Boosting the Performance of MOEA/D-DRA with a Multi-Objective Hyper-Heuristic Based on Irace and UCB Method for Heuristic Selection

Multi-Objective Evolutionary Algorithm based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA) is one of the most successful decomposition based multiobjective algorithm. Its main feature is a mechanism to allocate different computational effort proportional to the difficult of each subproblem. Despite its success, MOEA/D-DRA has a large set of parameters and operators, whose selection could be a difficult task. This paper aims at improving the performance of MOEA/D-DRA by means of a hyper-heuristic using two parameter/operator selection phases: one off-line strongly based on Iterated Race Automatic Algorithm Configuration (Irace) and another one (online) based on the Upper Confidence Bound (UCB) technique. The proposed approach is compared with the original MOEA/D-DRA, NSGAII and IBEA over 51 instances of 7 well known benchmarks (CEC 2009, GLT, LZ09, MOP, DTLZ, ZDT and WFG). Results show that Irace and UCB are interesting methods to support the hyper-heuristic functioning when selecting parameters/operators of MOEA/D-DRA in the addressed problems.

[1]  Michèle Sebag,et al.  Analysis of adaptive operator selection techniques on the royal road and long k-path problems , 2009, GECCO.

[2]  R. Sundaram.  A First Course in Optimization Theory: Optimization in ℝ n , 1996 .

[3]  Graham Kendall,et al.  Hyper-Heuristics: An Emerging Direction in Modern Search Technology , 2003, Handbook of Metaheuristics.

[4]  Heike Trautmann,et al.  R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection , 2013, LION.

[5]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[6]  Leslie Pérez Cáceres,et al.  The irace package: Iterated racing for automatic algorithm configuration , 2016 .

[7]  Gary B. Lamont,et al.  Applications Of Multi-Objective Evolutionary Algorithms , 2004 .

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

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

[10]  Ponnuthurai N. Suganthan,et al.  Recent advances in differential evolution - An updated survey , 2016, Swarm Evol. Comput..

[11]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

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

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

[14]  Lothar Thiele,et al.  A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers , 2006 .

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

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

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

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

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

[20]  Qingfu Zhang,et al.  Meta-Heuristic Combining Prior Online and Offline Information for the Quadratic Assignment Problem , 2014, IEEE Transactions on Cybernetics.

[21]  Aurora Trinidad Ramirez Pozo,et al.  Upper Confidence Bound (UCB) Algorithms for Adaptive Operator Selection in MOEA/D , 2015, EMO.

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

[23]  Qingfu Zhang,et al.  Multiobjective optimization Test Instances for the CEC 2009 Special Session and Competition , 2009 .

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

[25]  Marco Laumanns,et al.  Scalable Test Problems for Evolutionary Multiobjective Optimization , 2005, Evolutionary Multiobjective Optimization.

[26]  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.

[27]  Michel Gendreau,et al.  Hyper-heuristics: a survey of the state of the art , 2013, J. Oper. Res. Soc..

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

[29]  Graham Kendall,et al.  A Classification of Hyper-heuristic Approaches , 2010 .

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