Solving multi-objective optimization problems using self-adaptive harmony search algorithms

Abstract

In recent years, there have been many multi-objective evolutionary algorithms proposed to solve multi-objective optimization problems. These evolutionary algorithms generate many solutions for iterations and move to the true Pareto optimal region gradually. As expected, since the harmony search algorithm can also iterate over a large number of solutions (in HM memory) and moves to the true Pareto optimal region, we use it to solve multi-objective optimization problems. In this paper, the proposed system architecture can be divided into two phases. In the first phase, we aim to search feasible solution regions as widely as possible in the entire process. In the second phase, we focus on searching optimized solutions stepwise in the feasible solution regions. Since the proposed algorithm uses many parameters, we adjust some of them in a self-adaptive way and call the algorithm self-adaptive. In the experiments, we use the eleven well-known multi-objective problems and three many-objective problems to examine the proposed algorithm and other existing algorithms, based on five performance indicators. As a result, our algorithm achieves better performances than the others in inverted generational distance, hypervolume, and spread indicators.