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EBCRO: Hybrid chemical reaction with employed bee operator

机译:EBCRO:与受雇的养蜂人进行混合化学反应

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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,它将化学反应优化算法(CRO)的本地搜索算子与人工蜂群算法(ABC)的改进的采用的蜜蜂算子(EB)的主要部分相结合,并引入了基于概率的选择方法分子方法。该算法不仅通过CRO的本地搜索操作,而且通过EB的机制创建了新的解决方案。 CRO是一种新的,有效的群体智能优化算法。但是,由于CRO的随机性,并且没有利用其他解决方案信息,因此其搜索能力和收敛速度很低。作为ABC一部分的EB具有简单和高效的全局搜索功能,但是该解决方案仅由其他解决方案信息更新,从而导致缺乏本地搜索能力。因此,本文提出了一种EBCRO,它可以平衡数值函数优化算法的开发和探索。在这项研究中,对EBCRO进行了23个基准数值函数的测试,并且EBCRO,CRO,正交化学反应优化(OCRO),混合粒子群和化学反应优化(HP-CRO2)和ABC的仿真结果表明,EBCRO的性能优于其他收敛性能的算法。

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