| 自适应混沌果蝇优化算法 |
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| 引用本文： | 韩俊英,刘成忠.自适应混沌果蝇优化算法[J].计算机应用,2013,33(5):1313-1333. |
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| 作者姓名： | 韩俊英 刘成忠 |
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| 作者单位： | 甘肃农业大学 信息科学技术学院，兰州 730070 |
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| 基金项目： | 甘肃省科技支撑计划资助项目;甘肃省教育厅科研基金资助项目 |
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| 摘 要： | 本文针对基本果蝇优化算法(FOA)寻优精度不高和易陷入局部最优的缺点，融入混沌算法对果蝇优化算法的进化机制进行优化，提出自适应混沌果蝇优化算法(ACFOA)。在算法处于收敛状态时，应用混沌算法进行全局寻优，从而跳出局部极值而继续优化。对几种经典测试函数的仿真结果表明，ACFOA算法具有更好的全局搜索能力，在收敛速度、收敛可靠性及收敛精度上均比基本FOA算法有较大的提高。
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| 关 键 词： | 自适应 混沌 果蝇优化 适应度 |
| 收稿时间： | 2012-12-03 |
| 修稿时间： | 2013-01-15 |
Adaptive Chaos Fruit Fly Optimization Algorithm |
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| HAN Junying LIU Chengzhong.Adaptive Chaos Fruit Fly Optimization Algorithm[J].journal of Computer Applications,2013,33(5):1313-1333. |
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| Authors: | HAN Junying LIU Chengzhong |
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| Affiliation: | School of Information Science and Technology, Gansu Agricultural University, Lanzhou Gansu 730070, China |
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| Abstract: | In order to overcome the problems of low convergence precision and easily relapsing into local extremum in basic Fruit Fly Optimization Algorithm(FOA), by introducing the chaos algorithm into the evolutionary process of basic FOA, an improved FOA called Adaptive Chaos FOA (ACFOA)is proposed. In the condition of local convergence, chaos algorithm is applied to search the global optimum in the outside space of convergent area and to jump out of local extremum and continue to optimize. Experimental results show that the new algorithm has the advantages of better global searching ability, speeder convergence and more precise convergence. |
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| Keywords: | Adaptive Chaos Fruit Fly Optimization Algorithm Fitness |
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