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Solving binary multi-objective knapsack problems with novel greedy strategy

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Abstract

This paper shows that the many greedy strategies that have been designed to repair infeasible solutions to multi-objective knapsack problems (MOKPs) with small item differences perform poorly when item differences are large. To effectively solve different types of MOKPs, this paper proposes a greedy strategy to improve the quality of feasible and infeasible solutions. It repairs all of the infeasible solutions to feasible solutions, and then maximizes the quality of each feasible solution under the limitations of knapsack capacities. Simulation experiments on different types of MOKPs show that the proposed strategy is superior to existing strategies. Compared with MOGLS, MOEA/D, and MOEA/D-M2M, the proposed evolutionary framework performs better in solving different MOKPs.

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Notes

  1. \(A \prec B\) means that A dominates B under the Pareto dominance [33].

  2. It is provided at https://github.com/BIMK/PlatEMO.

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Authors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Jiawei Yuan

  2. PCI-Suntek Technology Co Ltd, Guangzhou, China

    Yifan Li

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  1. Jiawei Yuan
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  2. Yifan Li
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Correspondence to Jiawei Yuan.

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Yuan, J., Li, Y. Solving binary multi-objective knapsack problems with novel greedy strategy. Memetic Comp. 13, 447–458 (2021). https://doi.org/10.1007/s12293-021-00344-7

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