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CHAOTIC INITIALIZED MULTIPLE OBJECTIVE DIFFERENTIAL EVOLUTION WITH ADAPTIVE MUTATION STRATEGY (CA-MODE) FOR CONSTRUCTION PROJECT TIME-COST-QUALITY TRADE-OFF

机译:随适度突变策略(CA-Mode)的混沌初始化多目标差分演进,用于建设项目时间 - 质量 - 质量折衷

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摘要

Time, cost and quality are three factors playing an important role in the planning and controlling of construc­tion. Trade-off optimization among them is significant for the improvement of the overall benefits of construction pro­jects. In this paper, a novel optimization model, named as Chaotic Initialized Multiple Objective Differential Evolution with Adaptive Mutation Strategy (CA-MODE), is developed to deal with the time-cost-quality trade-off problems. The proposed algorithm utilizes the advantages of chaos sequences for generating an initial population and an external elitist archive to store non-dominated solutions found during the evolutionary process. In order to maintain the exploration and exploitation capabilities during various phases of optimization process, an adaptive mutation operation is introduced. A numerical case study of highway construction is used to illustrate the application of CA-MODE. It has been shown that non-dominated solutions generated by CA-MODE assist project managers in choosing appropriate plan which is other­wise hard and time-consuming to obtain. The comparisons with non-dominated sorting genetic algorithm (NSGA-II), multiple objective particle swarm optimization (MOPSO), multiple objective differential evolution (MODE) and previ­ous results verify the efficiency and effectiveness of the proposed algorithm.First published online: 24 Aug 2015
机译:时间,成本和质量是三个因素在规划和控制建设中发挥着重要作用。在改善建筑项目的整体效益方面,它们之间的权衡优化很重要。本文采用了一种新的优化模型,作为混沌初始化多目标差分演进,采用自适应突变策略(CA-Mode),以处理时间成本质量的权衡问题。该算法利用混沌序列的优点来产生初始群体和外部精英档案,以存储在进化过程中发现的非主导解决方案。为了在优化过程的各个阶段保持勘探和利用能力,引入了自适应突变操作。公路施工的数值研究用于说明CA-MODE的应用。已经表明,CA-Mode辅助项目经理生成的非主导解决方案在选择适当的计划时,这些计划是恰当的,耗时耗时。具有非主导分类遗传算法(NSGA-II),多目标粒子群优化(MOPSO),多目标差分演进(模式)和先前结果的比较验证了所提出的算法的效率和有效性。首次发布在线:2015年8月24日

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