A Method for a Posteriori Identification of Knee Points Based on Solution Density

Many evolutionary algorithms have been proposed and demonstrated to have excellent performance in striking a balance between convergence and diversity in dealing with multiobjective optimization problems. However, little attention has been paid to the decision making stage where a small number of solutions are selected to be presented to the user. It is believed that knee points are considered to be the naturally preferred solutions when no specific preferences are available, because knee solutions incur a large loss in at least one objective to gain a small amount in other objectives. One common issue in the identification of knee points is that some knee points are easily ignored and knees in concave regions are hard to be identified. To resolve these issues, this paper proposes a novel method for knee identification, which first maps the non-dominated solutions to a constructed hyperplane and then divides them into groups, each representing a candidate knee region, based on the density of the solutions projected on the hyperplane. Finally, the convexity and curvature of the candidate knee groups are determined and only those having a strong curvature are kept. The proposed method is empirically demonstrated to be effective in identifying knee points located in both convex and concave regions on three existing test problems and one newly proposed test problem.

[1]  Lily Rachmawati,et al.  Preference Incorporation in Multi-objective Evolutionary Algorithms: A Survey , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[2]  Eckart Zitzler,et al.  Indicator-Based Selection in Multiobjective Search , 2004, PPSN.

[3]  Monark Bag,et al.  A review of multi-criteria decision making techniques for supplier evaluation and selection , 2011 .

[4]  K. Deb,et al.  Understanding knee points in bicriteria problems and their implications as preferred solution principles , 2011 .

[5]  Kalyanmoy Deb,et al.  An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints , 2014, IEEE Transactions on Evolutionary Computation.

[6]  Marco Laumanns,et al.  Approximating the Knee of an MOP with Stochastic Search Algorithms , 2008, PPSN.

[7]  Antonio J. Nebro,et al.  jMetal: A Java framework for multi-objective optimization , 2011, Adv. Eng. Softw..

[8]  Bernhard Sendhoff,et al.  A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization , 2016, IEEE Transactions on Evolutionary Computation.

[9]  Eckart Zitzler,et al.  HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization , 2011, Evolutionary Computation.

[10]  Kalyanmoy Deb,et al.  Finding Knees in Multi-objective Optimization , 2004, PPSN.

[11]  Peter J. Fleming,et al.  Preference-Inspired Coevolutionary Algorithms for Many-Objective Optimization , 2013, IEEE Transactions on Evolutionary Computation.

[12]  Ye Tian,et al.  A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization , 2015, IEEE Transactions on Evolutionary Computation.

[13]  Khaled Ghédira,et al.  Searching for knee regions of the Pareto front using mobile reference points , 2011, Soft Comput..

[14]  Markus Olhofer,et al.  A mini-review on preference modeling and articulation in multi-objective optimization: current status and challenges , 2017, Complex & Intelligent Systems.

[15]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[16]  Hartmut Schmeck,et al.  Angle-Based Preference Models in Multi-objective Optimization , 2017, EMO.

[17]  Tapabrata Ray,et al.  Bridging the Gap: Many-Objective Optimization and Informed Decision-Making , 2017, IEEE Transactions on Evolutionary Computation.

[18]  Nicola Beume,et al.  SMS-EMOA: Multiobjective selection based on dominated hypervolume , 2007, Eur. J. Oper. Res..

[19]  Jinhua Zheng,et al.  Decomposing the user-preference in multiobjective optimization , 2016, Soft Comput..

[20]  Jinhua Zheng,et al.  Binary search based boundary elimination selection in many-objective evolutionary optimization , 2017, Appl. Soft Comput..

[21]  Marco Laumanns,et al.  Combining Convergence and Diversity in Evolutionary Multiobjective Optimization , 2002, Evolutionary Computation.

[22]  Gary G. Yen,et al.  Visualization and Performance Metric in Many-Objective Optimization , 2016, IEEE Transactions on Evolutionary Computation.

[23]  Xiaodong Li,et al.  On decomposition methods in interactive user-preference based optimization , 2017, Appl. Soft Comput..

[24]  Matthias Ehrgott,et al.  Multiple criteria decision analysis: state of the art surveys , 2005 .

[25]  E. Hughes Multiple single objective Pareto sampling , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[26]  Indraneel Das On characterizing the “knee” of the Pareto curve based on Normal-Boundary Intersection , 1999 .

[27]  Hartmut Schmeck,et al.  Preference Ranking Schemes in Multi-Objective Evolutionary Algorithms , 2011, EMO.

[28]  Marco Laumanns,et al.  SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization , 2002 .

[29]  Lily Rachmawati,et al.  Multiobjective Evolutionary Algorithm With Controllable Focus on the Knees of the Pareto Front , 2009, IEEE Transactions on Evolutionary Computation.

[30]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[31]  Kalyanmoy Deb,et al.  An Interactive Evolutionary Multi-objective Optimization Method Based on Polyhedral Cones , 2010, LION.