Leaders and speed constraint multi-objective particle swarm optimization

The particle swarm optimization (PSO) algorithm has been very successful in single objective optimization as well as in multi-objective (MO) optimization. However, the selection of representative leaders in MO space is a challenging task. Most previous MO-based PSOs used exclusively the concept of non-dominance to select leaders which might slow down the search process if the selected leaders are concentrated in a specific region of the objective space. In this paper, a new restriction mechanism is added to non-dominance in order to select leaders in more representative (distributed) way. The proposed algorithm is named leaders and speed constrained multi-objective PSO (LSMPSO) which is an extended version of SMPSO. The convergence speed of LSMPSO is compared to state-of-the-art metaheuristics, namely, NSGA-II, SPEA2, GDE3, SMPSO, AbYSS, MOCell, and MOEA/D. The ZDT and DTLZ family problems are utilized for the comparisons. The proposed LSMPSO algorithm outperformed the other algorithms in terms of convergence speed.