Normalized ranking based particle swarm optimizer for many objective optimization

Cheng, Shi1; Lei, Xiujuan1; Chen, Junfeng2; Feng, Jiqiang3; Shi, Yuhui4

10.1007/978-3-319-68759-9_29

2017

16113349

11th International Conference on Simulated Evolution and Learning, SEAL 2017

10593 LNCS

347-357

Shenzhen, China

Springer Verlag

Nearly all solutions are Pareto non-dominated for multi-objective problems with more than three conflicting objectives. Thus, the comparison of solutions is a critical issue in many objective optimization. A simple but effective normalized ranking metric based method is proposed to compare solutions in this paper. All solutions are ranked by the sum of normalized fitness value of each objective. A solution with a small value is considered to be a good solution for minimum optimization problems. To enhance the population diversity of all solutions, the solutions with small values and the solutions with better fitness values on each objective are kept in an archive and updated per iteration. This ranking metric is further utilized in a particle swarm optimization algorithm to solve multiobjective and many objective problems. Four benchmark problems are utilized to test the proposed algorithm. Experimental results demonstrate that the proposed algorithm is a promising approach for solving the multiobjective and many objective optimization problems.
© Springer International Publishing AG 2017.

其他

EI

Shenzhen Science and Technology Innovation Commission[ZDSYS201703031748284] ; National Natural Science Foundation of China[61672334] ; National Natural Science Foundation of China[61403121] ; National Natural Science Foundation of China[61273367] ; [GK201703062]

20174704428213

Iterative methods ; Particle swarm optimization (PSO)

Optimization Techniques:921.5 ; Numerical Methods:921.6

EV Compendex

1.School of Computer Science, Shaanxi Normal University, Xi’an, China
2.College of IOT Engineering, Hohai University, Changzhou, China
3.Institute of Intelligent Computing Science, Shenzhen University, Shenzhen, China
4.Shenzhen Key Lab of Computational Intelligence, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

Cheng, Shi,Lei, Xiujuan,Chen, Junfeng,et al. Normalized ranking based particle swarm optimizer for many objective optimization[C]:Springer Verlag,2017:347-357.