The slime mould algorithm (SMA) has recently been introduced to solve continuous engineering problems, which has been employed to solve a wide range of various problems due to its good performance. This paper presents an enhanced binary SMA for solving the 0–1 knapsack problem at different scales. In the presented binary SMA, eight different transfer functions have been used and evaluated. The transfer function, which has performed better than others, has been proposed for the subsequent experiments. The Bitwise and Gaussian mutation operators are used to enhance the performance of the proposed binary SMA. Furthermore, a penalty function and a repair algorithm are used to handle infeasible solutions. The proposed method’s performance was evaluated statistically on 63 standard datasets with different scales. The obtained results from the proposed method were compared with ten state-of-the-art methods. The results indicated the superiority of the proposed methods.

An enhanced binary slime mould algorithm for solving the 0–1 knapsack problem

Nicola Epicoco
2021

Abstract

The slime mould algorithm (SMA) has recently been introduced to solve continuous engineering problems, which has been employed to solve a wide range of various problems due to its good performance. This paper presents an enhanced binary SMA for solving the 0–1 knapsack problem at different scales. In the presented binary SMA, eight different transfer functions have been used and evaluated. The transfer function, which has performed better than others, has been proposed for the subsequent experiments. The Bitwise and Gaussian mutation operators are used to enhance the performance of the proposed binary SMA. Furthermore, a penalty function and a repair algorithm are used to handle infeasible solutions. The proposed method’s performance was evaluated statistically on 63 standard datasets with different scales. The obtained results from the proposed method were compared with ten state-of-the-art methods. The results indicated the superiority of the proposed methods.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11697/168296
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