A solution to particle swarm optimization algorithm with adaptive inertia weight for unit commitment

Unit commitment is a large-scale and mixed-integer non-linear programming problem. An adaptive inertia weight of particle swarm optimization algorithm (AWPSO) is presented to increase the global and local search. The population is divided into two sub-populations according to the value of fitness, a...

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Published inDianli Xitong Baohu yu Kongzhi Vol. 37; no. 15; pp. 15 - 18
Main Authors Chang, Wen-Ping, Yu, Hai, Hua, Da-Peng
Format Journal Article
LanguageChinese
Published 01.08.2009
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Abstract Unit commitment is a large-scale and mixed-integer non-linear programming problem. An adaptive inertia weight of particle swarm optimization algorithm (AWPSO) is presented to increase the global and local search. The population is divided into two sub-populations according to the value of fitness, and the inertia weight is formulated as a function of evolution speed and stagnate state. The algorithm is tested with well-known benchmark functions and the simulation results with systems of up to 10 units and 24-h scheduling horizon are presented. The experiments show that the convergence accuracy is increased.
AbstractList Unit commitment is a large-scale and mixed-integer non-linear programming problem. An adaptive inertia weight of particle swarm optimization algorithm (AWPSO) is presented to increase the global and local search. The population is divided into two sub-populations according to the value of fitness, and the inertia weight is formulated as a function of evolution speed and stagnate state. The algorithm is tested with well-known benchmark functions and the simulation results with systems of up to 10 units and 24-h scheduling horizon are presented. The experiments show that the convergence accuracy is increased.
Author Hua, Da-Peng
Yu, Hai
Chang, Wen-Ping
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