Relaxed complemental estimation of distribution algorithm for the multidimesional knapsack problem
Relaxed complemental estimation of distribution algorithm for the multidimesional knapsack problem
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发表时间：
2007
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通讯作者：
Quan Hui-yun
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作者：
Quan Hui-yun
进化计算属于受自然启发的算法。分布估计算法（EDAs）是基于对总体概率分布的模拟和推断。参考文献1的实践已经展示了EDAs的优势。受自然界互补机制的启发，本文提出了一种宽松互补分布估计算法（RCEDA）。我们在著名的Chu和Beasley MPK基准上进行了实验研究。计算结果表明该算法具有竞争力。
Evolutionary Computation belongs to the algorithms inspired by nature.Estimation of Distribution Algorithms(EDAs) is based on the simulation and inference of probability distribution of population.Practices in reference1 have showed the advantage of EDAs.Inspired by the complementarity mechanism in nature,this paper presents a Relaxed Complemental Estimation of Distribution Algorithm(RCEDA).We carry out experiment studies on the well-know Chu and Beasley MPK benchmark.The analysis and computational results show that the algorithm is competitive.