Investigation and Comparison of Inertia Weight Control Schemes in Particle Swarm Optimization

Particle swarm optimization (PSO) is a global search optimization algorithm that simulates the swarm behavior of birds. Compared with other evolutionary computation algorithms, PSO is simple, easy to implement, and has powerful optimization ability. However, the performance of PSO is heavily affected by the inertial weight parameter. Many studies have shown that PSO variant with smaller inertial weight has good local search ability and can improve the solution accuracy; while the PSO variant with larger inertial weight has good global search ability and can avoid falling into local optimum to a certain extent. Therefore, many researchers have proposed a variety of control schemes for dynamically adjusting the inertia weight. This paper systematically introduces, analyzes, and compares five typical decreasing and random control schemes in the literatures for dynamically adjusting the inertia weight. Moreover, a decreasing scheme based on concave exponential function, and two incremental schemes based on concave exponential function and convex exponential function are also designed and compared. The investigations and comparisons are conducted on 10 typical different unimodal and multimodal functions. The experimental investigations and comparisons are helpful for researchers to adopt different kind of inertia weight control schemes to solve different kinds of optimization problems.