Title :
Multiobjective optimization by Artificial Fish Swarm Algorithm
Author :
Jiang, Mingyan ; Zhu, Kongcun
Author_Institution :
Sch. of Inf. Sci. & Eng., Shandong Univ., Jinan, China
Abstract :
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.
Keywords :
Pareto optimisation; artificial intelligence; search problems; Pareto optimal solutions; artificial fish swarm algorithm; convergence; global search capability; multiobjective optimization; parallel-external record set; robustness; Approximation algorithms; Educational institutions; Genetic algorithms; Information science; Marine animals; Optimization; Particle swarm optimization; AFSA; Pareto dominance; global information; multiobjective optimization;
Conference_Titel :
Computer Science and Automation Engineering (CSAE), 2011 IEEE International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-8727-1
DOI :
10.1109/CSAE.2011.5952729
