In this paper, a novel intelligentoptimizationalgorithm - Artificial Tribe algorithm (ATA) is presented and discussed based on the analyses of the principle and uniform framework of the bionicintelligent Optimiz...
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ISBN:
(纸本)9781424450015
In this paper, a novel intelligentoptimizationalgorithm - Artificial Tribe algorithm (ATA) is presented and discussed based on the analyses of the principle and uniform framework of the bionicintelligentoptimizationalgorithms (BIOA). ATA simulates the existent skills of the natural tribes, and actualizes the optimization purpose through the propagation and migration behaviors of the tribes. ATA is used for optimizing multivariable functions and the results produced by ATA, Genetic algorithm (GA) have been compared. The results showed that ATA is a powerful algorithm for global optimization problems.
Artificial Searching Swarm algorithm (ASSA) is an intelligentoptimizationalgorithm, and its performance has been analyzed and compared with some famous algorithms. For farther understanding the running principle of ...
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ISBN:
(数字)9783642134951
ISBN:
(纸本)9783642134944
Artificial Searching Swarm algorithm (ASSA) is an intelligentoptimizationalgorithm, 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.
Artificial Tribe algorithm (ATA) is a novel intelligentoptimizationalgorithm based on the simulation of bionicintelligentoptimizationalgorithm. This work discusses the main factors which influence the perform...
详细信息
Artificial Tribe algorithm (ATA) is a novel intelligentoptimizationalgorithm based on the simulation of bionicintelligentoptimizationalgorithm. This work discusses the main factors which influence the performance of ATA, and compares the performance of ATA with that of genetic algorithm (GA), particle swarm optimization (PSO), and artificial fish-swarm algorithm (AFSA) for optimization multivariable functions. The simulation results showed that ATA outperforms the mentioned algorithms in global optimization problems.
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