A decomposition based multiobjective evolutionary algorithm with self-adaptive mating restriction strategy

MOEA/D decomposes the multiobjective optimization problem into a number of subproblems. However, one subproblem’s requirement for exploitation and exploration varies with the evolutionary process. Furthermore, different subproblems’ requirements for exploitation and exploration are also different as the subproblems have been solved in distinct degree. This paper proposes a decomposition based multiobjective evolutionary algorithm with self-adaptive mating restriction strategy (MOEA/D-MRS). Considering the distinct solved degree of the subproblems, each subproblem has a separate mating restriction probability to control the contributions of exploitation and exploration. Besides, the mating restriction probability is updated by the survival length at each generation to adapt to the changing requirements. The experimental results validate that MOEA/D-MRS performs well on two test suites.

[1]  Xiaodong Li,et al.  A decomposition based memetic algorithm for multi-objective vehicle routing problem with time windows , 2015, Comput. Oper. Res..

[2]  Hui Li,et al.  An improved MOEA/D algorithm for multi-objective multicast routing with network coding , 2017, Appl. Soft Comput..

[3]  Jianming Zhan,et al.  General Forms of (α, β)-Fuzzy Subhypergroups of Hypergroups , 2013, J. Multiple Valued Log. Soft Comput..

[4]  Tao Zhang,et al.  Localized Weighted Sum Method for Many-Objective Optimization , 2018, IEEE Transactions on Evolutionary Computation.

[5]  Hong Li,et al.  MOEA/D + uniform design: A new version of MOEA/D for optimization problems with many objectives , 2013, Comput. Oper. Res..

[6]  Bo Zhang,et al.  Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers , 2016, IEEE Transactions on Evolutionary Computation.

[7]  Qingfu Zhang,et al.  Multiobjective evolutionary algorithm based on decomposition for 3-objective optimization problems with objectives in different scales , 2015, Soft Comput..

[8]  Jianqiang Li,et al.  A novel adaptive control strategy for decomposition-based multiobjective algorithm , 2017, Comput. Oper. Res..

[9]  Qingfu Zhang,et al.  The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances , 2009, 2009 IEEE Congress on Evolutionary Computation.

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

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

[12]  Tao Zhang,et al.  Pareto adaptive penalty-based boundary intersection method for multi-objective optimization , 2017, Inf. Sci..

[13]  Yong Wang,et al.  Scalarizing Functions in Decomposition-Based Multiobjective Evolutionary Algorithms , 2018, IEEE Transactions on Evolutionary Computation.

[14]  Qingfu Zhang,et al.  Decomposition-Based Multiobjective Evolutionary Algorithm With an Ensemble of Neighborhood Sizes , 2012, IEEE Transactions on Evolutionary Computation.

[15]  Qingfu Zhang,et al.  Adaptively Allocating Search Effort in Challenging Many-Objective Optimization Problems , 2018, IEEE Transactions on Evolutionary Computation.

[16]  Qingfu Zhang,et al.  Decomposition-Based-Sorting and Angle-Based-Selection for Evolutionary Multiobjective and Many-Objective Optimization , 2017, IEEE Transactions on Cybernetics.

[17]  Yang Li,et al.  An MOEA/D with multiple differential evolution mutation operators , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[18]  Qingfu Zhang,et al.  MOEA/D-ACO: A Multiobjective Evolutionary Algorithm Using Decomposition and AntColony , 2013, IEEE Transactions on Cybernetics.

[19]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[20]  Shengxiang Yang,et al.  An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts , 2016, IEEE Transactions on Cybernetics.

[21]  R. Lyndon While,et al.  A Scalable Multi-objective Test Problem Toolkit , 2005, EMO.

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

[23]  Xiaodong Li,et al.  Self-adaptive multi-objective evolutionary algorithm based on decomposition for large-scale problems: A case study on reservoir flood control operation , 2016, Inf. Sci..

[24]  Fang Liu,et al.  MOEA/D with opposition-based learning for multiobjective optimization problem , 2014, Neurocomputing.

[25]  Qingfu Zhang,et al.  Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II , 2009, IEEE Transactions on Evolutionary Computation.

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

[27]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[28]  Qingfu Zhang,et al.  Are All the Subproblems Equally Important? Resource Allocation in Decomposition-Based Multiobjective Evolutionary Algorithms , 2016, IEEE Transactions on Evolutionary Computation.

[29]  Yalin Chen,et al.  A modified MOEA/D approach to the solution of multi-objective optimal power flow problem , 2016, Appl. Soft Comput..

[30]  Sandra M. Venske,et al.  ADEMO/D: An adaptive differential evolution for protein structure prediction problem , 2016, Expert Syst. Appl..

[31]  Qingfu Zhang,et al.  Multiobjective differential evolution algorithm based on decomposition for a type of multiobjective bilevel programming problems , 2016, Knowl. Based Syst..

[32]  Fan Lin,et al.  A MOEA/D-based multi-objective optimization algorithm for remote medical , 2017, Neurocomputing.

[33]  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 .

[34]  Qingfu Zhang,et al.  Constrained Subproblems in a Decomposition-Based Multiobjective Evolutionary Algorithm , 2016, IEEE Transactions on Evolutionary Computation.

[35]  Qingfu Zhang,et al.  Matching-Based Selection With Incomplete Lists for Decomposition Multiobjective Optimization , 2016, IEEE Transactions on Evolutionary Computation.

[36]  Jun Zhang,et al.  A Diversity-Enhanced Resource Allocation Strategy for Decomposition-Based Multiobjective Evolutionary Algorithm , 2018, IEEE Transactions on Cybernetics.

[37]  Qingfu Zhang,et al.  On Tchebycheff Decomposition Approaches for Multiobjective Evolutionary Optimization , 2018, IEEE Transactions on Evolutionary Computation.

[38]  Qingfu Zhang,et al.  Adaptive Replacement Strategies for MOEA/D , 2016, IEEE Transactions on Cybernetics.

[39]  Shengxiang Yang,et al.  Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes , 2017, Soft Comput..

[40]  Qingfu Zhang,et al.  Interrelationship-Based Selection for Decomposition Multiobjective Optimization , 2015, IEEE Transactions on Cybernetics.

[41]  Fang Liu,et al.  MOEA/D with Baldwinian learning inspired by the regularity property of continuous multiobjective problem , 2014, Neurocomputing.

[42]  Qingfu Zhang,et al.  Adaptive Operator Selection With Bandits for a Multiobjective Evolutionary Algorithm Based on Decomposition , 2014, IEEE Transactions on Evolutionary Computation.

[43]  Tsung-Che Chiang,et al.  MOEA/D-AMS: Improving MOEA/D by an adaptive mating selection mechanism , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[44]  Hisao Ishibuchi,et al.  Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes , 2017, IEEE Transactions on Evolutionary Computation.

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

[46]  Hiroyuki Sato,et al.  Inverted PBI in MOEA/D and its impact on the search performance on multi and many-objective optimization , 2014, GECCO.

[47]  Jun Zhang,et al.  DECAL: Decomposition-Based Coevolutionary Algorithm for Many-Objective Optimization , 2019, IEEE Transactions on Cybernetics.

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

[49]  Dipti Srinivasan,et al.  Enhanced Multiobjective Evolutionary Algorithm Based on Decomposition for Solving the Unit Commitment Problem , 2015, IEEE Transactions on Industrial Informatics.

[50]  Yiu-Ming Cheung,et al.  Self-Organizing Map-Based Weight Design for Decomposition-Based Many-Objective Evolutionary Algorithm , 2018, IEEE Transactions on Evolutionary Computation.

[51]  Qingfu Zhang,et al.  Hybridization of Decomposition and Local Search for Multiobjective Optimization , 2014, IEEE Transactions on Cybernetics.

[52]  Wali Khan Mashwani,et al.  Multiobjective memetic algorithm based on decomposition , 2014, Appl. Soft Comput..

[53]  Qingfu Zhang,et al.  An External Archive Guided Multiobjective Evolutionary Algorithm Based on Decomposition for Combinatorial Optimization , 2015, IEEE Transactions on Evolutionary Computation.

[54]  Xin Li,et al.  A self-adaptive mating restriction strategy based on survival length for evolutionary multiobjective optimization , 2018, Swarm Evol. Comput..

[55]  Qingfu Zhang,et al.  Biased Multiobjective Optimization and Decomposition Algorithm , 2017, IEEE Transactions on Cybernetics.

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

[57]  Qingfu Zhang,et al.  Learning to Decompose: A Paradigm for Decomposition-Based Multiobjective Optimization , 2019, IEEE Transactions on Evolutionary Computation.

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

[59]  Qiuzhen Lin,et al.  Adaptive composite operator selection and parameter control for multiobjective evolutionary algorithm , 2016, Inf. Sci..

[60]  Fang Liu,et al.  MOEA/D with Adaptive Weight Adjustment , 2014, Evolutionary Computation.

[61]  Qingfu Zhang,et al.  Stable Matching-Based Selection in Evolutionary Multiobjective Optimization , 2014, IEEE Transactions on Evolutionary Computation.

[62]  Xiao Zhi Gao,et al.  Self-organizing multiobjective optimization based on decomposition with neighborhood ensemble , 2016, Neurocomputing.

[63]  Zhao Wang,et al.  A Similarity-Based Multiobjective Evolutionary Algorithm for Deployment Optimization of Near Space Communication System , 2017, IEEE Transactions on Evolutionary Computation.

[64]  Dipti Srinivasan,et al.  A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition , 2017, IEEE Transactions on Evolutionary Computation.

[65]  Cai Dai,et al.  A new decomposition based evolutionary algorithm with uniform designs for many-objective optimization , 2015, Appl. Soft Comput..

[66]  Qingfu Zhang,et al.  On the use of two reference points in decomposition based multiobjective evolutionary algorithms , 2017, Swarm Evol. Comput..