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Original Articles

A probabilistic solution discovery algorithm for solving 0-1 knapsack problem

Pages 618-626 | Received 13 Mar 2017, Accepted 29 Mar 2017, Published online: 17 Apr 2017

References

  • Pisinger D . An expanding-core algorithm for the exact 0–1 knapsack problem. Eur J Oper Res. 1995;87:175–187.
  • Kong X , Gao L , Ouyang H , et al . A simplified binary harmony search algorithm for large scale 0–1 knapsack problems. Exp Syst Appl. 2015;42:5337–5355.
  • Zhou Y , Bao Z , Luo Q , et al . A complex-valued encoding wind driven optimization for the 0–1 knapsack problem. Appl Intell. 2016;1–19.
  • Jaszkiewicz A . On the performance of multiple-objective genetic local search on the 0/1 knapsack problem-a comparative experiment. IEEE Trans Evol Comput. 2002;6:402–412.
  • Martello S , Pisinger D , Toth P . New trends in exact algorithms for the 0–1 knapsack problem. Eur J Oper Res. 2000;123:325–332.
  • Kulkarni AJ , Shabir H . Solving 0–1 knapsack problem using cohort intelligence algorithm. Int J Mach Learn Cybern. 2016;7:427–441.
  • Pendharkar PC , Rodger JA . Information technology capital budgeting using a knapsack problem. Int Trans Oper Res. 2006;13:333–351.
  • Bas E . An investment plan for preventing child injuries using risk priority number of failure mode and effects analysis methodology and a multi-objective, multi-dimensional mixed 0–1 knapsack model. Reliab Eng Syst Saf. 2011;96:748–756.
  • Haddar B , Khemakhem M , Hanafi S , et al . A hybrid heuristic for the 0–1 knapsack sharing problem. Exp Syst Appl. 2015;42:4653–4666.
  • Fréville A . The multidimensional 0–1 knapsack problem: an overview. Eur J Oper Res. 2004;155:1–21.
  • Truong TK , Li K , Xu Y . Chemical reaction optimization with greedy strategy for the 0–1 knapsack problem. Appl Soft Comput. 2013;13:1774–1780.
  • Balev S , Yanev N , Fréville A , et al . A dynamic programming based reduction procedure for the multidimensional 0–1 knapsack problem. Eur J Oper Res. 2008;186:63–76.
  • Zhang X , Deng Y , Chan FT , et al . Ifsjsp: a novel methodology for the job-shop scheduling problem based on intuitionistic fuzzy sets. Int J Prod Res. 2013;51:5100–5119.
  • Billionnet A , Soutif É . An exact method based on lagrangian decomposition for the 0–1 quadratic knapsack problem. Eur J Oper Res. 2004;157:565–575.
  • Kozanidis G , Melachrinoudis E . A branch & bound algorithm for the 0–1 mixed integer knapsack problem with linear multiple choice constraints. Comput Oper Res. 2004;31:695–711.
  • Andonov R , Poirriez V , Rajopadhye S . Unbounded knapsack problem: dynamic programming revisited. Eur J Oper Res. 2000;123:394–407.
  • Kellerer H , Pferschy U . Improved dynamic programming in connection with an fptas for the knapsack problem. J Comb Optim. 2004;8:5–11.
  • Martello S , Pisinger D , Toth P . Dynamic programming and strong bounds for the 0–1 knapsack problem. Manage Sci. 1999;45:414–424.
  • He YC , Wang XZ , He YL , et al . Exact and approximate algorithms for discounted {0-1} knapsack problem. Inf Sci. 2016;369:634–647.
  • Vasquez M , Hao JK . A hybrid approach for the 0–1 multidimensional knapsack problem. In: IJCAI; 2001. p. 328–333.
  • Dorigo M , Birattari M , Stutzle T . Ant colony optimization. IEEE Comput Intell Mag. 2006;1:28–39.
  • Stützle T . Ant colony optimization. In: International Conference on Evolutionary Multi-criterion Optimization; 2009. p. 2–2.
  • Merkle D , Middendorf M , Schmeck H . Ant colony optimization for resource-constrained project scheduling. IEEE Trans Evol Comput. 2002;6:333–346.
  • Kennedy J . Particle swarm optimization. In: Encyclopedia of machine learning. New York (NY): Springer; 2011. p. 760–766.
  • Shi Y . Particle swarm optimization: developments, applications and resources. Vol. 1, In: Proceedings of the 2001 Congress on Evolutionary Computation; 2001. p. 81–86.
  • Robinson J , Rahmat-Samii Y . Particle swarm optimization in electromagnetics. IEEE Trans Antennas Propag. 2004;52:397–407.
  • Van Laarhoven PJ , Aarts EH . Simulated annealing. In: van Laarhoven PJM , Aarts EHL , editors. Simulated annealing: theory and applications. The Netherlands: Springer; 1987. p. 7–15.
  • Kirkpatrick S . Optimization by simulated annealing: quantitative studies. J Stat Phys. 1984;34:975–986.
  • Bouleimen K , Lecocq H . A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version. Eur J Oper Res. 2003;149:268–281.
  • Deb K , Pratap A , Agarwal S , et al . A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput. 2002;6:182–197.
  • Leung YW , Wang Y . An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Trans Evol Comput. 2001;5:41–53.
  • Juang CF . A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Trans Syst Man Cybern, Part B (Cybern). 2004;34:997–1006.
  • Zhang X , Zhang Y , Zhang Z , et al . Rapid physarum algorithm for shortest path problem. Appl Soft Comput. 2014;23:19–26.
  • Zhang X , Wang Q , Adamatzky A , et al . An improved physarum polycephalum algorithm for the shortest path problem. Sci World J. 2014;2014:9 p.
  • Zhang X , Chan FT , Adamatzky A , et al . An intelligent physarum solver for supply chain network design under profit maximization and oligopolistic competition. Int J Prod Res. 2017;55:244–263.
  • Zhang X , Adamatzky A , Chan FT , et al . Physarum solver: a bio-inspired method for sustainable supply chain network design problem. Ann Oper Res. 2017:1–20.
  • Baker BM , Ayechew M . A genetic algorithm for the vehicle routing problem. Comput Oper Res. 2003;30:787–800.
  • Moslehi G , Mahnam M . A pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search. Int J Prod Econ. 2011;129:14–22.
  • Xing LN , Chen YW , Wang P , et al . A knowledge-based ant colony optimization for flexible job shop scheduling problems. Appl Soft Comput. 2010;10:888–896.
  • Lin FT . Solving the knapsack problem with imprecise weight coefficients using genetic algorithms. Eur J Oper Res. 2008;185:133–145.
  • Lv J , Wang X , Huang M , et al . Solving 0–1 knapsack problem by greedy degree and expectation efficiency. Appl Soft Comput. 2016;41:94–103.
  • Zhang X , Huang S , Hu Y , et al . Solving 0–1 knapsack problems based on amoeboid organism algorithm. Appl Math Comput. 2013;219:9959–9970.
  • Ramirez-Marquez JE , Rocco CM . Stochastic network interdiction optimization via capacitated network reliability modeling and probabilistic solution discovery. Reliab Eng Syst Saf. 2009;94:913–921.
  • Ramirez-Marquez JE , Rocco CM . Evolutionary optimization technique for multi-state two-terminal reliability allocation in multi-objective problems. IIE Trans. 2010;42:539–552.

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