A Parallel Cooperative Coevolutionary SMPSO Algorithm for Multi-objective Optimization

This paper presents a new parallel multi-objective cooperative coevolutionary variant of the Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO) algorithm. SMPSO adopts a strategy for limiting the velocity of the particles that prevents them from having erratic movements. This characteristic provides the algorithm with a high degree of reliability. The proposed approach, called CCSMPSO, is based on a new design and implementation of SMPSO in a cooperative coevolutionary (CC) framework. In such an architecture, the population is split into several subpopulations, which are in turn in charge of optimizing a subset of the global solution by using the original multi-objective algorithm. We compare our work with two different state-of-the-art multi-objective CC metaheuristics, namely CCNSGA-II and CCSPEA2, as well as the original SMPSO in order to demonstrate its effectiveness. Our experiments indicate that our proposed solution, CCSMPSO, offers significant computational speedups, a higher convergence speed and better and comparable results in terms of solution quality, when compared to the other two CC algorithms and SMPSO, respectively. Three different criteria are used for making the comparisons, namely the quality of the resulting approximation sets, average computational time and the convergence speed to the Pareto front.