Parallel Dynamic Multi-Objective Optimization Evolutionary Algorithm

Abstract

Multi-objective optimization evolutionary algorithms (MOEAs) are considered as the most suitable heuristic methods for solving multi-objective optimization problems (MOPs). These MOEAs aim to search for a uniformly distributed, near-optimal and near-complete Pareto front for a given MOP. However, MOEAs fail to achieve their aim completely because of their fixed population size. To overcome this limit, an evolutionary approach of multi-objective optimization was proposed; the dynamic multi-objective evolutionary algorithms (DMOEAs). This paper deals with improving the user requirements (i.e., getting a set of optimal solutions in minimum computational time). Although, DMOEA has the distinction of dynamic population size, being an evolutionary algorithm means that it will certainly be characterized by long execution time. One of the main reasons for adapting parallel evolutionary algorithms (PEAs) is to obtain efficient results with an execution time much lower than the one of their sequential counterparts in order to tackle more complex problems. Thus, we propose a parallel version of DMOEA (i.e., PDMOEA). As experimental results, the proposed PDMOEA enhances DMOEA in terms of three criteria: improving the objective space, minimization of computational time and converging to the desired population size.