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Artificial Searching Swarm Algorithm and Its Performance Analysis
Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China
Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China
Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China
- 1 Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China
- 2 Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China
- 3 Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China
Applied Mathematics·Volume 03 (2012)·Pages 1435–1441·Published 31 October 2012·DOI10.4236/am.2012.330202
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Abstract
Artificial Searching Swarm Algorithm (ASSA) is a new optimization algorithm. ASSA simulates the soldiers to search an enemy’s important goal, and transforms the process of solving optimization problem into the process of searching optimal goal by searching swarm with set rules. This work selects complicated and highn dimension functions to deeply analyse the performance for unconstrained and constrained optimization problems and the results produced by ASSA, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Fish-Swarm Algorithm (AFSA) have been compared. The main factors which influence the performance of ASSA are also discussed. The results demonstrate the effectiveness of the proposed ASSA optimization algorithm.
KeywordsArtificial Searching Swarm AlgorithmBionic Intelligent Optimization AlgorithmOptimizationEvolutionary ComputationSwarm Intelligence
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