本文關(guān)鍵詞：進(jìn)化算法解決多目標(biāo)優(yōu)化問(wèn)題　出處：《中原工學(xué)院》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 粒子群算法 多目標(biāo)優(yōu)化 差分進(jìn)化算法 環(huán)境經(jīng)濟(jì)調(diào)度

【摘要】：進(jìn)化算法(Evolutionary Algorithm,EA)在處理多目標(biāo)優(yōu)化問(wèn)題(Multi-objective optimization problem)和在實(shí)際中應(yīng)用是當(dāng)前研究的熱門問(wèn)題,隨著待優(yōu)化問(wèn)題的維度越來(lái)越高、目標(biāo)變得更多,算法也隨之變得復(fù)雜,傳統(tǒng)的粒子群、差分進(jìn)化等算法已不能有效處理此類問(wèn)題。多目標(biāo)進(jìn)化算法(Multi-objective Evolutionary Algorithm,MOEA)隨之出現(xiàn)。它在處理上述問(wèn)題中表現(xiàn)杰出。本文在傳統(tǒng)優(yōu)化算法的基礎(chǔ)上,重點(diǎn)研究介紹了多目標(biāo)粒子群算法(MOPSO,Multi-Objective Particle Swarm optimization algorithm)來(lái)處理多目標(biāo)優(yōu)化問(wèn)題。將差分進(jìn)化算法(Differential evolution algorithm,DE)在解決環(huán)境經(jīng)濟(jì)調(diào)度(Environment/Economic Dispatch,EED)中應(yīng)用。具體內(nèi)容如下:首先介紹了粒子群算法(PSO)的概念和多種建立在PSO基礎(chǔ)上的改進(jìn)算法。將它們?cè)跍y(cè)試函數(shù)中的表現(xiàn)進(jìn)行對(duì)比并分析。對(duì)深入研究MOPSO算法提供了理論基礎(chǔ)。其次利用PSO算法的先驗(yàn)知識(shí)。針對(duì)傳統(tǒng)PSO算法不能處理多目標(biāo)問(wèn)題。提出了加入外部存檔和局部擾動(dòng)策略的MOPSO算法。從實(shí)驗(yàn)結(jié)果看出,本文介紹的MOPSO比之NSGA2可得到更好的Pareto前沿。之后詳細(xì)介紹DE算法的基本概念。列舉多種擴(kuò)展模式并比較。同時(shí)介紹多種改進(jìn)的DE算法,并在測(cè)試函數(shù)中測(cè)試比較。本章對(duì)后期研究DE算法在電力系統(tǒng)EED應(yīng)用打下理論基礎(chǔ)。最后介紹電力系統(tǒng)EED問(wèn)題。此問(wèn)題是多目標(biāo)、多約束并且是高維的優(yōu)化問(wèn)題,傳統(tǒng)算法沒有辦法處理。為此,利用DE算法的先驗(yàn)知識(shí),用啟發(fā)式策略解決多個(gè)約束條件并加入優(yōu)先列表方法。實(shí)驗(yàn)結(jié)果表明。本文的算法能使耗能低的發(fā)電機(jī)擁有更高優(yōu)先級(jí)去更多的輸出電力。從而得到更好的解決方案。
[Abstract]:Evolutionary algorithms (Evolutionary Algorithm, EA) in dealing with multi-objective optimization problems (Multi-objective optimization problem) and the application in practice is a hot issue of the current study, with more and more dimensions for the optimization problem, the goal becomes more, algorithm becomes complicated, the traditional particle swarm optimization, differential evolution algorithm has not effectively deal with such problems. The multi-objective evolutionary algorithm (Multi-objective Evolutionary Algorithm, MOEA) appears. Its performance in dealing with these issues outstanding. Based on the traditional optimization algorithm, the key research introduces multi-objective particle swarm algorithm (MOPSO, Multi-Objective Particle Swarm optimization algorithm) to solve multi-objective optimization problem. The difference the genetic algorithm (Differential evolution algorithm, DE) in solving the environmental economic dispatch (Environment/ Economic Dispatch, EED) in the application. The specific contents are as follows: firstly introduces the particle swarm optimization (PSO) algorithm and the concept of variety based on PSO. Their performance in the test function were compared and analyzed. Provide a theoretical basis for further study of the MOPSO algorithm. Then PSO algorithm using prior knowledge. According to the traditional PSO algorithm cannot handle the multi-objective problem is proposed. The addition of external archive and local perturbation strategy of MOPSO algorithm. The experimental results show that the MOPSO NSGA2 can be better than Pareto. After the detailed introduction to the basic concept of advanced DE algorithm. A list expansion mode and compared. At the same time, a variety of improved DE algorithm is introduced, and the comparison test in the test function. This chapter of the late DE algorithm to lay a theoretical foundation in power system EED power system EED application. At the end of the paper. This problem is a multi-objective, multi constraint and high dimension The optimization problem, the traditional algorithm can not deal with. Therefore, DE algorithm using the prior knowledge, the heuristic strategy to solve the multiple constraints and to priority list method. Experimental results show that this algorithm can make. The generator has low energy consumption of higher priority to more output power. In order to get a better solution.

【學(xué)位授予單位】：中原工學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18

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