多维背包问题的社会认知粒子群优化

Kusum Deep, Jagdish Chand Bansal
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引用次数: 25

摘要

多维背包问题(MKP)是对0-1简单背包问题的推广,是运筹学中经典的np困难问题之一,具有广泛的工程应用。文献中有几种精确的和启发式的算法来解决这个问题。本文提出一种新的粒子群优化算法——社会认知粒子群优化算法(social -cognitive particle swarm optimization, SCPSO)。与基本的二粒子群算法(BPSO)相比,改进算法引入了gbest和pbest之间的距离作为新的速度更新方程,保持了群中的多样性,使其在求解MKP时更加有效和高效。我们用各种数据实例进行了SCPSO参数微调的计算实验,并验证了我们的想法和所提出算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Socio-Cognitive Particle Swarm Optimization for Multi-Dimensional Knapsack Problem
The multidimensional knapsack problem (MKP), which is a generalization of the 0-1 simple Knapsack problem, is one of the classical NP-hard problems in operations research having a number of engineering applications. Several exact as well as heuristic algorithms are available in literature for its solution. In this paper, we propose a new particle swarm optimization (PSO) algorithm namely socio-cognitive particle swarm optimization (SCPSO) for solving the MKP. Comparing with the basic binary particle swarm optimization (BPSO), this improved algorithm introduces the distance between gbest and pbest as a new velocity update equation which maintains the diversity in the swarm and makes it more effective and efficient in solving MKP. We present computational experiments with various data instances for fine tuning of parameters of SCPSO and to validate our ideas and demonstrate the efficiency of the proposed algorithm.
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