An Improved Particle Swarm Optimization with Feasibility-Based Rules for Mixed-Variable Optimization Problems

This paper presents an improved particle swarm optimization algorithm with feasibility- based rules (FRIPSO) to solve mixed-variable constrained optimization problems. Different kinds of variables are dealt in different ways in FRIPSO algorithm. Constraint handling is based on simple feasibility-based rules without the use of a penalty function which is frequently cumbersome to parameterize, nor need it to guarantee the particles be in the feasible region at all time which turn out to cost much time sometimes. In order to improve the convergence speed of FRIPSO with the iteration growing and to find global optimum, the standard PSO is used to find a better position for the best history position of the swarm on the condition that the discrete value are same with those of g_best in each iteration. Two practical benchmark mixed-variable optimization problems are tested by our FRIPSO algorithm to demonstrate the effectiveness and robustness of the proposed approach.