Techniques for Highly Multiobjective Optimisation: Some Nondominated Points are Better than Others
David Corne, Joshua Knowles
备注：
8 pages, 2 tables
发表信息：
in Hod Lipson (Ed.): Genetic and Evolutionary Computation Conference, GECCO 2007, Proceedings, London, England, UK, July 7-11, 2007. ACM 2007, pp. 773--780, ISBN 978-1-59593-697-4
The research area of evolutionary multiobjective optimization (EMO) is reaching better understandings of the properties and capabilities of EMO algorithms, and accumulating much evidence of their worth in practical scenarios. An urgent emerging issue is that the favoured EMO algorithms scale poorly when problems have many (e.g. five or more) objectives. One of the chief reasons for this is believed to be that, in many-objective EMO search, populations are likely to be largely composed of nondominated solutions. In turn, this means that the commonly-used algorithms cannot distinguish between these for selective purposes. However, there are methods that can be used validly to rank points in a nondominated set, and may therefore usefully underpin selection in EMO search. Here we discuss and compare several such methods. Our main finding is that simple variants of the often-overlooked Average Ranking strategy usually outperform other methods tested, covering problems with 5-20 objectives and differing amounts of inter-objective correlation.
RDEx-CMOP: Feasibility-Aware Indicator-Guided Differential Evolution for Fixed-Budget Constrained Multiobjective Optimization
University of Toyama（富山大学）；Tohoku University（东北大学）；Hirosaki University（弘前大学）；Tongji University（同济大学）；University of Electronic Science and Technology of China（电子科技大学）；Chongqing Polytechnic University of Electronic Technology（重庆电子科技职业大学）；Yantai Vocational College（烟台职业学院）
Evolutionary Biparty Multiobjective UAV Path Planning: Problems and Empirical Comparisons
Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen（广东省新型安全智能技术重点实验室，哈尔滨工业大学计算机科学与技术学院，深圳）；Peng Cheng Laboratory（鹏城实验室）；School of Artificial Intelligence/School of Future Technology, Nanjing University of Information Science and Technology（人工智能学院/未来技术学院，南京信息科学技术大学）
RDEx-MOP: Indicator-Guided Reconstructed Differential Evolution for Fixed-Budget Multiobjective Optimization
Department of Engineering, University of Toyama（富山大学工学部）；Cyberscience Center, Tohoku University（东北大学网络科学中心）；Faculty of Science and Technology, Hirosaki University（弘前大学理工学部）；Sino-German College of Applied Sciences, Tongji University（同济大学中德工程学院）；School of Mechanical Engineering, Tongji University（同济大学机械工程学院）；Department of Information Engineering, Yantai Vocational College（烟台职业学院信息工程系）
