Multiobjective optimization by Artificial Fish Swarm Algorithm

Artificial Fish Swarm Algorithm (AFSA) is a kind of swarm intelligence algorithm, which has the features of fast convergence, good global search capability, strong robustness and so on. In this paper, an approach using AFSA to solve the multiobjective optimization problem is proposed. In this algori...

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Veröffentlicht in:2011 IEEE International Conference on Computer Science and Automation Engineering Jg. 3; S. 506 - 511
Hauptverfasser: Mingyan Jiang, Kongcun Zhu
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2011
Schlagworte:
AFSA
Approximation algorithms
Educational institutions
Genetic algorithms
global information
Information science
Marine animals
multiobjective optimization
Optimization
Pareto dominance
Particle swarm optimization
ISBN:9781424487271, 1424487277
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Zusammenfassung:Artificial Fish Swarm Algorithm (AFSA) is a kind of swarm intelligence algorithm, which has the features of fast convergence, good global search capability, strong robustness and so on. In this paper, an approach using AFSA to solve the multiobjective optimization problem is proposed. In this algorithm, the concept of Pareto dominance is used to evaluate the pros and cons of Artificial Fish (AF). Artificial fish swarm search the solution space in parallel and External Record Set is used to save the found Pareto optimal solutions. The simulation results of 4 benchmark test functions illustrate the effectiveness of the proposed algorithm.
ISBN:9781424487271
1424487277
DOI:10.1109/CSAE.2011.5952729