Using a Genetic Algorithm-based Hyper-heuristic to Tune MOEA/D for a Set of Various Test Problems

The multi-objective evolutionary algorithm based on decomposition (MOEA/D) is one of the most popular algorithms in the field of evolutionary multi-objective optimization (EMO). Even though MOEA/D has been widely used in many studies, it is likely that the performance of MOEA/D is not always optimized since the same MOEA/D implementation is often used on various problems with different characteristics. However, obtaining an appropriate implementation of MOEA/D for a different problem is not always easy, since there exists a wide variety of choices for the components and parameters in MOEA/D. In this paper, we examine the use of a genetic algorithm-based hyper-heuristic procedure to offline tune MOEA/D on a single test problem, a set of similar test problems, and a set of various test problems. A total of 26 benchmark test problems are used in our study. Experimental results show that the MOEA/D tuned for a set of various test problems does not always perform well. It is also shown that the MOEA/D tuned for a single test problem and for a set of similar test problems always has high performance. Our experimental results strongly suggest the necessity of using a tuning procedure to obtain a different MOEA/D implementation for a different type of problems.

I. Introduction

Multi-objective optimization problems (i.e., a class of optimization problems with two or more conflicting objectives) are ubiquitous in many real-world applications [1]-[3]. Evolutionary multi-objective optimization (EMO) algorithms have been well recognized as one of the effective approaches to solve multi-objective optimization problems [4]. The population-based search nature of EMO algorithms offers the advantage of finding a set of Pareto optimal solutions in a single run. Since a single run of EMO algorithms can find multiple optimal solutions, the main goal of EMO algorithms is to obtain a set of Pareto optimal solutions that are well-distributed over the true Pareto front.