• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Applied Soft Computing >Large scale economic dispatch of power systems using oppositional invasive weed optimization
【24h】

Large scale economic dispatch of power systems using oppositional invasive weed optimization

机译：使用对抗性入侵杂草优化的电力系统大规模经济调度

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper presents an evolutionary hybrid algorithm of invasive weed optimization (IWO) merged with oppositional based learning to solve the large scale economic load dispatch (ELD) problems. The oppositional invasive weed optimization (OIWO) is based on the colonizing behavior of weed plants and empowered by quasi opposite numbers. The proposed OIWO methodology has been developed to minimize the total generation cost by satisfying several constraints such as generation limits, load demand, valve point loading effect, multi-fuel options and transmission losses. The proposed algorithm is tested and validated using five different test systems. The most important merit of the proposed methodology is high accuracy and good convergence characteristics and robustness to solve ELD problems. The simulation results of the proposed OIWO algorithm show its applicability and superiority when compared with the results of other tested algorithms such as oppositional real coded chemical reaction, shuffled differential evolution, biogeography based optimization, improved coordinated aggregation based PSO, quantum inspired particle swarm optimization, hybrid quantum mechanics inspired particle swarm optimization, modified shuffled frog leaping algorithm with genetic algorithm, simulated annealing based optimization and estimation of distribution and differential evolution algorithm. (C) 2014 Elsevier B.V. All rights reserved.
机译：本文提出了一种入侵杂草优化（IWO）与基于对立的学习相结合的进化混合算法，以解决大规模的经济负荷分配（ELD）问题。对抗性入侵杂草优化（OIWO）基于杂草植物的定殖行为，并通过准相反的数字进行赋能。通过满足诸如发电限制，负荷需求，阀点负荷效应，多燃料选择和传输损失等几个约束条件，已开发出了提议的OIWO方法，以将总发电成本降至最低。使用五个不同的测试系统对提出的算法进行了测试和验证。所提出的方法的最重要的优点是高精度，良好的收敛特性和解决ELD问题的鲁棒性。与其他测试算法相比，提出的OIWO算法的仿真结果显示出其适用性和优越性，例如对立的实际编码化学反应，混洗的差分进化，基于生物地理的优化，基于改进的协同聚集的PSO，量子启发粒子群优化，混合量子力学启发了粒子群优化，遗传算法改进的改组蛙跳算法，基于模拟退火的优化以及分布和差分进化算法的估计。 （C）2014 Elsevier B.V.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号