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Indicator-Based Weight Adaptation for Solving Many-Objective Optimization Problems

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 11411)

Abstract

Weight adaptation methods can enhance the diversity of solutions obtained by decomposition-based approaches when addressing irregular Pareto front shapes. Generally, these methods adapt the location of each weight vector during the search process. However, early adaptation could be unnecessary and ineffective because the population does not provide a good Pareto front approximation at early generations. In order to improve its performance, a better approach would be to trigger such adaptation only when the population has reached the Pareto front. In this paper, we introduce a performance indicator to assist weight adaptation methods, called the median of dispersion of the population (MDP). The proposed indicator provides a general snapshot of the progress of the population toward the Pareto front by analyzing the local progress of each subproblem. When the population becomes steady according to the proposed indicator, the adaptation of weight vectors starts. We evaluate the performance of the proposed approach in both regular and irregular test problems. Our experimental results show that the proposed approach triggers the weight adaptation when it is needed.

Keywords

  • Weight adaptation
  • Many-objective optimization
  • Decomposition

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Acknowledgment

Auraham Camacho acknowledges support from CONACyT through a scholarship to pursue his studies. Gregorio Toscano and Ricardo Landa gratefully acknowledge support from SEP-Cinvestav project No. 262. Hisao Ishibuchi would like to thank the Shenzhen Peacock Plan (Grant No. KQTD2016112514355531), the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (Grant No. 2017ZT07X386), the Science and Technology Innovation Committee Foundation of Shenzhen (Grant No. ZDSYS201703031748284), and the Program for University Key Laboratory of Guangdong Province (Grant No. 2017KSYS008).

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Camacho, A., Toscano, G., Landa, R., Ishibuchi, H. (2019). Indicator-Based Weight Adaptation for Solving Many-Objective Optimization Problems. In: , et al. Evolutionary Multi-Criterion Optimization. EMO 2019. Lecture Notes in Computer Science(), vol 11411. Springer, Cham. https://doi.org/10.1007/978-3-030-12598-1_18

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  • DOI: https://doi.org/10.1007/978-3-030-12598-1_18

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