An enhanced multi-objective evolutionary optimization algorithm with inverse model

Abstract Multi-objective evolutionary algorithm based on the inverse model (IM-MOEA) is a popular method to solve multi-objective optimization problems (MOPs). However, IM-MOEA has some drawbacks such as low accuracy and difficulty in dealing with MOPs with irregular PFs. To address these issues, adaptive reference vector mechanism and nonrandom grouping strategy are employed in IM-MOEA, which enhances the reliability of the inverse model. In addition, a modified selection mechanism is used to choose candidate solutions. Further, an enhanced IM-MOEA with adaptive reference vectors and nonrandom grouping (AN-IMMOEA) is proposed in this paper. The experimental results on 27 MOPs indicate that the proposed method has a better performance than other MOEAs.

[1]  Kalyanmoy Deb,et al.  Omni-optimizer: A Procedure for Single and Multi-objective Optimization , 2005, EMO.

[2]  Mohsen Akbarpour Shirazi,et al.  A bi-objective multi-echelon supply chain model with Pareto optimal points evaluation for perishable products under uncertainty , 2018, Scientia Iranica.

[3]  Jouni Lampinen,et al.  An Empirical Study of Control Parameters for The Third Version of Generalized Differential Evolution (GDE3) , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[4]  David E. Goldberg,et al.  A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[5]  Thomas Stützle,et al.  An Empirical Assessment of the Properties of Inverted Generational Distance on Multi- and Many-Objective Optimization , 2017, EMO.

[6]  Han Liu,et al.  Dynamic reference vectors and biased crossover use for inverse model based evolutionary multi-objective optimization with irregular Pareto fronts , 2018, Applied Intelligence.

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

[8]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[9]  R. Lyndon While,et al.  A review of multiobjective test problems and a scalable test problem toolkit , 2006, IEEE Transactions on Evolutionary Computation.

[10]  Tapabrata Ray,et al.  An Enhanced Decomposition-Based Evolutionary Algorithm With Adaptive Reference Vectors , 2018, IEEE Transactions on Cybernetics.

[11]  Yangyang Li,et al.  Improved RM-MEDA with local learning , 2014, Soft Comput..

[12]  Abbas Ahmadi,et al.  A hybrid hierarchical soft computing approach for the technology selection problem in brick industry considering environmental competencies: A case study. , 2019, Journal of environmental management.

[13]  Qingfu Zhang,et al.  Decomposition of a Multiobjective Optimization Problem Into a Number of Simple Multiobjective Subproblems , 2014, IEEE Transactions on Evolutionary Computation.

[14]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[15]  S. Meysam Mousavi,et al.  Selection of IT outsourcing services' activities considering services cost and risks by designing an interval-valued hesitant fuzzy-decision approach , 2017, J. Intell. Fuzzy Syst..

[16]  Qingfu Zhang,et al.  Approximating the Set of Pareto-Optimal Solutions in Both the Decision and Objective Spaces by an Estimation of Distribution Algorithm , 2009, IEEE Transactions on Evolutionary Computation.

[17]  Marco Laumanns,et al.  Scalable multi-objective optimization test problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[18]  Kalyanmoy Deb,et al.  Multi-objective test problems, linkages, and evolutionary methodologies , 2006, GECCO.

[19]  Junichi Suzuki,et al.  R2-IBEA: R2 indicator based evolutionary algorithm for multiobjective optimization , 2013, 2013 IEEE Congress on Evolutionary Computation.

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

[21]  Amir Nakib,et al.  Image thresholding based on Pareto multiobjective optimization , 2010, Eng. Appl. Artif. Intell..

[22]  Qingfu Zhang,et al.  Multiobjective evolutionary algorithms: A survey of the state of the art , 2011, Swarm Evol. Comput..

[23]  Takayuki Yamada,et al.  Multiobjective optimization using an aggregative gradient-based method , 2015 .

[24]  Mohammad Hossein Fazel Zarandi,et al.  A Mixed Expert Evaluation System and Dynamic Interval-Valued Hesitant Fuzzy Selection Approach , 2016 .

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

[26]  Dirk Thierens,et al.  The Naive MIDEA: A Baseline Multi-objective EA , 2005, EMO.

[27]  Qingfu Zhang,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 1 RM-MEDA: A Regularity Model-Based Multiobjective Estimation of , 2022 .

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

[29]  Ye Tian,et al.  PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum] , 2017, IEEE Computational Intelligence Magazine.

[30]  Kalyanmoy Deb,et al.  Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..

[31]  Bernhard Sendhoff,et al.  A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling , 2015, IEEE Transactions on Evolutionary Computation.

[32]  Xin Yao,et al.  A benchmark test suite for evolutionary many-objective optimization , 2017, Complex & Intelligent Systems.

[33]  Mohsen Akbarpour Shirazi,et al.  An extended intuitionistic fuzzy modified group complex proportional assessment approach , 2018 .

[34]  M. A. Abido,et al.  Multiobjective particle swarm optimization for environmental/economic dispatch problem , 2009 .

[35]  Xin Yao,et al.  Analysis of Computational Time of Simple Estimation of Distribution Algorithms , 2010, IEEE Transactions on Evolutionary Computation.

[36]  Deng Ta.  A Multi-Objective Optimization Method for Energy Management Control of Hybrid Electric Vehicles Using NSGA-II Algorithm , 2015 .

[37]  Kaname Narukawa,et al.  Adaptive Reference Vector Generation for Inverse Model Based Evolutionary Multiobjective Optimization with Degenerate and Disconnected Pareto Fronts , 2015, EMO.

[38]  Qingfu Zhang,et al.  A new learning-based adaptive multi-objective evolutionary algorithm , 2019, Swarm Evol. Comput..

[39]  Hao Wang,et al.  The Set-Based Hypervolume Newton Method for Bi-Objective Optimization , 2020, IEEE Transactions on Cybernetics.