Guided Pareto Local Search and its Application to the 0/1 Multi-objective Knapsack Problems

Pareto Local Search (PLS) is a generalization of the local search algorithms to handle more than one objective. In this paper, two variants of PLS are examined on the multiobjective 0/1 knapsack problems, compared with three well-known multiobjective EA algorithms, namely SPEA, SPEA2 and NSGA2. Furthermore, A Guided Local Search (GLS) based multiobjective optimization algorithm is proposed, the Guided Pareto Local Search (GPLS). GPLS shows the ability of GLS to set on top of PLS not only to help PLS to escape Pareto local optimal set, but also to enhance its convergence toward and spread over the true Pareto front. Experimental results have shown that PLS can produce results with a very good quality, and proven the effectiveness of the GPLS.

[1]  Arnaud Fréville,et al.  Tabu Search Based Procedure for Solving the 0-1 MultiObjective Knapsack Problem: The Two Objectives Case , 2000, J. Heuristics.

[2]  Patrick Prosser,et al.  Guided Local Search for the Vehicle Routing Problem , 1997 .

[3]  Edward P. K. Tsang,et al.  Guided local search and its application to the traveling salesman problem , 1999, Eur. J. Oper. Res..

[4]  Mehrdad Tamiz,et al.  Multi-objective meta-heuristics: An overview of the current state-of-the-art , 2002, Eur. J. Oper. Res..

[5]  E. Tsang,et al.  Guided Local Search , 2010 .

[6]  P. John Clarkson,et al.  Multi-objective Parallel Tabu Search , 2004, PPSN.

[7]  Ujjwal Maulik,et al.  A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA , 2008, IEEE Transactions on Evolutionary Computation.

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

[9]  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..

[10]  Mhand Hifi,et al.  Heuristic algorithms for the multiple-choice multidimensional knapsack problem , 2004, J. Oper. Res. Soc..

[11]  Hisao Ishibuchi,et al.  Incorporation of Scalarizing Fitness Functions into Evolutionary Multiobjective Optimization Algorithms , 2006, PPSN.

[12]  Edward P. K. Tsang,et al.  Fast local search and guided local search and their application to British Telecom's workforce scheduling problem , 1997, Oper. Res. Lett..

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

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

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

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

[17]  Michel Gendreau,et al.  TRAVELING SALESMAN PROBLEMS WITH PROFITS: AN OVERVIEW , 2001 .

[18]  U Aickelin,et al.  Handbook of metaheuristics (International series in operations research and management science) , 2005 .

[19]  Rajeev Kumar,et al.  Pareto Evolutionary Algorithm Hybridized with Local Search for Biobjective TSP , 2007 .

[20]  Arnaud Liefooghe,et al.  A Study on Dominance-Based Local Search Approaches for Multiobjective Combinatorial Optimization , 2009, SLS.

[21]  Andrzej Jaszkiewicz,et al.  Pareto memetic algorithm with path relinking for bi-objective traveling salesperson problem , 2009, Eur. J. Oper. Res..

[22]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

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

[24]  E. L. Ulungu,et al.  MOSA method: a tool for solving multiobjective combinatorial optimization problems , 1999 .

[25]  Tapabrata Ray,et al.  A Memetic Algorithm for Dynamic Multiobjective Optimization , 2009 .

[26]  Carlos A. Coello Coello,et al.  Evolutionary multiobjective optimization , 2011, WIREs Data Mining Knowl. Discov..

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

[28]  Evan J. Hughes,et al.  Evolutionary many-objective optimisation: many once or one many? , 2005, 2005 IEEE Congress on Evolutionary Computation.

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