蝙蝠算法在背包问题中的应用
首发时间:2018-12-14
摘要:研究了蝙蝠算法在多目标多选择背包优化中的应用。针对传统的多目标多选择背包优化算法,由于计算复杂度高,难以获得满意解决方案的问题,试图用蝙蝠算法去解决。首先,讨论了蝙蝠算法的生物学动机,阐述了蝙蝠回声定位行为的原理,以及蝙蝠算法的实现流程,然后利用这一算法去解决一个背包问题。最后由实验结果表明,相比粒子群优化算法,蝙蝠算法可以更快速的求解问题。证明了蝙蝠算法解决问题的可行性和有效性以及性能的优越性,拓展了其应用领域。
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The application of bat algorithm in knapsack problem
Abstract: In this paper, the application of bat algorithm in multiobjective and multi choice knapsack optimization is studied. Aiming at the problem of traditional multi-objective multi-choice knapsack optimization algorithm, which is difficult to obtain satisfactory solution because of its high computational complexity, bat algorithm is attempted to solve it. Firstly, the biological motivation of bat algorithm is discussed, the principle of bat echolocation behavior and the implementation process of bat algorithm are elaborated, and then a knapsack problem is solved by using this algorithm. Finally, the experimental results show that the bat algorithm can solve the problem faster than the particle swarm optimization algorithm. It proves the feasibility, effectiveness and superiority of bat algorithm in solving problems, and expands its application fields.
Keywords: decision analysis bat algorithm knapsack problem multi-objective optimization
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