Experimental Evaluation of Nature-Inspired Algorithms on High Dimensions

Abstract

This paper concentrates on four very similar metaheuristic optimization algorithms: Differential Evolution (DE), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Cuckoo Search (CS) algorithm. These optimization algorithms are used to solve optimization problems with real parameters having real parametric functions. This paper gives a brief discussion of these algorithms followed by the experiment over various benchmark functions. Many researchers have attempted to compare these algorithms on various benchmark functions. This work compares these algorithms on high dimensions over benchmark functions like Ackley’s function, Alpine function, Brown function, Deb function, and Powell sum function. These above algorithms are compared on the basis of time required to converge on various benchmark functions. Our experiments indicate that the CS algorithm outperforms others when the dimensions are high, whereas in some cases, it is comparable to DE. © 2019, Springer Nature Singapore Pte Ltd.