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首页> 外文期刊>Applied Soft Computing >Development and investigation of efficient artificial bee colony algorithm for numerical function optimization
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Development and investigation of efficient artificial bee colony algorithm for numerical function optimization

机译：数值函数优化的高效人工蜂群算法的研究与开发

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摘要

Artificial bee colony algorithm (ABC), which is inspired by the foraging behavior of honey bee swarm, is a biological-inspired optimization. It shows more effective than genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). However, ABC is good at exploration but poor at exploitation, and its convergence speed is also an issue in some cases. For these insufficiencies, we propose an improved ABC algorithm called I-ABC. In I-ABC, the best-so-far solution, inertia weight and acceleration coefficients are introduced to modify the search process. Inertia weight and acceleration coefficients are defined as functions of the fitness. In addition, to further balance search processes, the modification forms of the employed bees and the onlooker ones are different in the second acceleration coefficient. Experiments show that, for most functions, the I-ABC has a faster convergence speed and better performances than each of ABC and the gbest-guided ABC (GABC). But I-ABC could not still substantially achieve the best solution for all optimization problems. In a few cases, it could not find better results than ABC or GABC. In order to inherit the bright sides of ABC, GABC and I-ABC, a high-efficiency hybrid ABC algorithm, which is called PS-ABC, is proposed. PS-ABC owns the abilities of prediction and selection. Results show that PS-ABC has a faster convergence speed like I-ABC and better search ability than other relevant methods for almost all functions.
机译：受蜜蜂群觅食行为启发的人工蜂群算法（ABC）是一种受生物启发的优化方法。它比遗传算法（GA），粒子群优化（PSO）和蚁群优化（ACO）更有效。但是，ABC擅长勘探，却不善于开发，其收敛速度在某些情况下也是一个问题。针对这些不足，我们提出了一种改进的ABC算法，称为I-ABC。在I-ABC中，引入了迄今为止最好的解决方案，惯性权重和加速度系数来修改搜索过程。惯性权重和加速度系数定义为适应度的函数。另外，为了进一步平衡搜索过程，所采用的蜜蜂和旁观者的修改形式在第二加速度系数上是不同的。实验表明，对于大多数功能，I-ABC具有比ABC和gbest-guided ABC（GABC）更快的收敛速度和更好的性能。但是，I-ABC仍无法在根本上为所有优化问题实现最佳解决方案。在某些情况下，找不到比ABC或GABC更好的结果。为了继承ABC，GABC和I-ABC的优点，提出了一种高效的混合ABC算法，称为PS-ABC。 PS-ABC拥有预测和选择的能力。结果表明，对于几乎所有功能，PS-ABC都具有比I-ABC更快的收敛速度和更好的搜索能力。

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