MOTGA: a multiobjective Tchebycheff based genetic algorithm for the multidimensional knapsack problem. A new multiobjective genetic algorithm based on the Chebyshev scalarizing function, which aims to generate a good approximation of the nondominated solution set of the multiobjective problem. The algorithm performs several stages, each one intended for searching potentially nondominated solutions in a different part of the Pareto front. Pre-defined weight vectors act as pivots to define the weighted-Chebyshev scalarizing functions used in each stage. Therefore, each stage focuses the search on a specific region, leading to an iterative approximation of the entire nondominated set.This algorithm, called MOTGA (multiple objective Chebyshev based genetic algorithm) has been designed to the multiobjective multidimensional 0/1 knapsack problem, for which a dedicated routine to repair infeasible solutions was implemented. Computational results are presented and compared with the outcomes of other evolutionary algorithms.

References in zbMATH (referenced in 18 articles , 1 standard article )

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  1. Chabane, Brahim; Basseur, Matthieu; Hao, Jin-Kao: Lorenz dominance based algorithms to solve a practical multiobjective problem (2019)
  2. Ünal, Ali Nadi; Kayakutlu, Gülgün: A partheno-genetic algorithm for dynamic 0-1 multidimensional Knapsack problem (2016)
  3. Caballero, Rafael; Hernández-Díaz, Alfredo G.; Laguna, Manuel; Molina, Julián: Cross entropy for multiobjective combinatorial optimization problems with linear relaxations (2015)
  4. Gao, Jiaquan; He, Guixia; Liang, Ronghua; Feng, Zhilin: A quantum-inspired artificial immune system for the multiobjective 0-1 knapsack problem (2014)
  5. Rong, Aiying; Figueira, José Rui: Dynamic programming algorithms for the bi-objective integer knapsack problem (2014)
  6. Rong, Aiying; Figueira, José Rui: A reduction dynamic programming algorithm for the bi-objective integer knapsack problem (2013)
  7. Tavana, Madjid; Khalili-Damghani, Kaveh; Abtahi, Amir-Reza: A fuzzy multidimensional multiple-choice knapsack model for project portfolio selection using an evolutionary algorithm (2013)
  8. Bonyadi, Mohammad Reza; Li, Xiaodong: A new discrete electromagnetism-based meta-heuristic for solving the multidimensional knapsack problem using genetic operators (2012)
  9. Davis, Lauren; Samanlioglu, Funda; Jiang, Xiaochun; Mota, Daniel; Stanfield, Paul: A heuristic approach for allocation of data to RFID tags: a data allocation knapsack problem (DAKP) (2012)
  10. Lust, Thibaut; Teghem, Jacques: The multiobjective multidimensional knapsack problem: a survey and a new approach (2012)
  11. Tricoire, Fabien: Multi-directional local search (2012)
  12. Yoon, Yourim; Kim, Yong-Hyuk; Moon, Byung-Ro: A theoretical and empirical investigation on the Lagrangian capacities of the (0)-(1) multidimensional knapsack problem (2012)
  13. Berrichi, A.; Yalaoui, F.; Amodeo, L.; Mezghiche, M.: Bi-objective ant colony optimization approach to optimize production and maintenance scheduling (2010)
  14. Chang, Pei-Chann; Chen, Shih-Hsin; Fan, Chin-Yuan; Mani, V.: Generating artificial chromosomes with probability control in genetic algorithm for machine scheduling problems (2010)
  15. Florios, Kostas; Mavrotas, George; Diakoulaki, Danae: Solving multiobjective, multiconstraint knapsack problems using mathematical programming and evolutionary algorithms (2010)
  16. Aghezzaf, Brahim; Naimi, Mohamed: The two-stage recombination operator and its application to the multiobjective (0/1) knapsack problem: A comparative study (2009)
  17. Chang, Pei-Chann; Chen, Shih-Hsin; Fan, Chin-Yuan; Chan, Chien-Lung: Genetic algorithm integrated with artificial chromosomes for multi-objective flowshop scheduling problems (2008)
  18. Alves, Maria João; Almeida, Marla: MOTGA: a multiobjective Tchebycheff based genetic algorithm for the multidimensional knapsack problem (2007)