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首页> 外文期刊>The international arab journal of information technology >Enhanced Constrained Artificial Bee Colony Algorithm for Optimization Problems
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Enhanced Constrained Artificial Bee Colony Algorithm for Optimization Problems

机译：优化问题的增强约束人工蜂群算法

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摘要

Artificial Bee Colony (ABC) algorithm is a relatively new swarm intelligence algorithm that has attracted great deal of attention from researchers in recent years with the advantage of less control parameters and strong global optimization ability. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. This drawback can be even more significant when constraints are also involved. To address this issue, an Enhanced Constrained ABC algorithm (EC-ABC) is proposed for Constrained Optimization Problems (COPs) where two new solution search equations are introduced for employed bee and onlooker bee phases respectively. In addition, both chaotic search method and opposition-based learning mechanism are employed to be used in population initialization in order to enhance the global convergence when producing initial population. This algorithm is tested on several benchmark functions where the numerical results demonstrate that the EC-ABC is competitive with state of the art constrained ABC algorithm.
机译：人工蜂群算法（ABC）是一种相对较新的群体智能算法，其近年来以其较少的控制参数和强大的全局优化能力受到了研究人员的关注。但是，ABC的解搜索方程仍存在不足之处，它善于探索，但善于利用。当还涉及约束时，此缺点可能更加明显。为了解决此问题，针对约束优化问题（COP）提出了一种增强约束ABC算法（EC-ABC），其中针对所用蜜蜂和旁观蜂阶段分别引入了两个新的解决方案搜索方程。此外，混沌搜索方法和基于对立的学习机制都用于种群初始化，以增强初始种群产生时的全局收敛性。该算法在几个基准函数上进行了测试，数值结果表明EC-ABC与最新的受限ABC算法相比具有竞争力。

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