On the Farther Analysis of Performance of the Artificial Searching Swarm Algorithm
Artificial Searching Swarm Algorithm (ASSA) is an intelligent optimization algorithm, and its performance has been analyzed and compared with some famous algorithms. For farther understanding the running principle of ASSA, this work discusses the functions of three behavior rules which decide the moves of searching swarm. Some typical functions are selected to do the simulation tests. The function simulation tests showed that the three behavior rules are indispensability and endow the ASSA with powerful global optimization ability together.
Keywordsartificial searching swarm algorithm bionic intelligent optimization algorithm optimization evolutionary computation swarm intelligence
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- 1.Holland, J.H.: Adaptation in Nature and Artificial System. MIT Press, Cambridge (1992)Google Scholar
- 2.Colorni, A., Dorigo, M., Maniezzo, V., et al.: Distributed optimization by ant colonies. In: Proceedings of the 1st European Conference on Artificial Life, pp. 134–142 (1991)Google Scholar
- 3.Kennedy, J., Eberha, R.C.: Particle Swarm Optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
- 4.Li, X.L., Shao, Z.J., Qian, J.X.: An Optimization Method Based on Autonomous Animats: Fish-swarm Algorithm. Systems Engineering-Theory & Practice 22(11), 32–38 (2002)Google Scholar
- 5.Eusuffm, M., Lansey, K.E.: Optimization of Water Distribution Network Design Using Shuffled Frog Leaping Algorithm. J. Water Resources Planning and Management 129(3), 21–225 (2003)Google Scholar
- 6.Chen, T.G.: A Simulative Bionic Intelligent Optimization Algorithm: Artificial Searing Swarm Algorithm and its performance Analysis. In: Proceedings of the Second International Joint Conference on Computational Sciences and Optimization, vol. 2, pp. 864–866 (2009)Google Scholar