Multi-objective particle swarm optimization based on minimal particle angle

Lecture Notes in Computer Science

Volumn 3644, Issue PART I, 2005, Pages 571-580

  Gong, Dun Wei ^a   Zhang, Yong ^a   Zhang, Jian Hua ^a  

^a CHINA UNIVERSITY OF MINING AND TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ELEMENTARY PARTICLES; OPTIMAL CONTROL SYSTEMS; PARETO PRINCIPLE; PROBLEM SOLVING; INTELLIGENT COMPUTING; PARTICLE SWARM OPTIMIZATION (PSO);

MINIMUM PARTICLE ANGLE; PARTICLE ANGLE; PARTICLE SWARM OPTIMIZATIONS; COMPUTATIONAL INTELLIGENCE METHODS; EXTENSION PROPERTIES; MOPSO; MULTI OBJECTIVE PARTICLE SWARM OPTIMIZATION; MULTI-OBJECTIVE OPTIMIZATION PROBLEM; MULTIOBJECTIVE FUNCTION OPTIMIZATION; PARETO DOMINANCE RELATIONSHIP; PARTICLE SWARM;

OPTIMIZATION; MULTIOBJECTIVE OPTIMIZATION;

EID: 27144444057     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11538059_60     Document Type: Conference Paper

References (11)

1. Kennedy J., Eberhart R.C.: Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, Piscataway (1995) 1942-1948
2. Hu X.: Multi-objective optimization using dynamic neighborhood particle swarm optimization. Proceedings of the 2002 IEEE Congress on Evolutionary Computation, Honolulu (2002)
3. Coello C. C. A., Lechuga M.S.: MOPSO:A proposal for multiple objective particle swarm optimization. Proceedings of the 2002 IEEE Congress on Evolutionary Computation, Honolulu (2002) 1051-1056
4. Fieldsend J., Singh S.: A multi-objective algorithm based upon particle swarm optimization. Proceedings of The U.K. Workshop on Computational Intelligence (2002) 34-44
5. Mostaghim S., Teich J.: Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO). Proceedings of the 2003 IEEE Swarm Intelligence Symposium, Indianapolis (2003) 26-33
6. 6. LI X. D.: A Non-dominated Sorting Particle Swarm Optimizer for Multi-objective Optimization. Proceedings of Genetic and Evolutionary Computation. Springer (2003) 37-48
7. Deb K., Pratap A.: A fast elitist multi-objective genetic algorithm: NSGA-II.IEEE Transactions on Evolutionary Computation, 6(2) (Apr,2002) 182-197
8. Zitller E., Deb K., Thiele L.: Comparison of multi-objective evolutionary algorithms: empirical results. Evolutionary Computation, 8(2) (Apr,2000) 173-195
9. Deb K., Thiele L., Laumanns M.: Scalable test problems for evolutionary multi-objective optimization. Technical Report 112, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (2001)
10. 10. Schon J. R.: Fault tolerant design using single and multi-criteria genetic algorithm optimization. Master's thesis. Massachusetts Institute of Technology, Cambridge (1995)
11. Knowles J., Corne D.: On metrics for comparing nondominated sets. IEEE Proceedings, World Congress on Computational Intelligence (CEC2002) 711-716