A Multiobjective multifactorial optimization algorithm based on decomposition and dynamic resource allocation strategy

Multiobjective multifactorial optimization (MO-MEO), i.e., multiple multiobjective tasks are simultaneously optimized by a single population, has received considerable attention in recent years. Traditional algorithms for the MO-MFO usually allocate equal computing resources to each task, however, this may not be reasonable due to the fact that different tasks usually have different degrees of difficulty. Motivated by the idea that the limited computing resources should be adaptively allocated to different tasks according to their difficulties, this paper proposes an algorithm for the MO-MFO based on decomposition and dynamic resource allocation strategy (denoted as MFEA/D-DRA). In the MFEA/D-DRA, each multiobjective optimization task is firstly decomposed into a series of single-objective subproblems. Thereafter, a single population is used to evolve all the single-objective subproblems. In the process of evolution, subproblems with fast evolution rate will have the opportunity to get more rewards, i.e., computing resources. The evolution rate is measured by a utility function and updated periodically. Moreover, different multiobjective optimization tasks can communicate with each other according to a random mating probability. Finally, a set of evenly distributed approximate Pareto optimal solutions is obtained for each multiobjective optimization task. The statistical analysis of experimental results illustrates the superiority of the proposed MFEA/D-DRA algorithm on a variety of benchmark MO-MFO problems. (C) 2019 Elsevier Inc. All rights reserved.