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Solving non-convex economic load dispatch problem via artificial cooperative search algorithm

机译:通过人工协作搜索算法解决非凸的经济负荷分派问题

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According to no free lunch (NFL) theorem, a metaheuristic optimization method is superior to other meta heuristic optimization methods when it has focused on specific class of optimization problems. Thus, this paper focuses on developing artificial cooperative search (ACS) optimization algorithm to solve economic dispatch (ED) problems more precisely with less complexity than other metaheuristic optimization methods. ACS is a recently developed two population search algorithm based on coevolution process with high probability of finding optimal solution in complex and non-convex optimization problems. This merit is provided by balancing exploration of the problem's search space and exploitation of better results through use of two advanced evolutionary operators and only one control parameter. The constraint handling strategy of the proposed method for solving economic power load dispatch problems is to generate and work with feasible solutions along all the optimization iterations without any mismatch between the total amount of electric power generation and electricity demand plus network transmission loss. Unlike the penalty method, this strategy is unaffected by parameter setting of applied optimization method that complicates its applicability for solving economic power load dispatch problems. The feasibility of ACS for solving ED problem is tested on different lossy non-convex test systems of various sizes and complexities. The practical aspects such as satisfaction of power demand constraint, generation limits of generators and value-point loading effect are undertaken to solve ED problem in medium to relatively large-scale electric power systems. Obtained results confirm the ACS's capability in converging to a better solution highly robust within the reasonable computational time in all independent trials; all these as compared with other optimization algorithms reported in the literature for solving lossy non-convex ED problems. The results are analyzed statistically in terms of solution quality and computational efficiency. The statistical analyses reveal that ACS is a potential method to solve economic power load dispatch problems as it provides higher quality solution in comparison with other optimization algorithms. (C) 2019 Elsevier Ltd. All rights reserved.
机译:根据没有免费的午餐(NFL)定理,在专注于特定的优化问题的情况下,常规优化方法优于其他元启发式优化方法。因此,本文侧重于开发人工协作搜索(ACS)优化算法,以更精确地解决经济调度(ED)问题,这些问题更易于与其他成式优化方法的复杂性更少。 ACS是最近开发的基于协同过程的两种人口搜索算法,具有在复杂和非凸优化问题中找到最佳解决方案的高概率。通过使用两个先进的进化运算符和仅一个控制参数,通过平衡问题的搜索空间和利用更好的结果来提供此优点。解决经济负载调度问题的提出方法的约束处理策略是沿着所有优化迭代的可行解决方案,没有发电和电力需求总量与网络传输损耗之间的任何不匹配。与惩罚方法不同,该策略不受应用优化方法参数设置的影响,使其适用于解决经济动力负荷调度问题。在各种尺寸和复杂性的不同损耗的非凸测试系统上测试了用于解决ED问题的ACS的可行性。诸如充满电力需求约束的实际方面,发电机的产生限制和值点加载效应,以解决媒体到相对大规模电力系统的ED问题。获得的结果证实了ACS在所有独立试验中在合理的计算时间内收敛到更好的解决方案的能力;所有这些与文献中报告的其他优化算法相比,用于解决有损的非凸面问题。结果在溶液质量和计算效率方面进行了统计分析。统计分析表明,与其他优化算法相比,ACS是解决经济功率负荷调度问题的潜在方法。 (c)2019 Elsevier Ltd.保留所有权利。

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