针对0-1背包问题的智能多群体多目标蚁群优化

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI:10.1109/CEC.2008.4630948

S. K. Chaharsooghi, Amir Hosein Meimand Kermani

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引用次数: 11

摘要

背包问题是一个著名的优化问题。即使单目标情况已被证明是np困难的，但多目标情况比单目标情况更难。提出了求解背包多目标问题的改进蚁群优化算法，以达到非支配解的最优层。我们还提出了一种新的多目标信息素更新规则，增加了算法的学习量，从而提高了算法的有效性。最后，将该算法的计算结果与NSGA II进行了比较，NSGA II优于本文综述的大多数多目标蚁群优化算法。

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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An intelligent multi-colony multi-objective ant colony optimization (ACO) for the 0–1 knapsack problem

The knapsack problem is a famous optimization problem. Even the single objective case has been proven to be NP-hard the multi-objective is harder than the single objective case. This paper presents the modified ant colony optimization (ACO) algorithm for solving knapsack multi-objective problem to achieve the best layer of non-dominated solution. We also proposed a new pheromone updating rule for multi-objective case which can increase the learning of algorithm and consequently increase effectiveness. Finally, the computational result of proposed algorithm is compared with the NSGA II which outperforms most of the multi-objective ant colony optimization algorithm which are reviewed in this paper.

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2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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