective of this study is to draw a fair comparison among prominent NIAs in solving benchmark test functions. Algorithms we selected are Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) optimization, Firefly Algorithm (FFA), Cuckoo Search Optimization (CSO), Group Search Optimization (GSO) and Grey Wolf Optimizer (GWO). Among the methods, GA is the pioneer method for optimization, PSO is the most popular in recent time and GWO is the most recently developed method. Experimental results revealed that GWO is the overall best method among the NIAs and PSO is still promising to solve bench mark functions.