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CSO Technique for Solving the Economic Dispatch Problem Considering the Environmental Constraints

机译：考虑到环境限制的CSO技术解决经济派遣问题

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In this paper, the competitive swarm optimization (CSO) algorithm is applied for handling the economical load dispatch problem. The CSO algorithm is fundamentally encouraged by the particle swarm optimization (PSO) algorithm, but it does not memorize the personal best and global best to update the swarms. Rather in CSO algorithm, a pairwise competitive scenario was presented, where the loser particle is updated from the winner particle and the winner particles are directly accepted to the next population. The algorithm has been performed to find the generations of different units in a plant to reduce the entire fuel price and to maintain the total demand as well as the losses. The experimental study and investigations have revealed better performance for the CSO algorithm than the PSO and numerous state-of-art meta-heuristic algorithms in solving the economical power dispatch problem.
机译：在本文中，竞争群优化（CSO）算法用于处理经济负载调度问题。通过粒子群优化（PSO）算法，CSO算法基本上鼓励，但它不会记住个人最佳和全球最佳更新群体。相反，在CSO算法中，提出了一种成对竞争情景，其中失败者粒子从胜利者粒子更新，并且胜利者粒子直接接受下一个人口。已经进行了该算法，以便在工厂中找到不同单位，以减少整个燃料价格，并保持总需求以及损失。实验研究和研究表明，CSO算法的性能更好地比PSO和众多最先进的元 - 启发式算法解决了解决经济型电力调度问题。

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