Diversified sine–cosine algorithm based on differential evolution for multidimensional knapsack problem

Gupta, Shubham; Su, Rong; Singh, Shitu

doi:10.1016/j.asoc.2022.109682

Cited by 16 publications

(1 citation statement)

References 57 publications

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“…We tested 20 independent runs on each function with the similar populations and iterations with previous experiments, ensuring a rigorous evaluation of the algorithms. The function values are summarized in Table 5, which provide an in-depth analysis of the performance of the IWSCA and its comparison to other algorithms, including other improved versions of SCA, MSCA [37] and ISCA [38]. It shows that the IWSCA get closer to the minimum values than other algorithms.…”

Section: Ieee Cec2019 Large-scale Global Optimization Problems Experi...mentioning

confidence: 99%

Improved sine cosine algorithm for optimization problems based on self-adaptive weight and social strategy

chun

2023

Preprint

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The Sine Cosine Algorithm (SCA) is a well-known optimization technique that utilizes sine and cosine functions to identify optimal solutions. Despite its popularity, the SCA has limitations in terms of low diversity, stagnation in local optima, and difficulty in achieving global optimization, particularly in complex large-scale problems. In response, we propose a novel approach named the Improved Weight and Strategy Sine Cosine Algorithm (IWSCA). The IWSCA achieves this by introducing novel self-adaptive weight and social strategies that enable the algorithm to efficiently search for optimal solutions in complex large-scale problems. The performance of the IWSCA is evaluated with 23 benchmark test functions and the IEEE CEC 2019 benchmark suite, compare it to a state-of-the-art heuristic algorithm and two improved versions of the SCA. Our experimental results demonstrate that the IWSCA outperforms existing methods in terms of convergence precision and robustness.

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Section: Ieee Cec2019 Large-scale Global Optimization Problems Experi...mentioning

confidence: 99%

Improved sine cosine algorithm for optimization problems based on self-adaptive weight and social strategy

chun

2023

Preprint

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Ant-Antlion Optimizer with Similarity Information for Multidimensional Knapsack Problem

Liu,

Yang,

Zheng

et al. 2024

Communications in Computer and Information Science

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An enhanced multi-operator differential evolution algorithm for tackling knapsack optimization problem

Sallam

Abohany

Rizk-Allahi

2023

Neural Comput & Applic

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Diversified sine–cosine algorithm based on differential evolution for multidimensional knapsack problem

Cited by 16 publications

References 57 publications

Improved sine cosine algorithm for optimization problems based on self-adaptive weight and social strategy

Improved sine cosine algorithm for optimization problems based on self-adaptive weight and social strategy

Ant-Antlion Optimizer with Similarity Information for Multidimensional Knapsack Problem

An enhanced multi-operator differential evolution algorithm for tackling knapsack optimization problem

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