Hybrid Learning Moth Search Algorithm for Solving Multidimensional Knapsack Problems

Yanhong Feng, Hongmei Wang (), Zhaoquan Cai (), Mingliang Li and Xi Li
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Yanhong Feng: School of Information Engineering, Hebei GEO University, Shijiazhuang 050031, China
Hongmei Wang: Xinjiang Institute of Engineering, Information Engineering College, Ürümqi 830023, China
Zhaoquan Cai: Shanwei Institute of Technology, Shanwei 516600, China
Mingliang Li: School of Information Engineering, Hebei GEO University, Shijiazhuang 050031, China
Xi Li: School of Information Engineering, Hebei GEO University, Shijiazhuang 050031, China

Mathematics, 2023, vol. 11, issue 8, 1-28

Abstract: The moth search algorithm (MS) is a relatively new metaheuristic optimization algorithm which mimics the phototaxis and Lévy flights of moths. Being an NP-hard problem, the 0–1 multidimensional knapsack problem (MKP) is a classical multi-constraint complicated combinatorial optimization problem with numerous applications. In this paper, we present a hybrid learning MS (HLMS) by incorporating two learning mechanisms, global-best harmony search (GHS) learning and Baldwinian learning for solving MKP. (1) GHS learning guides moth individuals to search for more valuable space and the potential dimensional learning uses the difference between two random dimensions to generate a large jump. (2) Baldwinian learning guides moth individuals to change the search space by making full use of the beneficial information of other individuals. Hence, GHS learning mainly provides global exploration and Baldwinian learning works for local exploitation. We demonstrate the competitiveness and effectiveness of the proposed HLMS by conducting extensive experiments on 87 benchmark instances. The experimental results show that the proposed HLMS has better or at least competitive performance against the original MS and some other state-of-the-art metaheuristic algorithms. In addition, the parameter sensitivity of Baldwinian learning is analyzed and two important components of HLMS are investigated to understand their impacts on the performance of the proposed algorithm.

Keywords: combinatorial optimization; multidimensional knapsack problem; metaheuristic; moth search algorithm; Baldwinian learning; global-best harmony search (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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