An Improved Scalarization-based Dominance Evolutionary Algorithm for Many-Objective Optimization

Many-objective optimization problems (MaOPs) pose a multitude of challenges for existing multi-objective evolutionary algorithms. One of the key challenges is the poor selection pressure for optimization problems involving a high-dimensional objective space. To overcome this challenge, this paper extends the scalarization-based dominance evolutionary algorithm (SDEA) to improve its convergence rate. Inspired by the neighborhood information sharing scheme between the subproblems in the decomposition-based multi-objective evolutionary algorithm (MOEA/D), a selection mechanism is proposed for enhancing the SDEA in tackling MaOPs. The improved SDEA model is evaluated using different MaOP instances, which include DTLZ and WFG. The results indicate the effectiveness of the enhanced SDEA model in undertaking MaOPs.

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

[2]  Saeid Nahavandi,et al.  Solving a multiobjective job shop scheduling problem using Pareto Archived Cuckoo Search , 2012, Proceedings of 2012 IEEE 17th International Conference on Emerging Technologies & Factory Automation (ETFA 2012).

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

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

[5]  Peter J. Fleming,et al.  Evolutionary many-objective optimisation: an exploratory analysis , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[6]  Chee Peng Lim,et al.  A scalarization-based dominance evolutionary algorithm for many-objective optimization , 2019, Inf. Sci..

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

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

[9]  Abbas Khosravi,et al.  Intelligent Traffic Light Control of Isolated Intersections Using Machine Learning Methods , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[10]  Saeid Nahavandi,et al.  Target coverage in camera networks for manufacturing workplaces , 2016, J. Intell. Manuf..

[11]  Peter J. Fleming,et al.  On the Evolutionary Optimization of Many Conflicting Objectives , 2007, IEEE Transactions on Evolutionary Computation.

[12]  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).

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

[14]  Xin Yao,et al.  A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization , 2016, IEEE Transactions on Evolutionary Computation.

[15]  Chee Peng Lim,et al.  A new decomposition-based evolutionary framework for many-objective optimization , 2017, 2017 Annual IEEE International Systems Conference (SysCon).

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

[17]  Saeid Nahavandi,et al.  An effective heuristic for stockyard planning and machinery scheduling at a coal handling facility , 2013, 2013 11th IEEE International Conference on Industrial Informatics (INDIN).

[18]  Gary G. Yen,et al.  Diversity improvement in Decomposition-Based Multi-Objective Evolutionary Algorithm for many-objective optimization problems , 2014, 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

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

[21]  Martin J. Oates,et al.  PESA-II: region-based selection in evolutionary multiobjective optimization , 2001 .

[22]  Saeid Nahavandi,et al.  Optimal Feature Subset Selection for Neuron Spike Sorting Using the Genetic Algorithm , 2015, ICONIP.

[23]  L. Shapley,et al.  College Admissions and the Stability of Marriage , 1962 .

[24]  Nicola Beume,et al.  Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization , 2007, EMO.

[25]  Hisao Ishibuchi,et al.  Relation between Neighborhood Size and MOEA/D Performance on Many-Objective Problems , 2013, EMO.

[26]  Chee Peng Lim,et al.  Improved NSGA-III using neighborhood information and scalarization , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[27]  Carlos A. Coello Coello,et al.  Ranking Methods in Many-Objective Evolutionary Algorithms , 2009, Nature-Inspired Algorithms for Optimisation.

[28]  Saeid Nahavandi,et al.  A hybrid cuckoo search and variable neighborhood descent for single and multiobjective scheduling problems , 2014 .

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