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An improved version of a core based algorithm for the multi-objective multi-dimensional knapsack problem: A computational study and comparison with meta-heuristics

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  • Mavrotas, George
  • Florios, Kostas
  • Figueira, José Rui

Abstract

In this paper we propose an improved version of a core based algorithm for the multi-objective extension of one of the most well-known combinatorial optimization problems, the multi-dimensional knapsack problem. The original algorithm was designed only for bi-objective problems combining an extension of the core concept and a branch-and-bound algorithm. It is a deterministic algorithm and the core concept exploits the “divide and conquer” solution strategy which proves very effective in such problems. The new version is not limited to bi-objective problems; it can effectively handle problems with more than two objective functions and it has features that greatly accelerate the solution process. Namely, these features are the use of a heuristic based on the Dantzig bound in the fathoming process and the better housekeeping of the incumbent list through filtering of solutions. The key parameters of the new algorithm are (a) the size of the core and (b) the number of provided cores. Varying these parameters the user can easily tune the size of the obtained Pareto set. A comparison with evolutionary algorithms, like NSGA–II, SPEA2 and MOEA/D, has been made according to run time and the most widely used metrics (set coverage, set convergence etc). Our new version performs better than the most popular evolutionary algorithms and it is comparable to very recent state-of-the-art metaheuristics, like Multi-objective Memetic Algorithm based on Decomposition (MOMAD), for multi-objective programming.

Suggested Citation

  • Mavrotas, George & Florios, Kostas & Figueira, José Rui, 2015. "An improved version of a core based algorithm for the multi-objective multi-dimensional knapsack problem: A computational study and comparison with meta-heuristics," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 25-43.
    • Handle: RePEc:eee:apmaco:v:270:y:2015:i:c:p:25-43
    DOI: 10.1016/j.amc.2015.08.018
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    1. Laumanns, Marco & Thiele, Lothar & Zitzler, Eckart, 2006. "An efficient, adaptive parameter variation scheme for metaheuristics based on the epsilon-constraint method," European Journal of Operational Research, Elsevier, vol. 169(3), pages 932-942, March.
    2. Shah, Ruchit & Reed, Patrick, 2011. "Comparative analysis of multiobjective evolutionary algorithms for random and correlated instances of multiobjective d-dimensional knapsack problems," European Journal of Operational Research, Elsevier, vol. 211(3), pages 466-479, June.
    3. Egon Balas & Eitan Zemel, 1980. "An Algorithm for Large Zero-One Knapsack Problems," Operations Research, INFORMS, vol. 28(5), pages 1130-1154, October.
    4. Mavrotas, George & Florios, Kostas, 2013. "An improved version of the augmented epsilon-constraint method (AUGMECON2) for finding the exact Pareto set in Multi-Objective Integer Programming problems," MPRA Paper 105034, University Library of Munich, Germany.
    5. Glover, Fred, 2013. "Advanced greedy algorithms and surrogate constraint methods for linear and quadratic knapsack and covering problems," European Journal of Operational Research, Elsevier, vol. 230(2), pages 212-225.
    6. Florios, Kostas & Mavrotas, George & Diakoulaki, Danae, 2010. "Solving multiobjective, multiconstraint knapsack problems using mathematical programming and evolutionary algorithms," European Journal of Operational Research, Elsevier, vol. 203(1), pages 14-21, May.
    7. Pisinger, David, 1995. "An expanding-core algorithm for the exact 0-1 knapsack problem," European Journal of Operational Research, Elsevier, vol. 87(1), pages 175-187, November.
    8. Mavrotas, George & Figueira, José Rui & Florios, Kostas, 2009. "Solving the bi-objective multidimensional knapsack problem exploiting the concept of core," MPRA Paper 105087, University Library of Munich, Germany.
    9. George B. Dantzig, 1957. "Discrete-Variable Extremum Problems," Operations Research, INFORMS, vol. 5(2), pages 266-288, April.
    10. Jakob Puchinger & Günther R. Raidl & Ulrich Pferschy, 2010. "The Multidimensional Knapsack Problem: Structure and Algorithms," INFORMS Journal on Computing, INFORMS, vol. 22(2), pages 250-265, May.
    11. Rong, Aiying & Figueira, José Rui, 2013. "A reduction dynamic programming algorithm for the bi-objective integer knapsack problem," European Journal of Operational Research, Elsevier, vol. 231(2), pages 299-313.
    12. Mavrotas, G. & Diakoulaki, D., 1998. "A branch and bound algorithm for mixed zero-one multiple objective linear programming," European Journal of Operational Research, Elsevier, vol. 107(3), pages 530-541, June.
    13. David Pisinger, 1999. "Core Problems in Knapsack Algorithms," Operations Research, INFORMS, vol. 47(4), pages 570-575, August.
    14. George Mavrotas & José Figueira & Alexandros Antoniadis, 2011. "Using the idea of expanded core for the exact solution of bi-objective multi-dimensional knapsack problems," Journal of Global Optimization, Springer, vol. 49(4), pages 589-606, April.
    15. Martello, Silvano & Pisinger, David & Toth, Paolo, 2000. "New trends in exact algorithms for the 0-1 knapsack problem," European Journal of Operational Research, Elsevier, vol. 123(2), pages 325-332, June.
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