An Effective Algorithm Based on Space Net Optimization for Multi-Objective Optimization

Chun-Wei Tsai, Cheng-Hao Lin, Wei-Hong Wang

Abstract: How to keep the information of most, if not all, of the searched solutions of a metaheuristic algorithm has been an important research issue in recent years. The main reason is because the information is very helpful in determining precisely the search directions during the convergence process. Recently, the space net optimization (SNO) was presented that attempts to use the information from most of the searched solutions to understand the solution (or objective) space landscape of the single objective bound constrained problem. In this study, a simplified version of SNO, called multi-objective simple space net optimization (MOSSNO), is presented to solve the multi-objective optimization problem. This algorithm adopts some essential mechanisms and operators of SNO; namely, (1) elastic points and space net to make it possible for a metaheuristic algorithm to depict the landscape of the objective space of an optimization problem and (2) expected values of different regions in the objective space to guide searches during the convergence process. Moreover, the proposed algorithm uses an external archive mechanism to save the nondominated solutions so that it is able to avoid searching the areas that have been searched before repeatedly. Experimental results show that the proposed method can provide better results than the other multi-objective evolutionary algorithms (MOEAs) evaluated in this study in terms of the inverted generational distance (IGD) in most cases.

External IDs:dblp:conf/cec/TsaiLW24