| 异维学习人工蜂群算法* |
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| 引用本文： | 李冰,孙辉,赵嘉,王晖,吴润秀. 异维学习人工蜂群算法*[J]. 计算机应用研究, 2016, 33(4) |
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| 作者姓名： | 李冰 孙辉 赵嘉 王晖 吴润秀 |
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| 作者单位： | 南昌工程学院 信息工程学院、江西省水信息协同感知与智能处理重点实验室,南昌工程学院 信息工程学院、江西省水信息协同感知与智能处理重点实验室,南昌工程学院 信息工程学院、江西省水信息协同感知与智能处理重点实验室,南昌工程学院 信息工程学院、江西省水信息协同感知与智能处理重点实验室,南昌工程学院 信息工程学院、江西省水信息协同感知与智能处理重点实验室 |
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| 基金项目： | 国家自然科学基金(61261039)资助；江西省教育厅落地计划(KJLD13096)项目资助；江西省研究生创新专项资金资助(YC2014-S460)。 |
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| 摘 要： | 针对人工蜂群算法局部搜索能力弱及易陷入局部最优的缺点，提出了一种改进的人工蜂群算法。首先，雇佣蜂使用全局最优引导的搜索策略，且引导程度随个体试验次数()自适应减小，以此平衡算法的全局和局部搜索能力。其次，观察蜂采用变异的异维学习策略，使算法的搜索具有跳跃性，以提高跳出局部最优的概率。对8个经典基准测试函数和CEC2013中8个复合基准函数的测试结果表明，与多种最近提出的类似算法相比，新算法在收敛速度和解的精度均具有显著优势。
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| 关 键 词： | 人工蜂群算法；自适应；异维学习；全局探索；局部开发 |
| 收稿时间： | 2014-12-07 |
| 修稿时间： | 2016-02-20 |
Artificial Bee Colony Algorithm with different dimensional learning |
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| LI Bing,SUN Hui,ZHAO Ji,WANG Hui and WU Run-xiu. Artificial Bee Colony Algorithm with different dimensional learning[J]. Application Research of Computers, 2016, 33(4) |
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| Authors: | LI Bing SUN Hui ZHAO Ji WANG Hui WU Run-xiu |
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| Affiliation: | School of Information Engineering,Nanchang Institute of Technology,Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing,,School of Information Engineering,Nanchang Institute of Technology,Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing,School of Information Engineering,Nanchang Institute of Technology,Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing,School of Information Engineering,Nanchang Institute of Technology,Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing |
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| Abstract: | In order to overcome standard artificial bee colony algorithm poor at exploitation and easily getting into local minima, this paper proposed an improved artificial bee colony algorithm. Firstly, it utilized a global optimum guided strategy whose level of guidance adaptively decreasing with the number of trials for employed bees to achieve a tradeoff between exploration and exploitation. Secondly, it used a mutated strategy with different dimensional learning for onlookers, so the search of algorithm with jumping can improve the probability of escaping from the local minima.Through the experiment on 8 benchmark functions and 8 CEC2013 composition functions, the result show that the new algorithm performs significantly better than several recently proposed similar algorithms in terms of the convergence speed and the solution accuracy. |
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| Keywords: | artificial bee colony algorithm adaptively different dimensional learning exploration exploitation |
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