Binary metaheuristic algorithms for 0–1 knapsack problems: Performance analysis, hybrid variants, and real-world application DOI Creative Commons
Mohamed Abdel‐Basset, Reda Mohamed,

Safaa Saber

et al.

Journal of King Saud University - Computer and Information Sciences, Journal Year: 2024, Volume and Issue: 36(6), P. 102093 - 102093

Published: June 13, 2024

This paper examines the performance of three binary metaheuristic algorithms when applied to two distinct knapsack problems (0–1 (KP01) and multidimensional (MKP)). These are based on classical mantis search algorithm (MSA), quadratic interpolation optimization (QIO) method, well-known differential evolution (DE). Because these were designed for continuous problems, they could not be used directly solve problems. As a result, V-shaped S-shaped transfer functions propose variants algorithms, such as (BDE), (BQIO), (BMSA). evaluated using various high-dimensional KP01 examples compared several techniques determine their efficacy. To enhance those combined with repair operator 2 (RO2) offer better hybrid variants, namely HMSA, HQIO, HDE. Those medium- large-scale MKP instances, well other demonstrate effectiveness. comparison is conducted metrics: average fitness value, Friedman mean rank, computational cost. The experimental findings that HQIO strong alternative solving MKP. In addition, proposed Merkle-Hellman Knapsack Cryptosystem resource allocation problem in adaptive multimedia systems (AMS) illustrate effectiveness optimize real applications. handling knapsack-based

Language: Английский

Binary metaheuristic algorithms for 0–1 knapsack problems: Performance analysis, hybrid variants, and real-world application DOI Creative Commons
Mohamed Abdel‐Basset, Reda Mohamed,

Safaa Saber

et al.

Journal of King Saud University - Computer and Information Sciences, Journal Year: 2024, Volume and Issue: 36(6), P. 102093 - 102093

Published: June 13, 2024

This paper examines the performance of three binary metaheuristic algorithms when applied to two distinct knapsack problems (0–1 (KP01) and multidimensional (MKP)). These are based on classical mantis search algorithm (MSA), quadratic interpolation optimization (QIO) method, well-known differential evolution (DE). Because these were designed for continuous problems, they could not be used directly solve problems. As a result, V-shaped S-shaped transfer functions propose variants algorithms, such as (BDE), (BQIO), (BMSA). evaluated using various high-dimensional KP01 examples compared several techniques determine their efficacy. To enhance those combined with repair operator 2 (RO2) offer better hybrid variants, namely HMSA, HQIO, HDE. Those medium- large-scale MKP instances, well other demonstrate effectiveness. comparison is conducted metrics: average fitness value, Friedman mean rank, computational cost. The experimental findings that HQIO strong alternative solving MKP. In addition, proposed Merkle-Hellman Knapsack Cryptosystem resource allocation problem in adaptive multimedia systems (AMS) illustrate effectiveness optimize real applications. handling knapsack-based

Language: Английский

Citations

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