A parameterless decomposition-based evolutionary multi-objective algorithm

Multiobjective evolutionary algorithm based on decomposition has made a great contribution to the field of evolutionary multiobjective optimization problem. The decomposition-based algorithms construct a number of scalar optimization subproblems by using a set of weight vectors, and optimize these subproblems simultaneously to approximate the Pareto front (PF). The weight vectors have a massive influence on the performance of the decomposition-based algorithm, especially for the multiobjective optimization problems (MOP) with a complex PF. To solve this, we propose a parameterless decomposition scheme to adjust the weight vectors automatically. Experiment results indicate that the proposed algorithm can obtain better uniformity solutions for the MOP with complex PF.

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

[2]  Carlos A. Coello Coello,et al.  On the Influence of the Number of Objectives on the Hardness of a Multiobjective Optimization Problem , 2011, IEEE Transactions on Evolutionary Computation.

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

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

[5]  Jiang Siwei,et al.  Multiobjective optimization by decomposition with Pareto-adaptive weight vectors , 2011, 2011 Seventh International Conference on Natural Computation.

[6]  Kay Chen Tan,et al.  A multiobjective evolutionary algorithm using dynamic weight design method , 2012 .

[7]  Hui Li,et al.  An Adaptive Evolutionary Multi-Objective Approach Based on Simulated Annealing , 2011, Evolutionary Computation.

[8]  Nicola Beume,et al.  An EMO Algorithm Using the Hypervolume Measure as Selection Criterion , 2005, EMO.

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

[10]  Hisao Ishibuchi,et al.  Behavior of Multiobjective Evolutionary Algorithms on Many-Objective Knapsack Problems , 2015, IEEE Transactions on Evolutionary Computation.

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

[12]  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.

[13]  Hisao Ishibuchi,et al.  Pareto Fronts of Many-Objective Degenerate Test Problems , 2016, IEEE Transactions on Evolutionary Computation.

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

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

[16]  Yuping Wang,et al.  A Multi-Objective Evolutionary Algorithm Using Min-Max Strategy And Sphere Coordinate Transformation , 2009, Intell. Autom. Soft Comput..

[17]  Yiu-ming Cheung,et al.  Objective Extraction for Many-Objective Optimization Problems: Algorithm and Test Problems , 2016, IEEE Transactions on Evolutionary Computation.

[18]  Lothar Thiele,et al.  Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study , 1998, PPSN.

[19]  Qingfu Zhang,et al.  An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition , 2015, IEEE Transactions on Evolutionary Computation.

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

[21]  Kalyanmoy Deb,et al.  An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach , 2014, IEEE Transactions on Evolutionary Computation.

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

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