Multi-objective particle swarm optimizationalgorithm based on self-adaptive learning
基于自适应学习的多目标粒子群优化算法

When evolutionary algorithm is applied to multi-objective optimization problems, it often requires a large population size and a large number of evolution generation. However, it consumed plenty of computation overhead of evaluating objective functions but just resulted in poor improvement of the search efficiency. This paper proposed a multi-objective particle swarm optimization algorithm based on self-adaptive learning of optimal search directions. The algorithm used the self-adaptive inertia weights to get the trade-off between the global and local search. And it used the clustering crowding to maintain the uniform distribution of the non-dominated Pareto solutions. And the algorithm incorporated the nearest neighbor rule to seek the best target in the non-dominated Pareto solutions to get the optimal flying direction for each particle, it sped the flying of single particle and kept the diversity of the flying directions for the particle swarm. The experimental results show that the algorithm can drive the particle swarm to approximate quickly and uniformly to the Pareto front, and decrease the evaluation cost of objective functions significantly.