KKT Proximity Measure Versus Augmented Achievement Scalarization Function

KKT proximity measure (KKTPM) is use as metric for obtained how we are close to the from a corresponding Pareto-optimal (PO) point without any knowledge about the true optimum point. This metric use one such common a scalarization method that also guarantees to find any PO solution that is achievement scalarizing function (ASF) method. Since that KKTPM formulation is based on augmented achievement scalarizing function (AASF) to avoid weak PO solutions. This paper studies a relation between KKTPM values and AASF values. Aim of this study to know the advantage and disadvantage of both measures. Also, this paper discusses some special cases to know the merits of both measures and to confirm that KKT proximity measure is an essential measure for convergence. In addition, this study investigates the correlation plot between these two measures for ZDT test problems, results show the difference in values and therefore cannot obtain a perfect correlation between KKTPM values and AASF values. Hence, it can be said that KKT proximity measure is better.

[1]  Kalyanmoy Deb,et al.  Investigating EA solutions for approximate KKT conditions in smooth problems , 2010, GECCO '10.

[2]  Jesús García,et al.  An approach to stopping criteria for multi-objective optimization evolutionary algorithms: The MGBM criterion , 2009, 2009 IEEE Congress on Evolutionary Computation.

[3]  Heike Trautmann,et al.  A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms , 2011, EMO.

[4]  Kalyanmoy Deb,et al.  Faster Hypervolume-Based Search Using Monte Carlo Sampling , 2008, MCDM.

[5]  José Mario Martínez,et al.  A New Sequential Optimality Condition for Constrained Optimization and Algorithmic Consequences , 2010, SIAM J. Optim..

[6]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[7]  Kalyanmoy Deb,et al.  A Computationally Fast Convergence Measure and Implementation for Single-, Multiple-, and Many-Objective Optimization , 2017, IEEE Transactions on Emerging Topics in Computational Intelligence.

[8]  R. Lyndon While,et al.  A faster algorithm for calculating hypervolume , 2006, IEEE Transactions on Evolutionary Computation.

[9]  Andrzej P. Wierzbicki,et al.  The Use of Reference Objectives in Multiobjective Optimization , 1979 .

[10]  J. B. G. Frenk,et al.  An Elementary Proof of the Fritz-John and Karush-Kuhn-Tucker Conditions in Nonlinear Programming , 2005, Eur. J. Oper. Res..

[11]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[12]  Kalyanmoy Deb,et al.  An Optimality Theory-Based Proximity Measure for Set-Based Multiobjective Optimization , 2016, IEEE Transactions on Evolutionary Computation.

[13]  Gabriel Haeser,et al.  On Approximate KKT Condition and its Extension to Continuous Variational Inequalities , 2011, J. Optim. Theory Appl..

[14]  丸山 徹 Convex Analysisの二,三の進展について , 1977 .

[15]  Kalyanmoy Deb,et al.  Towards faster convergence of evolutionary multi-criterion optimization algorithms using Karush Kuhn Tucker optimality based local search , 2017, Comput. Oper. Res..

[16]  Hussein A. Abbass,et al.  A dominance-based stability measure for multi-objective evolutionary algorithms , 2009, 2009 IEEE Congress on Evolutionary Computation.

[17]  A. Banerjee Convex Analysis and Optimization , 2006 .

[18]  Kalyanmoy Deb,et al.  Approximate KKT points and a proximity measure for termination , 2013, J. Glob. Optim..

[19]  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.

[20]  Nicola Beume,et al.  Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods Gradient-based / Evolutionary Relay Hybrid for Computing Pareto Front Approximations Maximizing the S-Metric , 2007 .

[21]  Kaisa Miettinen,et al.  A new achievement scalarizing function based on parameterization in multiobjective optimization , 2010, OR Spectrum.

[22]  K. Schittkowski,et al.  NONLINEAR PROGRAMMING , 2022 .

[23]  A. Ravindran,et al.  Engineering Optimization: Methods and Applications , 2006 .