{"title":"A population-based simulated annealing approach with adaptive mutation operator for solving the discounted {0-1} knapsack problem","authors":"Juntao Zhao , Xiaochuan Luo","doi":"10.1016/j.asoc.2025.113480","DOIUrl":null,"url":null,"abstract":"<div><div>The discounted {0-1} knapsack problem extends the traditional knapsack problem by incorporating unique discount relationships among item groups, adding complexity to the selection process. It finds applications in supply chain optimization, resource allocation, and financial portfolio management. The objective is to maximize profit while adhering to capacity constraints. This paper presents a novel approach that integrates population-based simulated annealing with adaptive differential evolution to efficiently solve the problem. The proposed algorithm introduces an advanced greedy randomized initialization, multi-neighborhood local optimization within simulated annealing framework, and use two differential evolution mutation operators (DE/current-to-rand/1/bin and DE/current-to-best/1/bin) to enhance exploration and exploitation. A comprehensive two-stage repair and re-optimization strategy is employed to handle infeasible solutions. Extensive testing on two groups of 80 benchmark instances highlights the algorithm’s robustness and performance, effectively tackling the complexities of the studied problem.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"181 ","pages":"Article 113480"},"PeriodicalIF":7.2000,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Soft Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1568494625007914","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
The discounted {0-1} knapsack problem extends the traditional knapsack problem by incorporating unique discount relationships among item groups, adding complexity to the selection process. It finds applications in supply chain optimization, resource allocation, and financial portfolio management. The objective is to maximize profit while adhering to capacity constraints. This paper presents a novel approach that integrates population-based simulated annealing with adaptive differential evolution to efficiently solve the problem. The proposed algorithm introduces an advanced greedy randomized initialization, multi-neighborhood local optimization within simulated annealing framework, and use two differential evolution mutation operators (DE/current-to-rand/1/bin and DE/current-to-best/1/bin) to enhance exploration and exploitation. A comprehensive two-stage repair and re-optimization strategy is employed to handle infeasible solutions. Extensive testing on two groups of 80 benchmark instances highlights the algorithm’s robustness and performance, effectively tackling the complexities of the studied problem.
期刊介绍:
Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities.
Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.