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A Modified Artificial Bee Colony Algorithm for Function Optimization Strategy

机译:一种用于功能优化策略的修改人工蜂殖民地算法

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Artificial Bee Colony (ABC) algorithm has received wide attention due to the outstanding performance in solving complex optimization problems. However, the further application of ABC is still hindered due to some bottlenecks, such as slow convergence velocity, poor global search ability and low accuracy of solution. To overcome these problems, an improved ABC (CT-ABC) is proposed in this paper by using both a chaotic map and a tolerance based search equation (TSE). Some standard test functions are chosen to verify the performance by implementing the proposed modified ABC algorithm, basic ABC and another modified ABC (WABC). Simulation results reveal the proposed CT-ABC algorithm is able to obtain the faster convergence speeds and better accurate approximate solution.
机译:由于解决复杂优化问题的出色表现,人造蜂殖民地(ABC)算法已受到广泛的关注。然而,由于一些瓶颈,诸如慢的收敛速度,较差的全球搜索能力和低的解决方案精度,因此仍然阻碍了ABC的进一步应用。为了克服这些问题,通过使用混沌图和基于公差的搜索方程(TSE),在本文中提出了一种改进的ABC(CT-ABC)。选择一些标准测试功能以通过实现所提出的修改的ABC算法,基本ABC和另一个修改的ABC(WABC)来验证性能。仿真结果显示所提出的CT-ABC算法能够获得更快的收敛速度和更好的准确近似解决方案。

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