Comparison of multi-modal optimization algorithms based on evolutionary algorithms

Abstract

Many engineering optimization tasks involve finding more than one optimum solution. The present study provides a comprehensive review of the existing work done in the field of multi-modal function optimization and provides a critical analysis of the existing methods. Existing niching methods are analyzed and an improved niching method is proposed. To achieve this purpose, we first give an introduction to niching and diversity preservation, followed by discussion of a number of algorithms. Thereafter, a comparison of clearing, clustering, deterministic crowding, probabilistic crowding, restricted tournament selection, sharing, species conserving genetic algorithms is made. A modified niching-based technique-modified clearing approach-is introduced and also compared with existing methods. For comparison, a versatile hump test function is also proposed and used together with two other functions. The ability of the algorithms in finding, locating, and maintaining multiple optima is judged using two performance measures: (i) number of peaks maintained, and (ii) computational time. Based on the results, we conclude that the restricted tournament selection and the proposed modified clearing approaches are better in terms of finding and maintaining the multiple optima.