A Dynamic Guided Multi-objective Optimization Strategy Based on Preference Information
ZHENG Jin-Hua,JIA Yue
Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education,College of Information Engineering,Xiangtan University,Xiangtan 411105
Abstract：The focus of the traditional multi-objective evolutionary algorithms is to obtain the optimal solution set distributed in the entire Pareto frontier. However,in reality problems,the decision makers are merely interested in certain regions of the Pareto frontier. Therefore,it is significant to take the preference information of decision-makers into multi-objective evolutionary algorithms. Thus,how to reduce computing resource and obtain Pareto optimal set effectively in preference regions becomes a hot topic in the research. Aiming at the problem,a dynamic heuristic multi-objective optimization strategy is proposed based on the preference information. The parameter ε is adjusted to reflect the dynamics of the guided regions,and another parameter is set to control the size of preference range of DM. The strategy employs the distance between solution set and the guided regions as a factor for selection strategy. The experimental results show the proposed algorithm with this strategy has a good performance especially on the convergence.
[1] Deb K. Multi-Objective Optimization Using Evolutionary Algorithms. Chichester,UK: John Wiley & Sons,2001 [2] Deb K,Pratap A,Agarwal S,et al. A Fast and Elitist Multi-objective Genetic Algorithm: NSGA-II. IEEE Trans on Evolutionary Computation,2002,6(2): 182-197 [3] Zitzler E,Laumanns M,Thiele L. SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report,TIK-Report 103. Lausanne,Switzerland: Swiss Federal Institute of Technology,2001 [4] Knowles J D,Corne D W. Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation,2000,8(2): 149-172 [5] Davarynejad M,Vrancken J,Van den Berg J,et al. A Fitness Granulation Approach for Large-Scale Structural Design Optimization // Chiong R,Weise T,Michalewicz Z,eds. Variants of Evolutionary Algorithms for Real-World Applications. Berlin,Germany: Springer-Verlag,2012 [6] Li M Q,Zheng J H. An Indicator for Assessing the Spread of Solutions in Multi-objective Evolutionary Algorithms. Chinese Journal of Computers,2011,34(4): 647-664 (in Chinese) (李密青,郑金华.一种多目标进化算法解集分布广度评价方法.计算机学报,2011,34(4): 647-664) [7] Sindhya K,Deb K,Miettinen K. Improving Convergence of Evolutionary Multi-objective Optimization with Local Search: A Concurrent-Hybrid Algorithm. Natural Computing,2011,10(4): 1407-1430 [8] Cui X X,Lin C. A Preference-Based Multi-objective Concordance Genetic Algorithm. Journal of Software,2005,16(5): 761-770 (in Chinese) (崔逊学,林 闯.一种基于偏好的多目标调和遗传算法.软件学报,2005,16(5): 761-770) [9] Deb K,Sundar J,Rao N U B,et al. Reference Point Based Multi-objective Optimization Using Evolutionary Algorithms. International Journal of Computational Intelligence Research,2006,2(3): 273-286 [10] Fonseca C M,Fleming P J. Multi-objective Genetic Algorithms Made Easy: Selection Sharing and Mating Restriction // Proc of the 1st IEEE International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications. Sheffield,UK,1995: 42-52 [11] Cvetkovic D,Parmee I C. Genetic Algorithm Based Multi-objective Optimization and Conceptual Engineering Design // Proc of the Congress on Evolutionary Computation. Washington,USA,1999, I: 29-36 [12] Cvetkovic D,Parmee I C. Preferences and Their Application in Evolutionary Multi-objective Optimization. IEEE Trans on Evolutionary Computation,2002,6(1): 42-57 [13] Molina J,Santana L V,Coello C A C,et al. G-Dominance: Reference Point Based Dominance for Multi-objective Metaheuristics. European Journal of Operational Research,2009,197(2): 685-692 [14] Bui L T,Barlow M,Abbass H A. A Multi-objective Risk-Based Framework for Mission Capability Planning. New Mathematics and Natural Computation,2009,5(2): 459-485 [15] Coello C,Carlos A. Handling Preferences in Evolutionary Multi-objective Optimization: A Survey // Proc of the Congress on Evolutionary Computation. La Jolla,USA,2000,I: 30-37 [16] Zheng J H. Multi-objective Evolutionary Algorithms and Applications. Beijing,China: Science Press,2007 (in Chinese) (郑金华.多目标进化算法及其应用.北京:科学出版社,2007) [17] Phelps S,Koksalan M. An Interactive Evolutionary Metaheuristic for Multi-objective Combinatorial Optimization. Management Science,2003,49(12): 1726-1738 [18] Deb K,Kumar A. Interactive Evolutionary Multi-objective Optimization and Decision-Making Using Reference Direction Method // Proc of the 9th Genetic and Evolutionary Computation Conference. London,UK,2007: 781-788 [19] Hakanen J,Kawajiri Y,Miettinen K,et al. Interactive Multi-objective Optimization for Simulated Moving Bed Processes. Control and Cybernetics,2007,36(2): 283-302 [20] Thiele L,Miettinen K,Korhonen P J,et al. A Preference-Based Evolutionary Algorithm for Multi-objective Optimization. Evolutionary Computation,2009,17(3): 411-436 [21] Chaudhuri S,Deb K. An Interactive Evolutionary Multi-objective Optimization and Decision Making Procedure. Applied Soft Computing,2010,10(2): 496-511 [22] Soylu B,Ulusoy S K. A Preference Ordered Classification for a Multi-objective Max-Min Redundancy Allocation Problem. Computers and Operations Research,2011,38(12): 1855-1866 [23] Yu J,He Z Y,Qian Q Q. Study on Multi-objective Particle Swarm Optimization Algorithm Based on Preference.Control and Decision,2009,24(1): 66-70 (in Chinese) (余 进,何正友,钱清泉.基于偏好信息的多目标微粒群优化算法研究.控制与决策,2009,24(1): 66-70) [24] Jaszkiewicz A,Slowinski R. The Light Beam Search Approach: An Overview of Methodology and Applications. European Journal of Operation Research,1999,113(2): 300-314 [25] Deb K,Kumar A. Light Beam Search Based Multi-objective Optimization Using Evolutionary Algorithms // Proc of the IEEE Congress on Evolutionary Computation. Singapore,Singapore,2007: 2125-2132 [26] Zitzler E,Deb K,Thiele L. Comparison of Multi-objective Evolutionary Algorithms: Empirical Results. Evolutionary Computation,2000,8(2): 173-195 [27] Deb K,Thiele L,Laumanns M,et al. Scalable Multi-objective Optimization Test Problems // Proc of the Congress on Evolutionary Computation. Honolulu,USA,2002,I: 825-830 [28] Van Veldhuizen D A,Lamont G B. Evolutionary Computation and Convergence to a Pareto Front // Proc of the Late Breaking Papers at the Genetic Programming Conference. Madison,USA,1998: 221-228
2014, Vol. 27 
