A population-based simulated annealing approach with adaptive mutation operator for solving the discounted {0-1} knapsack problem

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Juntao Zhao , Xiaochuan Luo
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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.
基于种群的自适应变异算子模拟退火方法求解打折{0-1}背包问题
打折的{0-1}背包问题扩展了传统的背包问题,纳入了物品组之间唯一的折扣关系,增加了选择过程的复杂性。它可以在供应链优化、资源分配和财务组合管理中找到应用。目标是在遵守产能限制的情况下实现利润最大化。本文提出了一种将基于种群的模拟退火与自适应差分进化相结合的方法来有效地解决这一问题。该算法引入了一种先进的贪婪随机初始化算法,在模拟退火框架内进行多邻域局部优化,并使用两个差分进化突变算子(DE/current-to-rand/1/bin和DE/current-to-best/1/bin)来增强探索和开发。采用综合的两阶段修复和再优化策略来处理不可行解。在两组80个基准实例上的广泛测试突出了算法的鲁棒性和性能,有效地解决了所研究问题的复杂性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Applied Soft Computing
Applied Soft Computing 工程技术-计算机:跨学科应用
CiteScore
15.80
自引率
6.90%
发文量
874
审稿时长
10.9 months
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.
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