【摘 要】
：
Many real-world optimization problems involve multiple conflicting objectives.Such problems are called multiobjective optimization problems(MOPs).Typically,MOPs have a set of so-called Pareto optimal solutions rather than one unique optimal solution.To as
【机 构】
：
School of Automation,Beijing Institute of Technology,Beijing 100081,China;School of Automation,Beiji
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Many real-world optimization problems involve multiple conflicting objectives.Such problems are called multiobjective optimization problems(MOPs).Typically,MOPs have a set of so-called Pareto optimal solutions rather than one unique optimal solution.To assist the decision maker(DM)in finding his/her most preferred solution,we propose an interactive multiobjective evolutionary algorithm(MOEA)called iDMOEA-εC,which utilizes the DM\'s preferences to compress the objective space directly and pro-gressively for identifying the DM\'s preferred region.The proposed algorithm employs a state-of-the-art decomposition-based MOEA called DMOEA-εC as the search engine to search for solutions.DMOEA-εC decomposes an MOP into a series of scalar constrained subproblems using a set of evenly distributed upper bound vectors to approximate the entire Pareto front.To guide the population toward only the DM\'s pre-ferred part on the Pareto front,an adaptive adjustment mechanism of the upper bound vectors and two-level feasibility rules are proposed and integrated into DMOEA-εC to control the spread of the population.To ease the DM\'s burden,only a small set of representative solutions is presented in each interaction to the DM,who is expected to specify a preferred one from the set.Furthermore,the proposed algorithm includes a two-stage selection procedure,allowing to elicit the DM\'s preferences as accurately as possible.To evaluate the performance of the proposed algorithm,it was compared with other interactive MOEAs in a series of experiments.The experimental results demonstrated the superiority of iDMOEA-εC over its competitors.
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