Proposal of particle swarm optimization methods with nonlinear dissipative term

Optimization methods based on meta-heuristics are proposed as a class of global optimization methods, by which the global minimum can be obtained without trapping in local minima. Particle swarm optimization(PSO), which is one of those methods, is known for its high search ability and easy implement. However, it might be difficult to find the global optimum for optimization problems which have a lot of decision variables and local optima. In this paper, we propose three types of new PSO to clear the weak point. One is a model with the nonlinear dissipative term intoroduced by Fujita, Yasuda and Yokoyama ⁽⁴⁾ to prohibit the search point's velocity being zero. The others are models with the nonlinear dissipative term with the pbest or the gbest information to disturb the search around them.