Yan Xiaofan


Decomposition-based multi-objective evolutionary algorithm can obtain effective solution sets for solving multi-objective optimization problems. However, the random way of selecting individuals is not conducive to the preservation of good solutions and reduces the convergence speed of the algorithm. In order to solve this problem, a multi-objective optimization decomposition algorithm based on differentiated selection strategy (MOEA/D-DFS) is proposed. The differentiated selection strategy adopted by the algorithm is to select the non-dominant solution from population, using the method of individual selection in NSGA-II. Meanwhile, the strategy of first substitution and stopping is adopted when the offspring replace parent. It is conducive to enhancing the diversity of the population. The experimental results of ZDT test function shows that the algorithm is superior to other algorithm in convergence and diversity, and has certain advantages in solving performance.


decomposition-based; multi-objective; genetic operator; selection strategy

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Trindade B C, Reed P M, Herman J D, et al. Reducing regional drought vulnerabilities and multi-city robustness conflicts using many-objective optimization under deep uncertainty[J]. Advances in Water Resources, 2017, 104:195-209.

Ringkamp M, Ober-Bl?Baum S, Dellnitz M, et al. Handling high-dimensional problems with multi-objective continuation methods via successive approximation of the tangent space[J]. Engineering Optimization, 2012, 44(9):1117-1146.

Fleming P J, Purshouse R C, Lygoe R J. Many-Objective Optimization: An Engineering Design Perspective[J]. 2005.

Goulart F, Campelo F. Preference-guided evolutionary algorithms for many-objective optimization[J]. Information Sciences, 2016, 329:236-255.

Kim M, Hiroyasu T, Miki M, et al. SPEA2+: Improving the Performance of the Strength Pareto Evolutionary Algorithm 2.[J]. Parallel Problem Solving from Nature-PPSN VIII, 2004, 3242(4):742-751.

Sheng W, Liu Y, Meng X, et al. An Improved Strength Pareto Evolutionary Algorithm 2 with application to the optimization of distributed generations[J]. Computers & Mathematics with Applications, 2012, 64(5).

Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2):0-197.

Deb K, Jain H. An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints[J]. IEEE Transactions on Evolutionary Computation, 2014, 18(4):577-601.

Yao X , Burke E K , Lozano, José A, et al. [Lecture Notes in Computer Science] Parallel Problem Solving from Nature - PPSN VIII Volume 3242 || Indicator-Based Selection in Multiobjective Search[C]// 2004:832-842.

Bader J, Zitzler E. HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization[J]. EVOLUTIONARY COMPUTATION, 2011, 19(1):45-76.

Suzuki J, Phan D H. R2-IBEA: R2 Indicator Based Evolutionary Algorithm for Multiobjective Optimization[C]// IEEE Congress on Evolutionary Computation. IEEE, 2013.

Zhang Q, Li H. MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition[J]. IEEE Transactions on Evolutionary Computation, 2008, 11(6):712-731.

Cheng R, Jin Y, Olhofer M, et al. A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization[J]. IEEE Transactions on Evolutionary Computation, 2016:1-1.

Asafuddoula M, Ray T, Sarker R. A Decomposition-Based Evolutionary Algorithm for Many Objective Optimization[J]. IEEE Transactions on Evolutionary Computation, 2015, 19(3):445-460.

Zapotecas Martínez, Saúl, Coello Coello C A . [ACM Press the 13th annual conference - Dublin, Ireland (2011.07.12-2011.07.16)] Proceedings of the 13th annual conference on Genetic and evolutionary computation - GECCO "11 - A multi-objective particle swarm optimizer based on decomposition[C]// Conference on Genetic & Evolutionary Computation. ACM, 2011:69.


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