An adaptive reference point technique to improve the quality of decomposition based multi-objective evolutionary algorithm
102 viewsDOI:
https://doi.org/10.54939/1859-1043.j.mst.CSCE7.2023.3-14Keywords:
Evolutionary multi-objective optimization; Balance of exploration and exploitation; Population distribution; Empty region; Adaptive reference point; MOEA/D.Abstract
Applying multi-objective evolutionary optimization algorithms in solving multi-objective optimization problems is a research field that has received attention recently. In the literature of this research field, many studies have been carried out to propose multi-objective evolutionary algorithms or improve published algorithms. However, balancing the exploitation and exploration capabilities of the algorithm during the evolution process is still challenging. This article proposes an approach to solve that equilibrium problem based on analyzing population distribution during the evolutionary process to identify empty regions in which no solutions are selected. After that, information about empty regions with the most significant area will be combined with the current reference point to create a new reference point to prioritize choosing solutions in those regions. Experiments on 10 test problems of 2 typical benchmark sets showed that this mechanism increases the diversity of the population, thereby contributing to a balance between the algorithm's abilities in the evolutionary process and enhancing the algorithm.
References
[1]. H. Wenlan, Zh. Yu, L. Lan, “Survey on multi-objective evolutionary algorithms”, J. of Physics: Conference Series, Conference Series, 1288, 012057, (2019). DOI: https://doi.org/10.1088/1742-6596/1288/1/012057
[2]. K. Deb and J. Sundar, “Reference point based multi-objective optimization using evolutionary algorithms”, Proc. of the 8th annual conference on Genetic and evolutionary computation, pp.635-642, (2006). DOI: https://doi.org/10.1145/1143997.1144112
[3]. D. H. Phan, J. Suzuki, I. Hayashi, “Leveraging IndicatorBased Ensemble Selection in Evolutionary Multiobjective Optimization Algorithms”, Proc. of 2012 Genetic and Evolutionary Computation Conference, pp.497-504, (2012). DOI: https://doi.org/10.1145/2330163.2330234
[4]. R. H. Gomez, C. A. C. Coello, “A Hyper- Heuristic of Scalarizing Functions”, Proc. of 2017 Genetic and Evolutionary Computation Conference, pp.577-584, (2017). DOI: https://doi.org/10.1145/3071178.3071220
[5]. J. G. Falcón-Cardona and C. A. C Coello, “A Multi-Objective Evolutionary Hyper-Heuristic Based on Multiple Indicator Based Density Estimators”, Proc. of 2018 Genetic and Evolutionary Computation Conference, pp.633-640, (2018). DOI: https://doi.org/10.1145/3205455.3205463
[6]. J. G. Falcón-Cardona and C. A. C Coello, “An ensemble indicator-based density estimator for evolutionary multi-objective optimization”, Proc. of International Conference on Parallel Problem Solving from Nature, pp.201-214, (2020). DOI: https://doi.org/10.1007/978-3-030-58115-2_14
[7]. Zh. Qingfu and L. Hui, “MOEA/D: A multiobjective evolutionary algorithm based on decomposition”, J. of Evolutionary Computation, Vol 11, pp. 712-731, (2008). DOI: https://doi.org/10.1109/TEVC.2007.892759
[8]. Q. Xu, Z. Xu and T. Ma, “A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future Directions”, J. of IEEE Access, Vol. 8, pp.41588-41614, (2020). DOI: https://doi.org/10.1109/ACCESS.2020.2973670
[9]. K. Deb, H. Jain, “An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints”, J. of IEEE Transactions on Evolutionary Computation, Vol 18(4), pp.577–601, (2014). DOI: https://doi.org/10.1109/TEVC.2013.2281535
[10]. A. Masood, G. Chen, Y. Mei, M. Zhang, “Adaptive Reference Point Generation for Many-Objective Optimization Using NSGA-III”, Lecture Notes in Computer Science, pp.358-370, (2018). DOI: https://doi.org/10.1007/978-3-030-03991-2_34
[11]. W. Rui, X. Jian, I. Hisao, W. Guohua, Zh. Tao, “On the effect of reference point in MOEA/D for multi-objective optimization”, J. of Applied Soft Computing, pp.25-34, (2017). DOI: https://doi.org/10.1016/j.asoc.2017.04.002
[12]. J. Zou, L. Fu, S. Yang, J. Zheng, G. Ruan, T. Pei, L. Wang, “An adaptation reference-point-based multiobjective evolutionary algorithm”, J. of Information Sciences, Vol 488, pp.41-57, (2019). DOI: https://doi.org/10.1016/j.ins.2019.03.020
[13]. P. V. Pellicer, M. I. Escudero, S. F. Alzueta, K. Deb, “Gap finding and validation in evolutionary multi- and many-objective optimization”, Proc. of the 2020 Genetic and Evolutionary Computation Conference, pp. 578-586, (2020). DOI: https://doi.org/10.1145/3377930.3389835
[14]. T. B. Minh, N. Long, N. D. Dinh, “Using bliss points to enhance direction based multi-objective algorithms”, Proc. of 14th International Conference on Knowledge and Systems Engineering, pp.1-6, (2022). DOI: https://doi.org/10.1109/KSE56063.2022.9953747
[15]. E. Zitzler, K. Deb, L. Thiele, “Comparison of multiobjective evolutionary algorithms: Empirical results”, J. of Evolutionary Computation, Vol 8(2); pp.173-195, (2000).
[16]. Q. Zhang, A. Zhou, S. Zhao, P. N. Suganthan, W. Liu, and S. Tiwari, “Multiobjective optimization test instances for the CEC 2009 specialsession and competition”, University of Essex and NanyangTechnological University, Tech. Rep. CES-487, (2008).
[17]. D.A.V. Veldhuizen, “Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovation”, PhD thesis, Airforce Institue of Technology, Ohio (1999).
[18]. E. Zitzler, L. Thiele, and K. Deb, “Comparision of multiobjective evolutionary algorithms: Emprical results”, J. of Evolutionary Computation, Vol 8(1), pp.173-195, (2000). DOI: https://doi.org/10.1162/106365600568202
