Abstract
Water wave optimization (WWO) is a novel evolutionary algorithm that draws inspiration from shallow water wave model for continuous optimization problems. Multidimensional knapsack problem (MKP) is a well-known NP-hard combinatorial optimization problem which has a wide range of applications in practice. This paper proposes a discrete WWO algorithm for MKP, which adapts the key evolutionary operators including propagation and breaking to effectively search the discrete solution space of MKP. We also propose an adaptive WWO algorithm for MKP by integrating four different breaking operators and adaptively selecting among them according to their historical performance in the evolution. Experimental results show that the proposed discrete WWO algorithms have a competitive performance compared to a number of well-known evolutionary algorithms including genetic algorithm, particle swarm optimization, fruit fly optimization, ant colony optimization, and some state-of-the-art hybrid evolutionary algorithms.
Supported by grants from National Natural Science Foundation of China under Grant No. 61662036.
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Yan, HF., Cai, CY., Liu, DH., Zhang, MX. (2019). Water Wave Optimization for the Multidimensional Knapsack Problem. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_65
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