Particle Swarm Optimization for Minimax Problems

This paper investigates the ability of the Particle Swarm Optimization (PSO) method to cope with minimax problems through experiments on well--known test functions. Experimental results indicate that PSO tackles minimax problems effectively. Moreover, PSO alleviates difficulties that might be encountered by gradient--based methods, due to the nature of the minimax objective function, and potentially lead to failure. The performance of PSO is compared with that of other established approaches, such as the Sequential Quadratic Programming (SQP) method and a recently proposed Smoothing Technique# conclusions are derived