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首页> 外文期刊>International journal of RF and microwave computer-aided engineering >Quadratic approximation–based hybrid Artificial CooperativeSearch algorithm for economic emission load dispatch problems
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Quadratic approximation–based hybrid Artificial CooperativeSearch algorithm for economic emission load dispatch problems

机译：基于二次近似的混合人工合作社搜索经济排放负荷调度问题的算法

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摘要

In this article, Artificial Cooperative Search (ACS) algorithm is incorporated with thequadratic approximation (QA) operator to solve the multi‐objective economic emissionload dispatch (EELD) problems with different generation units. ACS is a SwarmIntelligence–based metaheuristic algorithm, based on the interaction between preyand predator organisms in a habitat, which is effective at global search; however, itdoes not perform so well at exploring promising regions. The QA operator, on theother hand, is a non‐derivative–based efficient local search method that finds the minimumof a quadratic hyperspace passing through three points in a D‐dimensionalspace. Solving the EELD problems with the hybridized ACS‐QA algorithm, as beingproposed in the present article, leads to more accurate results with fewer functionevaluations. Also, multi‐objectivity of the problem is handled by transforming it intoa single‐objective problem by using the weighted sum method. The efficiency of theproposed ACS‐QA algorithm is tested in comparison to the algorithms existing in literatureby implementing it on six different benchmark optimization problems. Afterward,the proposed ACS‐QA algorithm and the ACS algorithm are implemented onmulti‐objective EELD problems with different generation units. The results are comparedwith the solutions in literature utilizing different metaheuristic optimizationalgorithms. Both studies firmly showed that the ACS‐QA algorithm is able to findmore accurate results even though it uses fewer function evaluation calls.
机译：在本文中，人工协作搜索（ACS）算法包含在内二次近似（QA）操作员解决多目标经济排放加载不同一代单位的调度（EELD）问题。 ACS是一个群体基于智力的成群质算法，基于猎物之间的相互作用栖息地的捕食者生物，在全球搜索有效;但是，它在探索有前途的地区时不会表现得如此。 QA运营商，在另一只手是一种基于非衍生的有效的本地搜索方法，找到最小值在D维中通过三个点的二次高度空间。用杂交的ACS-QA算法解决ELED问题，如在本文中提出，导致更准确的结果与较少的功能评估。此外，问题的多象性通过将其转换为来处理使用加权和方法的单个客观问题。效率与文献中存在的算法相比，测试了所提出的ACS-QA算法通过在六种不同的基准优化问题上实施它。之后，所提出的ACS-QA算法和ACS算法是在开启的不同一代单位的多目标EELD问题。结果进行了比较利用不同型血管型优化的文献解决方案算法。这两项研究都牢固地表明ACS-QA算法能够找到即使它使用较少的函数评估呼叫，结果也是更准确的结果。

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