Adaptive Reference Vector Generation for Inverse Model Based Evolutionary Multiobjective Optimization with Degenerate and Disconnected Pareto Fronts

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• 作者：Ran Cheng (16)
  Yaochu Jin (16) (18)
  Kaname Narukawa (17)
  
  16. Department of Computing ; University of Surrey ; Guildford ; Surrey ; GU2 7XH ; UK
  18. College of Information Sciences and Technology ; Donghua University ; Shanghai ; 201620 ; China
  17. Honda Research Institute Europe GmbH ; 63073 ; Offenbach am Main ; Germany
  
• 关键词：Multiobjective optimization ; Model based evolutionary optimization ; Inverse modeling ; Reference vectors
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9018
• 期：1
• 页码：127-140
• 全文大小：305 KB
• 参考文献：1. Bosman, PA, Thierens, D (2003) The balance between proximity and diversity in multiobjective evolutionary algorithms. IEEE Transactions on Evolutionary Computation 7: pp. 174-188 CrossRef
  2. Cheng, R., Jin, Y., Narukawa, K., Sendhoff, B.: A multiobjective evoltuionary algorithm using Gaussian process based inverse modeling. IEEE Transactions on Evolutionary Computation (accepted in 2015)
  3. Cornell, J.A.: Experiments with mixtures: designs, models, and the analysis of mixture data. John Wiley & Sons (2011)
  4. Deb, K, Jain, H (2013) An evolutionary many-objective optimization algorithm using reference-point based non-dominated sorting approach, part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation 18: pp. 577-601 535" target="_blank" title="It opens in new window">CrossRef
  5. Deb, K, Pratap, A, Agarwal, S, Meyarivan, T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6: pp. 182-197 5.996017" target="_blank" title="It opens in new window">CrossRef
  6. Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable multi-objective optimization test problems. In: Proceedings of the Congress on Evolutionary Computation, pp. 825鈥?30. IEEE (2002)
  7. Giagkiozis, I., Fleming, P.J.: Pareto front estimation for decision making. Evolutionary Computation (accepted in 2014)
  8. Jain, H, Deb, K (2013) An evolutionary many-objective optimization algorithm using reference-point based non-dominated sorting approach, part II: Handling constraints and extending to an adaptive approach. IEEE Transactions on Evolutionary Computation 18: pp. 602-622 534" target="_blank" title="It opens in new window">CrossRef
  9. Jin, Y., Sendhoff, B.: Connectedness, regularity and the success of local search in evolutionary multi-objective optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 3, pp. 1910鈥?917. IEEE (2003)
  10. Liu, HL, Gu, F, Zhang, Q (2014) Decomposition of a multiobjective optimization problem into a number of simple multiobjective subproblems. IEEE Transactions on Evolutionary Computation 18: pp. 2450-455 533" target="_blank" title="It opens in new window">CrossRef
  11. Mart铆, L., Garcia, J., Berlanga, A., Coello Coello, C., Molina, J.M.: On current model-building methods for multi-objective estimation of distribution algorithms: Shortcommings and directions for improvement. Department of Informatics, Universidad Carlos III de Madrid, Madrid, Spain, Technical Report GIAA2010E001 (2010)
  12. Okabe, T., Jin, Y., Sendoff, B., Olhofer, M.: Voronoi-based estimation of distribution algorithm for multi-objective optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 2, pp. 1594鈥?601. IEEE (2004)
  13. Rasmussen, C.E.: Gaussian processes for machine learning. MIT Press (2006)
  14. Zhang, Q, Li, H (2007) MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation 11: pp. 712-731 59" target="_blank" title="It opens in new window">CrossRef
  15. Zhang, Q, Zhou, A, Jin, Y (2008) RM-MEDA: A regularity model-based multiobjective estimation of distribution algorithm. IEEE Transactions on Evolutionary Computation 12: pp. 41-63 CrossRef
  16. Zhou, A, Qu, BY, Li, H, Zhao, SZ, Suganthan, PN, Zhang, Q (2011) Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation 1: pp. 32-49 CrossRef
  
• 作者单位：Evolutionary Multi-Criterion Optimization
• 丛书名：978-3-319-15933-1
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Inverse model based multiobjective evolutionary algorithm aims to sample candidate solutions directly in the objective space, which makes it easier to control the diversity of non-dominated solutions in multiobjective optimization. To facilitate the process of inverse modeling, the objective space is partitioned into several subregions by predefining a set of reference vectors. In the previous work, the reference vectors are uniformly distributed in the objective space. Uniformly distributed reference vectors, however, may not be efficient for problems that have nonuniform or disconnected Pareto fronts. To address this issue, an adaptive reference vector generation strategy is proposed in this work. The basic idea of the proposed strategy is to adaptively adjust the reference vectors according to the distribution of the candidate solutions in the objective space. The proposed strategy consists of two phases in the search procedure. In the first phase, the adaptive strategy promotes the population diversity for better exploration, while in the second phase, the strategy focused on convergence for better exploitation. To assess the performance of the proposed strategy, empirical simulations are carried out on two DTLZ benchmark problems, namely, DTLZ5 and DTLZ7, which have a degenerate and a disconnected Pareto front, respectively. Our results show that the proposed adaptive reference vector strategy is promising in tacking multiobjective optimization problems whose Pareto front is disconnected.