Power Transmission and Transformation Project Establishing and Decision-Making Based on ASU-MOPSO Algorithm

Abstract:

This paper demonstrates an asynchronous-stepwise updated strategy multi-objective particle swarm optimization (ASU-MOPSO) algorithm to improve the convergence and diversity of the multi-objective particle swarm optimization. In the process of the elite reduction, we utilize the asynchronous grid strategy to filter particles since this strategy has lower computing complexity. Meanwhile, the stepwised Euclidean crowding distance strategy is presented to filter particles within every grid, which uses the sum of the Euclidean distance with the two nearest particles to replace of the traditional crowding distance. This strategy can avoid the destruction of distribution diversity. Finally, our algorithm is successfully applied for the actual power transmission and transformation project establishing and decision-making problem. Comparing with traditional MOPSO based on crowding distance strategy and grid strategy, our algorithm can obtain the better solution.

• Periodical: Applied Mechanics and Materials (Volume 521)
• Pages: 521-529
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.521.521
• Online since: February 2014
• Authors: Ke Sun, Kai Xu*, Zhao Ming Zheng, Xiao Yu Ding, Ke Sun, Hong Wei Chen, Quan Yuan Jiang
• Keywords: Asynchronous-Stepwise Update, Elitism Reduction, Multi-Objective Particle Swarm Optimization, Power Transmission and Transformation Project Establishing and Decision-Making

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