An IGD+ Performance Indicator Based Particle Swarm Optimizer For Multi-objective Optimization

Particle swarm optimizer (PSO) is suitable for solving multi-objective optimization problems (MOPs). However, there are two main issues for any multi-objective particle swarm optimizers (MOPSOs). The first issue is how to balance the convergence and diversity. The second issue is how to enhance the exploitation and exploration during the evolutionary procedure. In order to address these issues, an modified inverted generational distance (IGD(+)) performance indicator based PSO (IGD(+)-MOPSO) is proposed. The external archive updating strategies based on the IGD(+) indicator and the objective space decomposition method are proposed to select the evenly distributed non-dominated solutions. The leader updated strategy of each particle is based on the IGD(+) indicator value which is associated to the corresponding reference vector. The genetic operator is embedded into the evolutionary procedure to reset the position in order to help the particle jump out of the local optimum. We have conducted the simulation on some related benchmark test instances. The experimental results have indicated that the proposed algorithm is competitive with some related algorithms.