| 引用本文： |
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赵汝鑫,罗淇芳,周永权.具有差分进化算子的社会蜘蛛群优化算法[J].广西科学,2017,24(3):247-257. [点击复制]
- ZHAO Ruxin,LUO Qifang,ZHOU Yongquan.Differential Mutation Operator—Based Social Spider Optimization Algorithm[J].Guangxi Sciences,2017,24(3):247-257. [点击复制]
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| 摘要: |
| [目的]社会蜘蛛群优化算法(SSO)是一种新颖的元启发式优化算法,自从它被提出之后就受到该领域学者的广泛关注,并且也被成功应用到许多领域。但是由于社会蜘蛛群优化算法还处在算法的研究初期,该算法的收敛速度与收敛精度还需要进一步提高。[方法]将差分进化算子引入到社会蜘蛛群优化算法(SSO-DM)中,并将改进的算法应用于函数优化问题中,通过5个标准测试函数来验证基于差分进化算子的社会蜘蛛群优化算法(SSO-DM)的优化性能。[结果]差分进化算子增强了社会蜘蛛群优化算法的收敛速度与收敛精度。[结论]本研究中所提出的算法能够获得精确解,并且它也具有较快的收敛速度和较高的算法稳定性。 |
| 关键词: 社会蜘蛛群优化算法 差分进化算子 元启发式优化算法 函数优化 |
| DOI：10.13656/j.cnki.gxkx.20170601.001 |
| 投稿时间：2017-04-03修订日期：2017-05-25 |
| 基金项目:国家自然科学基金项目(61463007,61563008)和广西自然科学基金项目(2016GXNSFAA380264)资助。 |
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| Differential Mutation Operator—Based Social Spider Optimization Algorithm |
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ZHAO Ruxin1,2, LUO Qifang1,2, ZHOU Yongquan1,2
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(1. College of Information Science and Engineering, Guangxi University for Nationalities, Nanning, Guangxi, 530006, China;2.
2. Guangxi Higher School Key Laboratories of Complex Systems and Intelligent Computing, Nanning, Guangxi, 530006, China) |
| Abstract: |
| [Objective] A social-spider optimization algorithm (SSO) is a novel meta-heuristic optimization algorithm, it has been widely concerned by scholars in this field since it was put forward, and it had been successfully applied in many fields, but the algorithm is still in the early stages of the study, the convergence speed and computational accuracy of the algorithm need to be improved.[Methods] In order to enhance the convergence speed and computational accuracy of the algorithm, in this paper, a social-spider optimization algorithm with differential mutation operator (SSO-DM) had been proposed, and was applied to the function optimization problem. The improvement involved differential mutation operator. A social-spider optimization algorithm with differential mutation operator (SSO-DM) was validated by five benchmark functions.[Results] Differential mutation operator enhanced the convergence speed and computational accuracy of the algorithm.[Conclusion] The results showed that the proposed algorithm was able to obtain accurate solution, and it also had a fast convergence speed and a high degree of stability. |
| Key words: social-spider optimization algorithm differential mutation operator meta-heuristic optimization algorithm functions optimization |
