Escaping from Local Optima and Convergence Mechanisms Based on Search History in Evolutionary Multi-criterion Optimization

Abstract

In this paper, a new local search approach using a search history in evolutionary multi-criterion optimization (EMO) is proposed. This approach was designed by two opposite mechanisms (escaping from local optima and convergence search) and assumed to incorporate these into an usual EMO algorithm for strengthening its search ability. The main feature of this approach is to perform a high efficient search by changing these mechanisms according to the search condition. If the search situation seems to be stagnated, escape mechanism would be applied for shifting search point from this one to another one. On the other hand, if it observes no sign of the improvement of solutions after repeating this escape mechanism for a fixed period, convergence mechanism is applied to improve the quality of solution through an intensive local search. This paper presents a new approach, called “escaping from local optima and convergence mechanisms based on search history - SPLASH -”. Experimental results showed the effectiveness of SPLASH and the workings of SPLASH’s two mechanisms using WFG test suites.