Multi-strategy gravitational search algorithm based on dynamic grouping

doi:10.3969/j.issn.1000-5641.2019.01.08

Abstract

Abstract: A multi-strategy gravitational search algorithm based on dynamic grouping is proposed in this paper. At the initial stage of the algorithm iteration, adaptive grouping strategies are used to optimize populations. Only the least-optimal individuals are updated in each group. The cloud model theory is used to improve the evolutionary behavior of the optimal individuals. In the later part of the algorithm iteration, the populations are divided into dominant and extension subgroups. The differential mutation operator is subsequently used to update the dominant subgroups to improve the precision and speed of the optimization. Tent chaos theory is used to update the extension subgroups to complete the individual variation. Typical complex function tests show that the algorithm has good convergence accuracy and computational speed.

Key words: gravitational search algorithm, cloud model, good point set, chaos, continuous space optimization

CLC Number:

TP301.6

ZHANG Qiang, WANG Mei. Multi-strategy gravitational search algorithm based on dynamic grouping[J]. Journal of East China Normal University(Natural Sc, 2019, 2019(1): 66-75.

References

[1] RASHEDI E, NEZAMABADI-POUr H, SARYAZDI S. GSA:A gravitational search algorithm[J]. Information Sciences, 2009, 179(13):2232-2248.
[2] 卫晓娟, 丁旺才, 李宁洲. 基于自适应混合引力搜索算法的混沌系统参数辨识[J]. 兰州大学学报(自然科学版), 2016, 52(3):410-416.
[3] LI P, DUAN H B. Path planning of unmanned aerial vehicle based on improved gravitational search algorithm[J]. Science China Technological Sciences, 2012, 55:2712-2719,
[4] BAHROLOLOUM A, NEZAMABADI-POUR H, BAHROLOLOUM H, et al. A prototype classifier based on gravitational search algorithm[J]. Applied Soft Computing, 2012, 12(2):819-825.
[5] 毕晓君, 刁鹏飞, 王艳娇. 求解动态优化问题的改进多种群引力搜索算法[J]. 中南大学学报(自然科学版), 2015, 46(9):3325-3331.
[6] 王宇, 黄胜, 廖全蜜. 基于引力搜索算法的船舶舱室布置方法[J]. 上海交通大学学报, 2016, 50(1):131-139.
[7] KAZAK N, DUYSAK A. Modified gravitational search algorithm[C]//International Symposium on Innovations in Intelligent Systems and Applications. IEEE, 2012:1-4.
[8] YAZDANI S, NEZAMABADI-POUR H, KAMYAB S. A gravitational search algorithm for multimodal optimization[J] Swarm and Evolutionary Computation, 2014, 14(2):1-14.
[9] GU B J, PAN F. Modified gravitational search algorithm with particle memory ability and its application[J] International Journal of Innovative Computing, Information and Control, 2013, 9:4531-4544.
[10] 张英杰, 龚中汉. 基于阈值统计学习的差分进化引力搜索算法[J]. 计算机研究与发展, 2014, 51(10):2187-2194.
[11] LI X T, YIN M H, MA Z Q. Hybrid differential evolution and gravitation search algorithm for unconstrained optimization[J]. International Journal of Physical Sciences, 2011, 6(25):5961-5981.
[12] 隋永霞, 孙合明. 基于高斯变异的引力搜索算法[J]. 江南大学学报(自然科学版), 2015, 14(5):596-600.
[13] 张维平, 任雪飞, 李国强, 等. 改进的万有引力搜索算法在函数优化中的应用[J]. 计算机应用, 2013, 33(5):1317-1320.
[14] 华罗庚, 王元. 数论在近代分析中的应用[M]. 北京:科学出版社, 1978:1-99.
[15] 刘香品, 宣士斌, 刘峰. 引入佳点集和猴群翻过程的人工蜂群算法[J]. 模式识别与人工智能, 2015, 28(1):80-89.
[16] 毕晓君, 张磊. 基于混合策略的双种群约束优化算法[J]. 控制与决策, 30(4):715-720.
[17] 俞志富, 李俊武, 王利华. 一种基于云模型和证据理论的融合识别方法[J]. 信息与控制, 2014, 43(1):30-36.
[18] 王庆龙, 王智学, 何红悦. 基于模糊-云模型的C~4ISR系统效能需求建模与分析方法[J]. 系统工程与电子技术, 2016, 38(9):2065-2071.
[19] SARKER R A, ELSAYED S M, RAY T. Differential evolution with dynamic parameters selection for optimization problems[J]. IEEE Transactions on Evolutionary Computation, 2014, 18(5):689-707.
[20] 李章维, 周晓根, 张贵军. 一种动态自适应差分进化算法[J]. 计算机科学, 2015, 42(6):52-56.
[21] 孔祥勇, 高立群, 欧阳海滨, 等. 求解大规模可靠性问题的改进差分进化算法[J]. 东北大学学报(自然科学版), 2014, 35(3):328-332.
[22] 刘振军. 结构全局优化设计的漏淹优化算法研究[D]. 辽宁大连:大连理工大学, 2016.
[23] 柏静. 基于多种混合策略的人工蜂群算法改进研究[D]. 济南:山东师范大学, 2016.