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- W2936364188 abstract "The 0-1 multidimensional knapsack problem is a popular NP-hard combinatorial optimization problem with a number of real-world applications. In this paper, we investigate a heuristic algorithm called the diversification-based quantum particle swarm optimization algorithm for solving this computationally challenging problem. The proposed algorithm integrates a quantum particle swarm optimization method, a diversification-based population updating criterion, and a popular repair operator aiming at repairing infeasible solutions. Computational results on 30 widely used benchmark instances in the literature show that the proposed algorithm is very competitive compared to two popular particle swarm optimization algorithms in the literature. In particular, the proposed algorithm is able to reproduce in a very short computational time (less than one second) the optimal solution for most tested instances, with a success rate of 100%. Finally, the experimental analysis shows that the diversity of population is a key factor affecting the effectiveness of the quantum particle swarm optimization." @default.
- W2936364188 created "2019-04-25" @default.
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- W2936364188 date "2018-11-01" @default.
- W2936364188 modified "2023-09-27" @default.
- W2936364188 title "A Diversification-based Quantum Particle Swarm Optimization Algorithm for the Multidimensional Knapsack Problem" @default.
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- W2936364188 doi "https://doi.org/10.1109/ccis.2018.8691247" @default.
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