特征选择优化的离散蟑螂算法(DCA)的发展

Y. Hendrawan, Muchnuria Rachmawati, M. R. Fauzy
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引用次数: 1

摘要

最近在仿生算法领域提出的算法之一是饥饿蟑螂入侵优化算法(HRIO)。Haven开发了优化算法HRIO,其灵感来自于最近对蟑螂社会行为的发现。结果表明,该方法能够有效地找到一组测试函数的全局最优解。然而,还没有研究者观察到HRIO用于解决离散问题。因此,我们尝试发展一种离散蟑螂算法(DCA),作为HRIO的改进来解决离散优化问题。通过单目标优化和多目标优化,对该算法进行了测试,以解决生物计算问题。结果表明,与遗传算法(GA)和离散粒子群优化算法(discrete-particle swarm optimization, pso)等已有的仿生优化算法相比,DCA具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of Discrete-Cockroach Algorithm (DCA) for Feature Selection Optimization
One of the recently proposed algorithms in the field of bio-inspired algorithm is the Hungry Roach Infestation Optimization (HRIO) algorithm. Haven has developed optimization algorithms HRIO that is inspired by recent discoveries in the social behaviour of cockroaches. Result showed that HRIO was effective at finding the global optima of a suite of test functions. However, there is no researcher who has observed HRIO for solving discrete problems. Therefore, we try to develop a discrete-cockroach algorithm (DCA) as the modification of HRIO for solving discrete optimization problem. We test the algorithm to solve bio-computation problem using single and multi-objectives optimization. The results showed DCA has better performance compared to the existed bio-inspired optimization algorithms such as genetic algorithms (GA) and discrete-particle swarm optimization (discrete-PSO).
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