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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 271831, 14 pages
http://dx.doi.org/10.1155/2012/271831
Research Article

A Parallel Adaptive Particle Swarm Optimization Algorithm for Economic/Environmental Power Dispatch

1School of Economic and Management, North China Electric Power University, Beijing 102206, China
2Department of Economic and Management, North China Electric Power University, Baoding 071000, China

Received 15 August 2012; Revised 15 November 2012; Accepted 20 November 2012

Academic Editor: Jung-Fa Tsai

Copyright © 2012 Jinchao Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A parallel adaptive particle swarm optimization algorithm (PAPSO) is proposed for economic/environmental power dispatch, which can overcome the premature characteristic, the slow-speed convergence in the late evolutionary phase, and lacking good direction in particles’ evolutionary process. A search population is randomly divided into several subpopulations. Then for each subpopulation, the optimal solution is searched synchronously using the proposed method, and thus parallel computing is realized. To avoid converging to a local optimum, a crossover operator is introduced to exchange the information among the subpopulations and the diversity of population is sustained simultaneously. Simulation results show that the proposed algorithm can effectively solve the economic/environmental operation problem of hydropower generating units. Performance comparisons show that the solution from the proposed method is better than those from the conventional particle swarm algorithm and other optimization algorithms.