An outward search to increase population diversity for optimization algorithms

Abstract

Outward search (OS) is a new scheme proposed to provide diverse forms for improving convergence in evolutionary algorithms. Rather than using new functionalities, OS is performed using the differential-vector equations of an evolutionary algorithm. Three OS schemes are recommended in this study to obtain solutions that improve the performance of an evolutionary algorithm. The first one uses the original equations of the algorithm to generate either an OS solution or a candidate solution. The second utilizes the original equations to produce an OS solution and a candidate solution simultaneously for one individual. The last employs equations of another algorithm to create an OS solution for the studied algorithm. Three bio-inspired algorithms were examined using the CEC2015 benchmark suite to compare the respective performances of the proposed OS schemes. The results of the comparison indicate that searching regions outward from the current area outperformed examining oppositional locations obtained by opposition-based learning.