Multiobjective optimization by artificial fish swarm algorithm

Proceedings - 2011 IEEE International Conference on Computer Science and Automation Engineering, CSAE 2011

Volumn 3, Issue , 2011, Pages 506-511

  Jiang, Mingyan ^a   Zhu, Kongcun ^a  

^a SHANDONG UNIVERSITY   (China)

Author keywords

AFSA; global information; multiobjective optimization; Pareto dominance

Indexed keywords

AFSA; ARTIFICIAL FISH; ARTIFICIAL FISH SWARM ALGORITHMS; BENCHMARK TESTS; FAST CONVERGENCE; GLOBAL INFORMATIONS; GLOBAL SEARCH CAPABILITY; MULTI OBJECTIVE; MULTI-OBJECTIVE OPTIMIZATION PROBLEM; PARETO DOMINANCE; PARETO OPTIMAL SOLUTIONS; SIMULATION RESULT; SOLUTION SPACE; SWARM INTELLIGENCE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BENCHMARKING; CELLULAR AUTOMATA; COMPUTER SCIENCE; CONVERGENCE OF NUMERICAL METHODS; FISH; PARETO PRINCIPLE;

MULTIOBJECTIVE OPTIMIZATION;

EID: 80051890957     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CSAE.2011.5952729     Document Type: Conference Paper

References (15)

1. Xiaolei Li, A New Intelligent Optimization-Artificial Fish School Algorithm (Doctor thesis), University of Zhejiang, Zhejiang, China, 2003. (in Chinese)
2. Mingyan Jiang, Yongming Cheng, Simulated Annealing Artificial Fish Swarm Algorithm, IEEE Proceedings of 8th World Congress on Intelligent Control and Automation, July 2010, pp: 1590-1593
3. Kongcun Zhu, Mingyan Jiang, Quantum Artificial Fish Swarm Algorithm, IEEE Proceedings of 8th World Congress on Intelligent Control and Automation, July 2010, pp: 1-5
4. Kongcun Zhu, Mingyan Jiang, Yongming Cheng, Niche Artificial Fish Swarm Algorithm Based on Quantum Theory, IEEE Proceedings of 10th International Conference on Signal Processing, Oct. 2010, pp: 1425-1428
5. Schffer J D, Multiple objective optimization with vector evaluated genetic algorithm, Proceedings of 1st International Conference on Genetic Algorithm and Their Applications, Hillsdale: Lawrence Erlbaum Associates, Inc., 1985, pp: 93-100
6. Fonseca C M, Fleming P J, Genetic algorithm for multi objective optimization: Formulation, discussion and generation, Proceedings of 5st International Conference on Genetic Algorithm, San Mateo: Morgan Kauffman Publishers, 1993, pp: 416-423
7. Horn J, Nafpliotis N, Goldberg D E ,A niched Pareto genetic algorithm for multi objective optimization, Proceedings of 1st IEEE Congress on Evolutionary Computation, Piscataway: IEEE Press, 1994, pp: 82-87
8. Srinivas N, Deb K, Multi objective optimization using nondominated sorting in genetic algorithm, Evolutionary Computation, 1994,2(3), pp: 221-248
9. Knowles J D, Corne D W, Approximating the non-dominated front using the Pareto archived evolution strategy, Evolutionary Computation, 2000,8(2), pp: 149-172
10. Deb K, Pratab A, Agarwal S, et, A A Fast and Elitist Multi objective Genetic Algorithm: NSGA II, IEEE Transactions on Evolutionary Computation, 2002,6(2), pp: 182-197 (Pubitemid 34555372)
11. Zitzler E, Laumanns M, Thiele L, SPEA2: Improving the strength Pareto evolutionary algorithm, Proceedings of International Conference on Evolutionary Methods for Design, Optimization and Control with Application to Industrial Problems, Berlin: Springer, 2002, pp: 95-100
12. Coello Coello C A, Pulido G T, Lechuga M S. Handling Multiple Objectives With Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation, 2004, 8(3), pp: 256-279
13. Liang J J, Qin A K ,Suganthan P N, Evaluation of comprehensive learning particle swarm optimizer, Lectures Notes in Computer Science, Berlin: Springer, 2004, 3316, pp: 230-235 (Pubitemid 39750737)
14. Liu D, Tan K C, Goh C K, A multi-objective memetic algorithm based on particle swarm optimization, IEEE Transactions on System, Man and Cybernetics, Part B, 2007, 37(1), pp: 42-50 (Pubitemid 46358478)
15. Mingyan Jiang, Yongming Cheng, Dongfeng Yuan. Improved Artificial Fish Swarm Algorithm, Proceedings of the 5th International Conference on Natural Computation (ICNC'09), 2009, 14-16 August, Tianjin China.