A novel discrete differential evolution algorithm combining transfer function with modulo operation for solving the multiple knapsack problem

Wang, Lina; He, Yichao; Wang, Xizhao; Zhou, Zihang; Ouyang, Haibin; Mirjalili, Seyedali

doi:10.1016/j.ins.2024.121170

Information Sciences

2024

DOI: 10.1016/j.ins.2024.121170

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A novel discrete differential evolution algorithm combining transfer function with modulo operation for solving the multiple knapsack problem

Lina Wang,

Yichao He,

Xizhao Wang

et al.

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2024

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Cited by 2 publications

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A Comparative Study of Metaheuristic Feature Selection Algorithms for Respiratory Disease Classification

Gürkan Kuntalp,

Özcan,

Düzyel

et al. 2024

Diagnostics

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The correct diagnosis and early treatment of respiratory diseases can significantly improve the health status of patients, reduce healthcare expenses, and enhance quality of life. Therefore, there has been extensive interest in developing automatic respiratory disease detection systems. Most recent methods for detecting respiratory disease use machine and deep learning algorithms. The success of these machine learning methods depends heavily on the selection of proper features to be used in the classifier. Although metaheuristic-based feature selection methods have been successful in addressing difficulties presented by high-dimensional medical data in various biomedical classification tasks, there is not much research on the utilization of metaheuristic methods in respiratory disease classification. This paper aims to conduct a detailed and comparative analysis of six widely used metaheuristic optimization methods using eight different transfer functions in respiratory disease classification. For this purpose, two different classification cases were examined: binary and multi-class. The findings demonstrate that metaheuristic algorithms using correct transfer functions could effectively reduce data dimensionality while enhancing classification accuracy.

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A Comparative Study of Metaheuristic Feature Selection Algorithms for Respiratory Disease Classification

Gürkan Kuntalp,

Özcan,

Düzyel

et al. 2024

Diagnostics

View full text Add to dashboard Cite

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Novel Solutions to the Multidimensional Knapsack Problem Using CPLEX: New Results on ORX Benchmarks

Meriem,

Saliha,

abdelaziz

et al. 2024

JUCCT

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The Multidimensional Knapsack Problem (MKP) is a challenging combinatorial optimization problem that extends the classical knapsack problem by introducing multiple capacity constraints across various dimensions. This problem has significant practical applications, including resource allocation in supply chain management, portfolio optimization in finance, and cargo loading in logistics, where the goal is to maximize the total profit of selected items while adhering to these constraints. In this research, the CPLEX solver was applied to address the MKP using a set of complex instances from the OR-Library, specifically the ORX Benchmarks. The study focuses on 270 MKP instances characterized by varying numbers of variables (n = 100, 250, 500), constraints (m = 5), and tightness ratios (α = 0.25). Through advanced CPLEX techniques, new results were successfully obtained by employing advanced CPLEX methods, contributing to the existing literature, and setting new benchmarks for these instances.

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A novel discrete differential evolution algorithm combining transfer function with modulo operation for solving the multiple knapsack problem

Cited by 2 publications

References 37 publications

A Comparative Study of Metaheuristic Feature Selection Algorithms for Respiratory Disease Classification

A Comparative Study of Metaheuristic Feature Selection Algorithms for Respiratory Disease Classification

Novel Solutions to the Multidimensional Knapsack Problem Using CPLEX: New Results on ORX Benchmarks

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