A multi-objective artificial physics optimization algorithm based on ranks of individuals

作者：Wang, Yan^*; Zeng, Jian-chao

来源：Soft Computing, 2013, 17(6): 939-952.

DOI：10.1007/s00500-012-0969-3

• Multi-objective optimization
• Rank
• Artificial physics optimization
• Neighborhood
• Crowding degree

This paper proposes a multi-objective artificial physics optimization algorithm based on individuals' ranks. Using a Pareto sorting based technique and incorporating the concept of neighborhood crowding degree, evolutionary individuals in the search space are evaluated at first. Then each individual is assigned a unique serial number in terms of its performance, which affects the mass of the individual. Thereby, the population evolves towards the direction of the Pareto-optimal front. Synchronously, the presented approach has good diversity, such that the population is spread evenly on the Pareto front. Results of simulation on a number of difficult test problems show that the proposed algorithm, with less evolutionary generations, is able to find a better spread of solutions and better convergence near the true Pareto-optimal front compared to classical multi-objective evolutionary algorithms (NSGA, SPEA, MOPSO) and to simple multi-objective artificial physics optimization algorithm.

• 出版日期2013-6
• 单位太原科技大学