Adaptive evolutionary multi-objective particle swarm optimization algorithm

Adaptive evolutionary particle swarm optimization(AEPSO)algorithm is proposed for multi-objective problem.Non-dominated sorting and dynamic aggregate method are used to guide the flight of particles and improve the diversity of the Pareto solutions,which incorporates adaptive inertia weight and a special mutation operation into PSO to enhance the global exploratory capability and avoid pre-mature convergence.Four well-known benchmark test functions ZDT1~ZDT4 are used to test the performance of the proposed algorithm.Simulation results show that the AEPSO algorithm can converge to the global optimal with good accuracy while keeps the diversity of the Pareto solutions.