An evolutionary algorithm recommendation method with a case study in flow shop scheduling

Numerous optimization problems exist in the product and process design, engineering, and planning, as well as production management, and the evolutionary algorithms (EAs) have been proved to be effective optimization methods to solve these problems. However, how to choose the appropriate EA is one of the key issues due to the variety of EAs and lack of experience. Under this circumstance, firstly, a novel EA recommendation system framework is designed and proposed, and the implementation of the EA recommendation method is also described in this paper. Then, computational experiments are implemented to demonstrate the effectiveness and accuracy of the proposed method on the basis of 14 typical benchmark problems and 20 classical EAs. Finally, a case study regarding permutation flow shop scheduling is also carried out to test its effectiveness and accuracy. The proposed recommendation method provides a new way to select the appropriate EAs scientifically and reasonably to solve a given optimization problem.

[1]  Fei Tao,et al.  Rotated neighbor learning-based auto-configured evolutionary algorithm , 2015, Science China Information Sciences.

[2]  Ender Özcan,et al.  A grouping hyper-heuristic framework: Application on graph colouring , 2015, Expert Syst. Appl..

[3]  Liang Gao,et al.  Optimization of process planning with various flexibilities using an imperialist competitive algorithm , 2012 .

[4]  Zhi-Hui Zhan,et al.  An Efficient Resource Allocation Scheme Using Particle Swarm Optimization , 2012, IEEE Transactions on Evolutionary Computation.

[5]  Wali Khan Mashwani,et al.  A decomposition-based hybrid multiobjective evolutionary algorithm with dynamic resource allocation , 2012, Appl. Soft Comput..

[6]  T. Blackwell,et al.  Particle swarms and population diversity , 2005, Soft Comput..

[7]  Yong Liu,et al.  An adaptive evolutionary algorithm for optimizing process planning of parallel drilling operations , 2015 .

[8]  Lina Yao,et al.  Unified Collaborative and Content-Based Web Service Recommendation , 2015, IEEE Transactions on Services Computing.

[9]  Qinbao Song,et al.  An improved data characterization method and its application in classification algorithm recommendation , 2015, Applied Intelligence.

[10]  Kay Chen Tan,et al.  Adaptive Memetic Computing for Evolutionary Multiobjective Optimization , 2015, IEEE Transactions on Cybernetics.

[11]  Liang Gao,et al.  An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem , 2009, Comput. Ind. Eng..

[12]  Daniel Brand,et al.  Optimal selection of cutting parameters in multi-tool milling operations using a genetic algorithm , 2011 .

[13]  Aydin Nassehi,et al.  Evolutionary algorithms for generation and optimization of tool paths , 2015 .

[14]  Zhiming Zhang,et al.  Similarity Measures for Retrieval in Case-Based Reasoning Systems , 1998, Appl. Artif. Intell..

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

[16]  Maoguo Gong,et al.  NNIA-RS: A multi-objective optimization based recommender system , 2015 .

[17]  Xiao-Bing Hu,et al.  Multi-objective optimization of material selection for sustainable products: Artificial neural networks and genetic algorithm approach , 2009 .

[18]  Haluk Topcuoglu,et al.  A hyper-heuristic based framework for dynamic optimization problems , 2014, Appl. Soft Comput..

[19]  Jürgen Branke,et al.  Evolutionary optimization in uncertain environments-a survey , 2005, IEEE Transactions on Evolutionary Computation.

[20]  Carlos Soares,et al.  A Comparative Study of Some Issues Concerning Algorithm Recommendation Using Ranking Methods , 2002, IBERAMIA.

[21]  Liang Gao,et al.  A hybrid backtracking search algorithm for permutation flow-shop scheduling problem minimizing makespan and energy consumption , 2015, 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).

[22]  Dong Zhou,et al.  Translation techniques in cross-language information retrieval , 2012, CSUR.

[23]  Konstantinos P. Anagnostopoulos,et al.  A particle swarm optimization based hyper-heuristic algorithm for the classic resource constrained project scheduling problem , 2014, Inf. Sci..

[24]  T. Warren Liao,et al.  A comparative study of different local search application strategies in hybrid metaheuristics , 2013, Appl. Soft Comput..

[25]  Abbas S. Milani,et al.  An exponential placement method for materials selection , 2015 .

[26]  Gordon Fraser,et al.  A Memetic Algorithm for whole test suite generation , 2015, J. Syst. Softw..

[27]  Hammoudi Abderazek,et al.  Adaptive mixed differential evolution algorithm for bi-objective tooth profile spur gear optimization , 2017 .

[28]  S. G. Ponnambalam,et al.  A Differential Evolution-Based Algorithm to Schedule Flexible Assembly Lines , 2013, IEEE Transactions on Automation Science and Engineering.

[29]  Marjan Mernik,et al.  Exploration and exploitation in evolutionary algorithms: A survey , 2013, CSUR.

[30]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[31]  Samee Ullah Khan,et al.  A survey on context-aware recommender systems based on computational intelligence techniques , 2015, Computing.

[32]  Ali M. S. Zalzala,et al.  Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons , 2000, IEEE Trans. Evol. Comput..

[33]  Tsung-Jung Hsieh.  Data-driven oriented optimization of resource allocation in the forging process using Bi-objective Evolutionary Algorithm , 2020, Eng. Appl. Artif. Intell..

[34]  Jun Zhang,et al.  Adaptive Particle Swarm Optimization , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[35]  Qiao Lihong,et al.  An improved genetic algorithm for integrated process planning and scheduling , 2012 .

[36]  Imma Ribas,et al.  Efficient heuristic algorithms for the blocking flow shop scheduling problem with total flow time minimization , 2015, Comput. Ind. Eng..

[37]  Qinbao Song,et al.  Automatic recommendation of classification algorithms based on data set characteristics , 2012, Pattern Recognit..

[38]  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).

[39]  Qining Wang,et al.  Concept, Principle and Application of Dynamic Configuration for Intelligent Algorithms , 2014, IEEE Systems Journal.

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

[41]  Thomas Stützle,et al.  Automatic Configuration of Multi-Objective ACO Algorithms , 2010, ANTS Conference.

[42]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[43]  Zhixiang Zhou,et al.  A two-stage DEA model for resource allocation in industrial pollution treatment and its application in China , 2019, Journal of Cleaner Production.

[44]  He Jiang,et al.  Hyper-Heuristics with Low Level Parameter Adaptation , 2012, Evolutionary Computation.

[45]  Jim E. Smith,et al.  Coevolving Memetic Algorithms: A Review and Progress Report , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[46]  Li Li,et al.  An integrated approach of process planning and cutting parameter optimization for energy-aware CNC machining , 2017 .

[47]  Zbigniew Michalewicz,et al.  Parameter control in evolutionary algorithms , 1999, IEEE Trans. Evol. Comput..

[48]  Lalit M. Patnaik,et al.  Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..

[49]  Qinbao Song,et al.  A Generic Multilabel Learning-Based Classification Algorithm Recommendation Method , 2014, TKDD.

[50]  Qinbao Song,et al.  A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine , 2015, PloS one.

[51]  Luyun Chen,et al.  A study on the application of material selection optimization approach for structural-acoustic optimization , 2013 .

[52]  Tao Wu,et al.  An energy-responsive optimization method for machine tool selection and operation sequence in flexible machining job shops , 2015 .

[53]  Chin-Yu Huang,et al.  An Architecture-Based Multi-Objective Optimization Approach to Testing Resource Allocation , 2015, IEEE Transactions on Reliability.

[54]  Xin-She Yang,et al.  Free Lunch or no Free Lunch: that is not Just a Question? , 2012, Int. J. Artif. Intell. Tools.

[55]  Wangtu Xu,et al.  The Memetic algorithm for the optimization of urban transit network , 2015, Expert Syst. Appl..

[56]  Wei Tan,et al.  Recommendation in an Evolving Service Ecosystem Based on Network Prediction , 2014, IEEE Transactions on Automation Science and Engineering.

[57]  Janez Brest,et al.  Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.

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

[59]  Satchidananda Dehuri,et al.  Enhancing scalability and accuracy of recommendation systems using unsupervised learning and particle swarm optimization , 2014, Appl. Soft Comput..

[60]  Yoonho Seo,et al.  Evolutionary algorithm for advanced process planning and scheduling in a multi-plant , 2005, Comput. Ind. Eng..

[61]  Ali Rıza Yıldız,et al.  Optimum design of cam-roller follower mechanism using a new evolutionary algorithm , 2018 .

[62]  A. Mileham,et al.  Applications of particle swarm optimisationin integrated process planning and scheduling , 2009 .

[63]  Xinyu Shao,et al.  A multi-objective memetic algorithm for integrated process planning and scheduling , 2016 .

[64]  Marco Leite,et al.  Selecting composite materials considering cost and environmental impact in the early phases of aircraft structure design , 2018, Journal of Cleaner Production.

[65]  Hai-Lin Liu,et al.  A Resource Allocation Evolutionary Algorithm for OFDM Based on Karush-Kuhn-Tucker Conditions , 2013 .

[66]  Udo Buscher,et al.  A multi-objective iterated local search algorithm for comprehensive energy-aware hybrid flow shop scheduling , 2019, Journal of Cleaner Production.

[67]  Wolfgang Banzhaf,et al.  A study of heuristic combinations for hyper-heuristic systems for the uncapacitated examination timetabling problem , 2009, Eur. J. Oper. Res..

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

[69]  Maoguo Gong,et al.  Personalized Recommendation Based on Evolutionary Multi-Objective Optimization [Research Frontier] , 2015, IEEE Computational Intelligence Magazine.

[70]  Hamed Samarghandi,et al.  A particle swarm optimisation for the no-wait flow shop problem with due date constraints , 2015 .

[71]  Xiaoyu Wen,et al.  An effective multi-objective genetic algorithm based on immune principle and external archive for multi-objective integrated process planning and scheduling , 2017 .

[72]  Quan-Ke Pan,et al.  Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems , 2011 .

[73]  Andy J. Keane,et al.  Meta-Lamarckian learning in memetic algorithms , 2004, IEEE Transactions on Evolutionary Computation.

[74]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[75]  Gang Yu,et al.  Two-Level Production Plan Decomposition Based on a Hybrid MOEA for Mineral Processing , 2013, IEEE Transactions on Automation Science and Engineering.

[76]  Yinong Chen,et al.  A content and user-oblivious video-recommendation algorithm , 2011, Simul. Model. Pract. Theory.

[77]  Min Liu,et al.  A High Performing Memetic Algorithm for the Flowshop Scheduling Problem With Blocking , 2013, IEEE Transactions on Automation Science and Engineering.

[78]  Hua Xu,et al.  Multiobjective Flexible Job Shop Scheduling Using Memetic Algorithms , 2015, IEEE Transactions on Automation Science and Engineering.

[79]  Shahryar Rahnamayan,et al.  Metaheuristics in large-scale global continues optimization: A survey , 2015, Inf. Sci..

[80]  Thomas Stützle,et al.  Automatic Algorithm Configuration Based on Local Search , 2007, AAAI.

[81]  Marcelo Seido Nagano,et al.  An effective constructive heuristic for permutation flow shop scheduling problem with total flow time criterion , 2017 .

[82]  Jesuk Ko,et al.  A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling , 2003, Comput. Oper. Res..

[83]  Indira G. Escamilla-Salazar,et al.  Intelligent tools selection for roughing and finishing in machining of Inconel 718 , 2017 .

[84]  Ender Özcan,et al.  A tensor-based selection hyper-heuristic for cross-domain heuristic search , 2015, Inf. Sci..

[85]  Andrew Y. C. Nee,et al.  A hybrid group leader algorithm for green material selection with energy consideration in product design , 2016 .

[86]  Feng-Hsu Wang,et al.  Effective personalized recommendation based on time-framed navigation clustering and association mining , 2004, Expert Syst. Appl..