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首頁> 外文期刊>Evolutionary computation >An Efficient and Accurate Solution Methodology for Bilevel Multi-Objective Programming Problems Using a Hybrid Evolutionary-Local-Search Algorithm
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An Efficient and Accurate Solution Methodology for Bilevel Multi-Objective Programming Problems Using a Hybrid Evolutionary-Local-Search Algorithm

機譯：使用混合進(jìn)化-局部搜索算法的雙級多目標(biāo)規(guī)劃問題的高效，精確解法

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Bilevel optimization problems involve two optimization tasks (upper and lower level), in which every feasible upper level solution must correspond to an optimal solution to a lower level optimization problem. These problems commonly appear in many practical problem solving tasks including optimal control, process optimization, game-playing strategy developments, transportation problems, and others. However, they are commonly converted into a single level optimization problem by using an approximate solution procedure to replace the lower level optimization task. Although there exist a number of theoretical, numerical, and evolutionary optimization studies involving single-objective bilevel programming problems, not many studies look at the context of multiple conflicting objectives in each level of a bilevel programming problem. In this paper, we address certain intricate issues related to solving multi-objective bilevel programming problems, present challenging test problems, and propose a viable and hybrid evolutionary-cum-local-search based algorithm as a solution methodology. The hybrid approach performs better than a number of existing methodologies and scales well up to 40-variable difficult test problems used in this study. The population sizing and termination criteria are made self-adaptive, so that no additional parameters need to be supplied by the user. The study indicates a clear niche of evolutionary algorithms in solving such difficult problems of practical importance compared to their usual solution by a computationally expensive nested procedure. The study opens up many issues related to multi-objective bilevel programming and hopefully this study will motivate EMO and other researchers to pay more attention to this important and difficult problem solving activity.
機譯：雙層優(yōu)化問題涉及兩個優(yōu)化任務(wù)（上層和下層），其中每個可行的上層解決方案都必須對應(yīng)于下層優(yōu)化問題的最優(yōu)解決方案。這些問題通常出現(xiàn)在許多實際的問題解決任務(wù)中，包括最佳控制，過程優(yōu)化，游戲策略開發(fā)，運輸問題等。但是，它們通常通過使用近似求解程序來替換較低級別的優(yōu)化任務(wù)而轉(zhuǎn)換為單級優(yōu)化問題。盡管存在許多涉及單目標(biāo)雙層編程問題的理論，數(shù)值和進(jìn)化優(yōu)化研究，但很少有研究關(guān)注雙層編程問題中每個級別的多個相互沖突的目標(biāo)。在本文中，我們解決了與解決多目標(biāo)雙層編程問題相關(guān)的某些復(fù)雜問題，提出了具有挑戰(zhàn)性的測試問題，并提出了一種可行且基于混合進(jìn)化和局部搜索的算法作為解決方法?；旌戏椒ǖ男阅軆?yōu)于許多現(xiàn)有方法，并且可以擴(kuò)展到本研究中使用的多達(dá)40個變量的困難測試問題。總體大小和終止標(biāo)準(zhǔn)是自適應(yīng)的，因此用戶不需要提供其他參數(shù)。研究表明，與通常的解決方案相比，進(jìn)化算法在解決此類具有實際重要性的困難問題上具有明顯的優(yōu)勢，而通常的解決方案是通過計算上昂貴的嵌套過程進(jìn)行的。該研究提出了與多目標(biāo)雙層編程有關(guān)的許多問題，希望該研究將激發(fā)EMO和其他研究人員更多地關(guān)注這一重要且困難的問題解決活動。

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