Very Large-Scale Neighborhood Search for Solving Multiobjective Combinatorial Optimization Problems

Very large-scale neighborhood search (VLSNS) is a technique intensively used in single-objective optimization. However, there is almost no study of VLSNS for multiobjective optimization. We show in this paper that this technique is very efficient for the resolution of multiobjective combinatorial optimization problems. Two problems are considered: the multiobjective multidimensional knapsack problem and the multiobjective set covering problem. VLSNS are proposed for these two problems and are integrated into the two-phase Pareto local search. The results obtained on biobjective instances outperform the state-of-the-art results for various indicators.

[1]  Ricardo P. Beausoleil,et al.  Multi-start and path relinking methods to deal with multiobjective knapsack problems , 2008, Ann. Oper. Res..

[2]  Maria João Alves,et al.  MOTGA: A multiobjective Tchebycheff based genetic algorithm for the multidimensional knapsack problem , 2007, Comput. Oper. Res..

[3]  José Rui Figueira,et al.  A Scatter Search Method for the Bi-Criteria Multi-dimensional {0,1}-Knapsack Problem using Surrogate Relaxation , 2004, J. Math. Model. Algorithms.

[4]  Piotr Czyzżak,et al.  Pareto simulated annealing—a metaheuristic technique for multiple‐objective combinatorial optimization , 1998 .

[5]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[6]  Jin-Kao Hao,et al.  An empirical study of tabu search for the mokp , 2002 .

[7]  Abraham P. Punnen,et al.  A survey of very large-scale neighborhood search techniques , 2002, Discret. Appl. Math..

[8]  Xavier Gandibleux,et al.  An Annotated Bibliography of Multiobjective Combinatorial Optimization , 2000 .

[9]  Eckart Zitzler,et al.  Evolutionary algorithms for multiobjective optimization: methods and applications , 1999 .

[10]  Edward P. K. Tsang,et al.  Guided Pareto Local Search based frameworks for biobjective optimization , 2010, IEEE Congress on Evolutionary Computation.

[11]  F. Glover,et al.  Handbook of Metaheuristics , 2019, International Series in Operations Research & Management Science.

[12]  Guanghui Lan,et al.  An effective and simple heuristic for the set covering problem , 2007, Eur. J. Oper. Res..

[13]  Christian Prins,et al.  Two-phase method and Lagrangian relaxation to solve the Bi-Objective Set Covering Problem , 2006, Ann. Oper. Res..

[14]  Andrzej Jaszkiewicz,et al.  On the performance of multiple-objective genetic local search on the 0/1 knapsack problem - a comparative experiment , 2002, IEEE Trans. Evol. Comput..

[15]  Brian W. Kernighan,et al.  An Effective Heuristic Algorithm for the Traveling-Salesman Problem , 1973, Oper. Res..

[16]  José Rui Figueira,et al.  Solving the bi-objective multi-dimensional knapsack problem exploiting the concept of core , 2009, Appl. Math. Comput..

[17]  Zbigniew Michalewicz,et al.  Genetic Algorithms for the 0/1 Knapsack Problem , 1994, ISMIS.

[18]  Xavier Gandibleux,et al.  Multiobjective Combinatorial Optimization — Theory, Methodology, and Applications , 2003 .

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

[20]  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.

[21]  Jacques Teghem,et al.  MEMOTS: a memetic algorithm integrating tabu search for combinatorial multiobjective optimization , 2008, RAIRO Oper. Res..

[22]  Y. Aneja,et al.  BICRITERIA TRANSPORTATION PROBLEM , 1979 .

[23]  Abdullah Alsheddy,et al.  Guided Pareto Local Search and its Application to the 0/1 Multi-objective Knapsack Problems , 2009 .

[24]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation) , 2006 .

[25]  Alexander Thomasian,et al.  A GRASP algorithm for the multi-objective knapsack problem , 2004 .

[26]  Andrzej Jaszkiewicz,et al.  A Comparative Study of Multiple-Objective Metaheuristics on the Bi-Objective Set Covering Problem and the Pareto Memetic Algorithm , 2004, Ann. Oper. Res..

[27]  Jacques Teghem,et al.  The multiobjective multidimensional knapsack problem: a survey and a new approach , 2010, Int. Trans. Oper. Res..

[28]  E. L. Ulungu,et al.  Multi‐objective combinatorial optimization problems: A survey , 1994 .

[29]  Evripidis Bampis,et al.  A Dynasearch Neighborhood for the Bicriteria Traveling Salesman Problem , 2004, Metaheuristics for Multiobjective Optimisation.

[30]  Dalessandro Soares Vianna,et al.  A GRASP algorithm for the multi-objective knapsack problem , 2004, XXIV International Conference of the Chilean Computer Science Society.

[31]  Marco Laumanns,et al.  An Adaptive Scheme to Generate the Pareto Front Based on the Epsilon-Constraint Method , 2005, Practical Approaches to Multi-Objective Optimization.

[32]  Pierre Hansen,et al.  Variable neighborhood search: Principles and applications , 1998, Eur. J. Oper. Res..

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

[34]  Jacques Teghem,et al.  Two-phase Pareto local search for the biobjective traveling salesman problem , 2010, J. Heuristics.

[35]  Xavier Gandibleux,et al.  Metaheuristics for Multiobjective Optimisation , 2004, Lecture Notes in Economics and Mathematical Systems.

[36]  Zbigniew W. Ras,et al.  Methodologies for Intelligent Systems , 1991, Lecture Notes in Computer Science.

[37]  Thomas Stützle,et al.  Pareto Local Optimum Sets in the Biobjective Traveling Salesman Problem: An Experimental Study , 2004, Metaheuristics for Multiobjective Optimisation.

[38]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[39]  Xavier Gandibleux,et al.  Multiple Criteria Optimization: State of the Art Annotated Bibliographic Surveys , 2013 .

[40]  Andrzej Jaszkiewicz,et al.  On the computational efficiency of multiple objective metaheuristics. The knapsack problem case study , 2004, Eur. J. Oper. Res..