Cultural quantum-behaved particle swarm optimization for environmental/economic dispatch
Cultural quantum-behaved particle swarm optimization for environmental/economic dispatch
复制标题
DOI：
10.1016/j.asoc.2016.04.021
复制
发表时间：
2016-11
期刊：
影响因子：
--
通讯作者：
Tianyu Liu;L. Jiao;Wenping Ma;Jingjing Ma;Ronghua Shang
中科院分区：
文献类型：
--
作者：
Tianyu Liu;L. Jiao;Wenping Ma;Jingjing Ma;Ronghua Shang
将文化进化机制引入量子行为粒子群优化算法（QPSO），提出了一种新的CMOQPSO算法来求解多目标环境/经济调度（EED）问题。QPSO处理多目标优化问题的能力越来越受到关注。QPSO扩展到多目标环境中的两个重要问题是为每个粒子构造样本位置和保持种群多样性。在CMOQPSO算法中，为了提高算法的全局搜索能力，在每次迭代中对一个粒子进行多次测量。该方法采用基于文化进化机制的信念空间，包含从粒子群中提取的不同类型的知识，为每个粒子的多个测量值生成全局最优位置。此外，为了保持种群多样性和避免早熟，本文提出了一种新的局部搜索算子，它是基于信念空间中的知识。CMOQPSO与几种最先进的算法进行了比较，并分别在6台和40台发电机的电火花系统上进行了测试。比较结果表明了该算法的有效性。
In this paper, a novel CMOQPSO algorithm is proposed, in which cultural evolution mechanism is introduced into quantum-behaved particle swarm optimization (QPSO) to solve multiobjective environmental/economic dispatch (EED) problems. There are growing concerns about the ability of QPSO to handle multiobjective optimization problems. Two important issues in extending QPSO to multiobjective context are the construction of exemplar positions for each particle and the maintenance of population diversity. In the proposed CMOQPSO, one particle is measured for multiple times at each iteration in order to enhance its global searching ability. Belief space, which is based on cultural evolution mechanism and contains different types of knowledge extracted from the particle swarm, is adopted to generate global best positions for the multiple measurements of each particle. Moreover, to maintain population diversity and avoid premature, a novel local search operator, which is based on the knowledge in belief space, is proposed in this paper. CMOQPSO is compared with several state-of-art algorithms and tested on EED systems with 6 and 40 generators respectively. The comparative results demonstrate the effectiveness of the proposed algorithm.