A HYBRID PARTICLE SWARM EVOLUTIONARY ALGORITHM FOR CONSTRAINED MULTI-OBJECTIVE OPTIMIZATION

In this paper, a hybrid pal tick swarm evolutionary algorithm is proposed km solving constrained multi-objective optimization Firstly, in order to keep some particles with smaller constraint violations, a threshold value is designed, the updating strategy of particles is revised based on the threshold value, then in order to keep some particles with smaller lank values, an infeasible elitist preservation strategy is proposed in older to make the infeasible elitists act as bridges connecting disconnected feasible regions Secondly, in older to find a set of diverse and well-distributed Pareto-optimal solutions, a new crowding distance function is designed for bi-objective optimization problems It can assign huger crowding distance function values not only for the particles located in the sparse legion but also for the particles located near to the boundary of the Pareto front In this step, the reference points are given, and the particles which are neat to the reference points are kept no matter how crowded these points are Thirdly, a new mutation operator with two phases is proposed In the first phase, the total force is computed first, then it is used as a mutation direction, searching along this direction, better pal ticks will be found The comparative study shows the proposed algorithm can generate widely spread and uniformly distributed solutions on the entire Pareto front