Many-objective particle swarm optimization algorithm for fitness ranking

Due to the complexity and difficulty of solving the many-objective optimization problem,a many-objective particle swarm optimization algorithm for ensemble fitness ranking is proposed.In this algorithm,the nearest vector between the individual and reference points in the population is obtained,and the individuals in the population are sorted by the penalty-based boundary intersection approach.Then,the poor individuals in the population are deleted and the elite individuals are saved in the external archives.The four advanced many-objective evolutionary optimization algorithms are adopted to make comparisons on 5,8,10,15 objectives of 13 standard test sets.Experimental results show that the performance of the proposed algorithm is better than comparison algorithms in most of the test cases.It has also been proved that the algorithm has good convergence and diversity,and that it can effectively deal with many-objective optimization problems.