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EBCRO: Hybrid chemical reaction with employed bee operator

机译:EBCRO:杂种化学反应与采用蜂算子

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His article proposes a new hybrid algorithm EBCRO, which combines the local search operator of chemical reaction optimization algorithm (CRO) with modified employed bee operator (EB) main part of artificial bee colony algorithm (ABC), and the introduction of probability-based selection of molecular method. This algorithm creates new solutions not only by local search operation of CRO but also mechanisms of EB. CRO is a newly, effectively swarm intelligence optimization algorithm. However, owing to random characteristics of CRO and not make use of other solutions information, its search ability and convergence speed are inefficient. EB as a part of ABC has simplicity and efficiency global search ability, but the solution is updated only by other solutions information which leading to lack of local search ability. Therefore, this paper presents EBCRO, which can balance the exploitation and exploration of algorithm of numerical function optimization. In this study, EBCRO is tested on 23 benchmark numerical functions, and the simulation results of EBCRO, CRO, orthogonal chemical reaction optimization (OCRO), hybrid particle swarm and chemical reaction optimization (HP-CRO2), and ABC showed that EBCRO outperforms other algorithms in convergence performance.
机译:他的文章提出了一种新的混合算法EBCRO,其将化学反应优化算法(CRO)的本地搜索操作员与修改的采用的蜜蜂算法(EB)主要部分相结合,以及基于概率的选择引入分子法。该算法不仅通过CRO的本地搜索操作而且创造了新的解决方案,而且产生了EB的机制。 CRO是一种新的有效的群体智能优化算法。然而,由于CRO的随机特征而不是利用其他解决方案信息,其搜索能力和收敛速度效率低下。 EB作为ABC的一部分具有简单性和效率的全球搜索能力,但解决方案仅被其他解决方案信息更新,这导致缺乏本地搜索能力。因此,本文介绍了EBCro,这可以平衡数值函数优化算法的开发和探索。在本研究中,EBCRO在23个基准数值函数中测试,以及EBCRO,CRO,正交化学反应优化(OCRO),杂化粒子群和化学反应优化(HP-CRO2)的仿真结果显示,EBCRO优于其他收敛性能中的算法。

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