A Tuning Free Approach to Multi-guide Particle Swarm Optimization

Multi-guide particle swarm optimization (MGPSO) is a highly competitive algorithm for multi-objective optimization problems. MGPSO has been shown to perform better than or similar to several state-of-the-art multi-objective algorithms for a variety of multi-objective optimization problems (MOOPs). When comparing algorithmic performance it is recommended that the control parameters of each algorithm be tuned to the problem. However, control parameter tuning is often an expensive and time-consuming process. Recent work has derived the theoretical stability conditions on the MGPSO control parameters to guarantee order-1 and order-2 stability. This paper investigates an approach to randomly sample control parameter values for MGPSO that satisfy these stability conditions. It was shown that the proposed approach yields similar performance to that of MGPSO using tuned parameters, and therefore is a viable alternative to parameter tuning.