Coordinate Particle Swarm Optimization with Dynamic Piecewise-mapped and Nonlinear Inertia Weights

To solve the premature convergence problem of particle swarm optimization, two novel methods are introduced to adjust the inertia weight in parallel according to different fitness values of two dynamic sub-swarms. When the fitness values of the particles are worse than the average, the inertia weight is adjusted by the introduced dynamic Piecewise linear chaotic map which can make the local-optima trapped particles dynamically break away from bad conditions and avoid premature convergence in very complex environments. On the contrary, when the fitness values of the particles are better than or equal to the average, two types of dynamic nonlinear equations are proposed to adjust the inertia weight in a continuous convex area which can retain the favorable conditions and achieve a good balance between global exploration and local exploitation. Experiments and comparisons demonstrated that the new proposed methods outperformed several other well-known improved PSO algorithms on many famous benchmark problems in all cases.