Title :
A self-adapting improvement Particles Swarm Optimization algorithm and application in Multi-Criteria Group Decision-making
Author_Institution :
Sch. of Manage., Harbin Univ. of Sci. & Technol., Harbin, China
Abstract :
In order to avoid the phenomenon such as earliness and convergence emerged by Particles Swarm Optimization (PSO) Algorithm during searching target solution, the paper supplies a Self-Adapting Improvement PSO (SAI-PSO) Algorithm including of its improvement methods, strategies and operational structure from the angles of evaluation characteristic generated and fitness function selected. The experimental results show that SAI-PSO uses self-adapting strategies to amend inertia weighing and particles selection mechanism, it could reduce the amount of computation for particles optimization, strength the algorithm convergence speed, obtains the better solution precision, which provides the valuable lessons and references for Multi-Criteria Group Decision-making resolution.
Keywords :
decision making; group theory; particle swarm optimisation; self-adjusting systems; SAI-PSO; fitness function; inertia weighing; multicriteria group decision making; operational structure; particle selection mechanism; self adapting improvement particle swarm optimization algorithm; self-adapting strategy; target solution searching; Computational modeling; Convergence; Decision making; Genetic algorithms; Heuristic algorithms; Optimization; Particle swarm optimization; Multi-Criteria Group Decision making; SAI-PSO; algorithm operational structure;
Conference_Titel :
Electronic and Mechanical Engineering and Information Technology (EMEIT), 2011 International Conference on
Conference_Location :
Harbin, Heilongjiang, China
Print_ISBN :
978-1-61284-087-1
DOI :
10.1109/EMEIT.2011.6023138
