A multi-objective immune algorithm with dynamic population strategy

Abstract

In this paper, we propose a multi-objective immune algorithm with dynamic population strategy, named MOIA-DPS, which introduces a control strategy of dynamic population size into multi-objective immune algorithm (MOIA). This scheme helps to compensate the lack of diversity due to the clonal principle in MOIA and adequately exploits the computational resource during the evolutionary progress. In MOIA-DPS, the status of external archive (full or not full) is used to decide the enlargement or the reduction of population size, so as to adaptively adjust the computational resource. Moreover, in order to further enhance the robustness of MOIA-DPS, we present an effective DE operator with two search models, called TDE, in which two search models such as rand/2/bin and rand/1/bin are alternatively exchanged according to a probability. When compared to four state-of-the-art heuristic algorithms, i.e., ISDE+, MOEA/D-GRA, AbYSS, CMPSO, and four MOIAs, i.e., IMADE, DMMO, HEIA, and AIMA, MOIA-DPS was shown to present several advantages in solving different sets of benchmark problems.