Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
The zero/one multiple knapsack problem and genetic algorithms
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
Swarm intelligence
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
An Electromagnetism-like Mechanism for Global Optimization
Journal of Global Optimization
Computers and Operations Research
A Revised EM-Like Algorithm + K-OPT Method for Solving the Traveling Salesman Problem
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1
Computers and Operations Research
An electromagnetic meta-heuristic for the nurse scheduling problem
Journal of Heuristics
A new ant colony optimization algorithm for the multidimensional Knapsack problem
Computers and Operations Research
The electromagnetism meta-heuristic applied to the resource-constrained project scheduling problem
EA'05 Proceedings of the 7th international conference on Artificial Evolution
Apply the particle swarm optimization to the multidimensional knapsack problem
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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In this paper a novel model based on Electromagnetism-Like Mechanism (EM) is proposed which is highly compatible with discrete space problems. The proposed method utilizes the EM operators to move particles towards an optimal or near optimal solutions. In fact, the proposed algorithm exploits the crossover operator to calculate forces on particles and move them according to these forces. To keep the algorithm from getting sock on local maxima a mutation approach is used. To show the performance of the proposed method it is applied on a discrete space problem called Multidimensional Knapsack Problem (MKP). To compare our results, some standard test benches of the MKP are used and the results are compared with other methods. Experimental results showed that the proposed method outperforms some new methods in terms of the number of needed iterations to find best known solutions (about 73% in average) and CPU time (about 77%). Furthermore, the DEM is applied on some bigger MKPs and the experiments showed that the algorithm works very well and had very small error from best known optimums (0.17% error in average).