Adaptive Memetic Computing for Evolutionary Multiobjective Optimization

Inspired by biological evolution, a plethora of algorithms with evolutionary features have been proposed. These algorithms have strengths in certain aspects, thus yielding better optimization performance in a particular problem. However, in a wide range of problems, none of them are superior to one another. Synergetic combination of these algorithms is one of the potential ways to ameliorate their search ability. Based on this idea, this paper proposes an adaptive memetic computing as the synergy of a genetic algorithm, differential evolution, and estimation of distribution algorithm. The ratio of the number of fitter solutions produced by the algorithms in a generation defines their adaptability features in the next generation. Subsequently, a subset of solutions undergoes local search using the evolutionary gradient search algorithm. This memetic technique is then implemented in two prominent frameworks of multiobjective optimization: the domination- and decomposition-based frameworks. The performance of the adaptive memetic algorithms is validated in a wide range of test problems with different characteristics and difficulties.

[1]  Giovanni Iacca,et al.  Ockham's Razor in memetic computing: Three stage optimal memetic exploration , 2012, Inf. Sci..

[2]  Kalyanmoy Deb,et al.  Real-coded Genetic Algorithms with Simulated Binary Crossover: Studies on Multimodal and Multiobjective Problems , 1995, Complex Syst..

[3]  Kwong-Sak Leung,et al.  A Memetic Algorithm for Multiple-Drug Cancer Chemotherapy Schedule Optimization , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  DebK.,et al.  A fast and elitist multiobjective genetic algorithm , 2002 .

[5]  Kay Chen Tan,et al.  Restricted Boltzmann machine based algorithm for multi-objective optimization , 2010, IEEE Congress on Evolutionary Computation.

[6]  Kalyanmoy Deb,et al.  Dynamic Multi-objective Optimization and Decision-Making Using Modified NSGA-II: A Case Study on Hydro-thermal Power Scheduling , 2007, EMO.

[7]  Giovanni Iacca,et al.  Disturbed Exploitation compact Differential Evolution for limited memory optimization problems , 2011, Inf. Sci..

[8]  Saku Kukkonen,et al.  Real-parameter optimization with differential evolution , 2005, 2005 IEEE Congress on Evolutionary Computation.

[9]  Kent McClymont,et al.  Deductive Sort and Climbing Sort: New Methods for Non-Dominated Sorting , 2012, Evolutionary Computation.

[10]  Qingfu Zhang,et al.  A Population Prediction Strategy for Evolutionary Dynamic Multiobjective Optimization , 2014, IEEE Transactions on Cybernetics.

[11]  Hisao Ishibuchi,et al.  A multi-objective genetic local search algorithm and its application to flowshop scheduling , 1998, IEEE Trans. Syst. Man Cybern. Part C.

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

[13]  Giovanni Iacca,et al.  Parallel memetic structures , 2013, Inf. Sci..

[14]  Joshua D. Knowles,et al.  Memetic Algorithms for Multiobjective Optimization: Issues, Methods and Prospects , 2004 .

[15]  Kalyanmoy Deb,et al.  An Improved Adaptive Approach for Elitist Nondominated Sorting Genetic Algorithm for Many-Objective Optimization , 2013, EMO.

[16]  Jasper A Vrugt,et al.  Improved evolutionary optimization from genetically adaptive multimethod search , 2007, Proceedings of the National Academy of Sciences.

[17]  Kalyanmoy Deb,et al.  Multi-Objective Evolutionary Algorithms for Engineering Shape Design , 2003 .

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

[19]  Kevin Kok Wai Wong,et al.  Classification of adaptive memetic algorithms: a comparative study , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[20]  Geoffrey E. Hinton.  Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[21]  Abdullah Al Mamun,et al.  Multi-Objective Optimization with Estimation of Distribution Algorithm in a Noisy Environment , 2013, Evolutionary Computation.

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

[23]  Carlos Cotta,et al.  Memetic algorithms and memetic computing optimization: A literature review , 2012, Swarm Evol. Comput..

[24]  Kok Kiong Tan,et al.  A hybrid adaptive evolutionary algorithm in the domination-based and decomposition-based frameworks of multi-objective optimization , 2012, 2012 IEEE Congress on Evolutionary Computation.

[25]  Joshua D. Knowles,et al.  M-PAES: a memetic algorithm for multiobjective optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[26]  Kay Chen Tan,et al.  A Multi-Facet Survey on Memetic Computation , 2011, IEEE Transactions on Evolutionary Computation.

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

[28]  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.

[29]  Xiaodong Li,et al.  Solving Rotated Multi-objective Optimization Problems Using Differential Evolution , 2004, Australian Conference on Artificial Intelligence.

[30]  Hussein A. Abbass,et al.  The Pareto Differential Evolution Algorithm , 2002, Int. J. Artif. Intell. Tools.

[31]  Lothar Thiele,et al.  An evolutionary algorithm for multiobjective optimization: the strength Pareto approach , 1998 .

[32]  David E. Goldberg,et al.  Multiobjective hBOA, clustering, and scalability , 2005, GECCO '05.

[33]  Bo Liu,et al.  An Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[34]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[35]  Kay Chen Tan,et al.  Enhancing the scalability of multi-objective optimization via restricted Boltzmann machine-based estimation of distribution algorithm , 2013, Inf. Sci..

[36]  Yuchou Chang,et al.  Convergence of estimation of distribution algorithms in optimization of additively noisy fitness functions , 2005, 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05).

[37]  Marco Laumanns,et al.  Bayesian Optimization Algorithms for Multi-objective Optimization , 2002, PPSN.

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

[39]  Yew-Soon Ong,et al.  Memetic Computation—Past, Present & Future [Research Frontier] , 2010, IEEE Computational Intelligence Magazine.

[40]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[41]  Jürgen Branke *,et al.  Anticipation and flexibility in dynamic scheduling , 2005 .

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

[43]  Patrick M. Reed,et al.  Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework , 2013, Evolutionary Computation.

[44]  Riccardo Poli,et al.  Evolving Problems to Learn About Particle Swarm Optimizers and Other Search Algorithms , 2006, IEEE Transactions on Evolutionary Computation.

[45]  Vladimír Kvasnička,et al.  Simulation of Baldwin effect and Dawkins memes by genetic algorithm , 1999 .

[46]  Peter J. Fleming,et al.  Preference-Inspired Coevolutionary Algorithms for Many-Objective Optimization , 2013, IEEE Transactions on Evolutionary Computation.

[47]  Bruce A. Robinson,et al.  Self-Adaptive Multimethod Search for Global Optimization in Real-Parameter Spaces , 2009, IEEE Transactions on Evolutionary Computation.

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

[49]  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 .

[50]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[51]  Miguel Á. Carreira-Perpiñán,et al.  On Contrastive Divergence Learning , 2005, AISTATS.

[52]  Pablo Moscato,et al.  A Gentle Introduction to Memetic Algorithms , 2003, Handbook of Metaheuristics.

[53]  Pablo Moscato,et al.  On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms , 1989 .

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

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

[56]  Jesús García,et al.  Solving complex high-dimensional problems with the multi-objective neural estimation of distribution algorithm , 2009, GECCO.

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

[58]  Xin Yao,et al.  Corner Sort for Pareto-Based Many-Objective Optimization , 2014, IEEE Transactions on Cybernetics.

[59]  Kay Chen Tan,et al.  An investigation on evolutionary gradient search for multi-objective optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).