A novel particle swarm optimizer for many-objective optimization

A novel many-objective particle swarm optimization (PSO) algorithm called IDMOPSO is presented in this study to robustly and effectively address many-objective optimization problems (MaOPs). IDMOPSO is based on a performance indicator and direction vectors. A selection strategy based on the quality indicator I epsilon+ and Pareto dominance for personal best (pbest) particles is proposed to ensure the convergence and diversity of the algorithm and enhance the capability of local exploration. An external archive based on I epsilon+ and direction vectors is used to preserve the diversity of non-dominated solutions found in the search process. A multi-global optimal (gbest) particle selection method is developed to increase global search ability and ensure the particles' diversity. This method allows each particle to be assigned to a different gbest particle. This method differs from the traditional method, wherein only one gbest particle is allocated for the whole population of PSO. We aim to design a robust multi-objective evolutionary algorithm to deal with MaOPs. Extensive comparative experiments on DTLZ and DTLZ(-1) problems with varied numbers of objectives show that IDMOPSO is effective and flexible in addressing MaOPs. The influences and effectiveness of the proposed strategies are also analyzed in detail.