SSPMO: A Scatter Tabu Search Procedure for Non-Linear Multiobjective Optimization

References

• Alves M. J., Climaco J. An interactive method for 0-1 multiobjective problems using simulated annealing and tabu search. J. Heuristics (2000) 6:385–403Crossref, Google Scholar
• Beausoleil R. P. MOSS: Multiobjective scatter search applied to non-linear multiple criteria optimization. Eur. J. Oper. Res. (2006) 169(2):426–450Crossref, Google Scholar
• Ben Abdelaziz F., Krichen S., Chaouachi J., Voss S., Martello S., Osman I., Roucairol C. A hybrid metaheuristic for the multiobjective knapsack problem. Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization (1999) (Kluwer Academic Publishers, Boston, MA) 205–212Crossref, Google Scholar
• Caballero R., Gandibleux X., Molina J. MOAMP—A generic multiobjective metaheuristic using an adaptive memory. (2004) . Technical report, University of Valenciennes, Valenciennes, FranceGoogle Scholar
• Coello C., Van Veldhuizen D. A., Lamont G. B.Evolutionary Algorithms for Solving Multi-Objective Problems (2002) (Kluwer Academic Publishers, Boston, MA) Crossref, Google Scholar
• Dahl G., Jörnsten K., Lokketangen A. A tabu search approach to the channel minimization problem. Internat. Conf. on Optim.: Techniques and Appl. (1995) Chengdu, ChinaGoogle Scholar
• Deb K. Multi-objective genetic algorithms: Problem difficulties and construction of test problems. Evolutionary Comput. (1999) 7:205–230Crossref, Google Scholar
• Deb K., Pratap A., Agarwal S., Meyarivan T. A fast and elitist multi-objective genetic algorithm-NSGA-II. (2000) . KanGAL Report 2000001Google Scholar
• Deb K., Thiele L., Laumanns M., Zitzler E. Scalable multi-objective optimization test problems. Congress on Evolutionary Computation (2002) 1(Piscataway, NJ)825–830Institute of Electrical and Electronics EngineersCrossref, Google Scholar
• Duckstein L., Atrek E., Gallagher R. H., Ragsdell K. M., Zienkiewicz O. C. Multiobjective optimization in structural design: The model choice problem. New Directions in Optimum Structural Design (1984) (Wiley, New York) 459–481Google Scholar
• Erhgott M., Gandibleux X. A survey and annotated bibliography on multiobjective combinatorial optimization. OR Spektrum (2000) 22:425–460Crossref, Google Scholar
• Gandibleux X., Freville A. Tabu search based procedure for solving the 0-1 multiobjective knapsack problem: The two objectives case. J. Heuristics (2000) 6:361–383Crossref, Google Scholar
• Gandibleux X., Mezdaoui N., Freville A., Caballero R., Ruiz F., Steuer R. A tabu search procedure to solve multiobjective combinatorial optimization problems. Advances in Multiple Objective and Goal Programming, Lecture Notes in Economics and Mathematical Systems (1997) 455(Springer, Berlin, Germany) 291–300Crossref, Google Scholar
• Garcia F., Melian B., Moreno J. A., Moreno-Vega J. M. Scatter search for multiple objective p-facility location problems. (2002) . Groupe de Travail ROADEF sur la Programmation Mathématique MultiObjectif, 6ème journée de travail, Paris, FranceGoogle Scholar
• Glover F. Tabu search for non-linear and parametric optimization (with links to genetic algorithms). Discrete Appl. Math. (1994) 49:231–255Crossref, Google Scholar
• Glover F., Laguna M.Tabu Search (1997) (Kluwer Academic Publishers, Boston, MA) Crossref, Google Scholar
• Glover F., Laguna M., Martí R. Fundamentals of scatter search and path relinking. Control Cybernetics (2000) 39:653–684Google Scholar
• Gomes da Silva C., Clímaco J., Figueira J. A scatter search method for the bi-criteria multi-dimensional {0, 1}-knapsack problem using surrogate relaxation. J. Math. Model. Algorithms (2004) 3:183–208Crossref, Google Scholar
• Hansen M. P. Tabu search for multiobjective optimization: MOTS. 13th Internat. Conf. Multiple Criteria Decision Making (1997) Cape Town, South AfricaGoogle Scholar
• Hertz A., Jaumard B., Ribeiro C., Formosinho F. W. A multicriteria tabu search approach to cell formation problems in group technology with multiple objectives. Recherche Operationelle/Oper. Res. (1994) 28:303–328Google Scholar
• Jones D. F., Mirrazavi S. K., Tamiz M. Multiobjective metaheuristics: An overview of the current state of the art. Eur. J. Oper. Res. (2002) 137:1–9Crossref, Google Scholar
• Laguna M., Martí R.Scatter Search: Methodology and Implementations in C (2003) (Kluwer, Boston, MA) Crossref, Google Scholar
• Schaffer J. D., Grefenstette J. J. Multiple objective optimization with vector evaluated genetic algorithms. Proc. First Internat. Conf. Genetic Algorithms and Their Appl. (1985) (Lawrence Erlbaum, Mahwah, NJ) 93–100Google Scholar
• Silverman B. W.Density Estimation for Statistics and Data Analysis (1986) (Chapman and Hall, London, UK) Crossref, Google Scholar
• Van Veldhuizen D. A., Lamont G. B. Multiobjective evolutionary algorithms: Analyzing the state-of-the-art. Evolutionary Comput. (2000) 8:125–147Crossref, Google Scholar
• Yu P. L. A class of solutions for group decision problems. Management Sci. (1973) 19:936–946Link, Google Scholar
• Zeleny M., Cochrane J. L., Zeleny M. Compromise programming. Multiple Criteria Decision Making (1973) (University of South Carolina Press, Columbia, SC) 262–301Google Scholar
• Zitzler E. Evolutionary algorithms for multiobjective optimization: Methods and applications. (1999) . Ph.D. thesis, Swiss Federal Institute of Technology (ETH), Zurich, SwitzerlandGoogle Scholar
• Zitzler E., Thiele L. Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Trans. Evolutionary Comput. (1999) 3:257–271Crossref, Google Scholar
• Zitzler E., Deb K., Thiele L. Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Comput. J. (2000) 8:125–148Crossref, Google Scholar
• Zitzler E., Laumanns M., Thiele L. SPEA2: Improving the strength pareto evolutionary algorithm. (2001) . Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zurich, SwitzerlandGoogle Scholar