A New Evolutionary Algorithm Based on Simplex Crossover and PSO Mutation for Constrained Optimization Problems

A new approach is presented to handle constraints optimization using evolutionary algorithms in this paper. First, we present a specific varying fitness function technique, this technique incorporates the problem's constraints into the fitness function in a dynamic way. The resulting varying fitness function facilitates the EA search. On one hand, The new fitness function without any parameters can properly evaluate not only feasible solution, but also infeasible one, on other hand, the information of the best solution in the current population is also concerned in fitness function, which make search more efficient. Meanwhile, a new crossover operator based on simplex crossover operator and a new PSO mutation operator is also proposed, and both the operators utilize the information of good individuals in the current populations so they can produce high quality offspring. Based on these, a new evolutionary algorithm for constrained optimization problems is proposed. The simulations are made on five widely used benchmark problems, and the results indicate the proposed algorithm is effective.