计算机科学 ›› 2021, Vol. 48 ›› Issue (10): 67-76.doi: 10.11896/jsjkx.200800128
田梦丹1, 梁晓磊1, 符修文2, 孙媛1, 李章洪1
TIAN Meng-dan1, LIANG Xiao-lei1, FU Xiu-wen2, SUN Yuan1, LI Zhang-hong1
摘要： 针对粒子群算法在求解复杂多峰函数时存在早熟、易陷入局部最优、全局收敛性能差等缺陷,考虑种群结构、多模式学习和个体间博弈等因素,提出了具有博弈概率选择的多子群粒子群算法。该算法从改善群体多样性、提升个体搜索能力的角度出发,构建了动态多种群结构,并针对每个子群构建不同的学习策略(极端学习、复合学习、邻域学习和随机学习),子群间进行最优信息共享,形成异构多子群的多源学习方式;将进化博弈思想引入群体搜索过程中,个体通过收益矩阵和扎根概率进行策略概率选择,进入适合个体能力提升的子群进行学习。基于12个标准测试函数,针对算法中重要参数子群规模L的取值进行了组合实验,结果表明L取值N/2或N/3时,种群适应度分布及中位值具有明显优势;针对算法性能测试,利用不同维度下的标准测试函数与7种同类型算法进行对比实验,实验结果显示,改进算法在最优值、求解稳定性及收敛特征上整体优于对比算法,说明多源学习和博弈概率选择策略可以有效改善粒子群算法的性能。
中图分类号:
| [1]KENNEDY J,EBERHART R.Particle swarm optimization[C]//Proceedings of IEEE International Conference on Neural Networks.1995:1942-1948. [2]MENG J,LI X Y,DING X Y.Optimal allocation of distributed generation based on improved adaptive weight multi-objective particle swarm optimization[J].Journal of Electric Power Science and Technology,2020,35(2):55-60,106. [3]XU H T,JI W D,SUN X Q,et al.A PSO algorithm with inertia weight decay by normal distribution[J].Journal of Shenzhen University (Science and Technology Edition),2020,37(2):208-213. [4]QI W,YU H Q,FAN G S,et al.WSN Coverage Optimization Based on Adaptive Particle Swarm Optimization[J].Computer Science,2020,47(7):243-249. [5]LI H J,ZHANG P W,GUO H D.Adaptive multi-objective particle swarm optimization algorithm based on population Manhattan distance[J].Computer Integrated Manufacturing Systems,2020,26(4):1019-1032. [6]ZHOU R,LI J,WANG H.Reverse Learning Particle SwarmOptimization Based on Grey Wolf Optimization[J].Computer Engineering and Applications,2020,56(7):48-56. [7]LIU H,CAI Z,WANG Y.Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimmization[J].Applied Soft Computing,2010,10(2):629-640. [8]CHEN M R,LI X,ZHANG X,et al.A novel particle swarm optimizer hybridized with extremal optimization[J].Applied Soft Computing,2010,10(2):367-373. [9]LUO Q,JI W D,XU H T,et al.Particle Swarm Optimization with Global Best Particle Dimension-by-dimension Mutation[J].Journal of Chinese Computer Systems,2020,41(2):259-263. [10]LI J,LUO Y K,LI B,et al.Differential Hybrid Particle Swarm Optimization Algorithm Based on Different Dimensional Variation[J].Computer Science,2018,45(5):208-214. [11]ZHOU L P,SHI Y B,ZHANG W.Improved particle swarm optimization with leadership[J].Control and Decision,2010,25(10):1463-1468. [12]CHEN C C.Two-layer particle swarm optimization for unconstrained optimization problems[J].Applied Soft Computing,2010,11(1):295-304. [13]MOJTABA G,EBRAHIM A,ABOLFAZL R,et al.Phasor particle swarm optimization:a simple and efficient variant of PSO[J].Soft Computing,2019,23(19):9701-9718. [14]WAN Z,WANG G,SUN B.A hybrid intelligent algorithm by combining particle swarm optimization with chaos searching technique for solving nonlinear believe programming problems[J].Swarm and Evolutionary Computation,2013,8:26-32. [15]KALAIVAANI P T,AKILA T,TAHIR M M,et al.A novel intelligent approach to simulate the blast-induced flyrock based on RFNN combined with PSO[J].Engineering with Computers,2020,36(2):0177-0667. [16]TANG B W,XIANG K,PANG M Y.An integrated particles swarm optimization approach hybridizing a new self-adaptiveparticle swarm optimization with a modified differential evolution[J].Neural Computing and Applications,2020,32(2):4849-4883. [17]ZHENG Q X,GU X H,ZHANG H M.K-Means Cluster Algorithm Based on Improved PSO[J].Computer Engineering and Applications,2008,54(13):131-136. [18]LIANG J,QIN A,SUGANTHAN P N,et al.Comprehensive learning particle swarm optimizer for global optimization of multimodal functions[J].IEEE Transactions on Evolutionary Computation,2006,10(3):281-295. [19]SIMON D.Biogeography-Based Optimization[J].IEEE Transactions on Evolutionary Computation,2008,12(6):702-713. [20]FISTER I,FISTER J R I,YANG X,et al.A comprehensive review of firefly algorithms[J].Swarm and Evolutionary Computation,2013,13:34-46. [21]YANG X,GANDOMI A H.Bat algorithm:a novel approach for global engineering optimization[J].Engineering Computation,2012,29(5):464-483. [22]YANG X,DEB S.Engineering optimisation by cuckoo search[J].International Journal of Mathematical Modelling and Numerical Optimisation,2010,1(4):330-343. [23]BHOSKAR T,KULKARNI O,KULKARNI N K,et al.Genetic Algorithm and its Applications to Mechanical Engineering:A Review[J].Materials Today:Proceedings,2015,2(4/5):2624-2630. [24]QIAN W Y,LI A J.Adaptive differential evolution algorithm for multiobjective optimization problems[J].Applied Mathematics and Computation,2007,201(1):431-440. |
| [1] | 徐汝利, 黄樟灿, 谢秦秦, 李华峰, 湛航. 基于金字塔演化策略的彩色图像多阈值分割 Multi-threshold Segmentation for Color Image Based on Pyramid Evolution Strategy 计算机科学, 2022, 49(6): 231-237. https://doi.org/10.11896/jsjkx.210300096 |
| [2] | 周天清, 岳亚莉. 超密集物联网络中多任务多步计算卸载算法研究 Multi-Task and Multi-Step Computation Offloading in Ultra-dense IoT Networks 计算机科学, 2022, 49(6): 12-18. https://doi.org/10.11896/jsjkx.211200147 |
| [3] | 邱旭, 卞浩卜, 吴铭骁, 朱晓荣. 基于5G毫米波通信的高速公路车联网任务卸载算法研究 Study on Task Offloading Algorithm for Internet of Vehicles on Highway Based on 5G MillimeterWave Communication 计算机科学, 2022, 49(6): 25-31. https://doi.org/10.11896/jsjkx.211100198 |
| [4] | 李晓东, 於志勇, 黄昉菀, 朱伟平, 涂淳钰, 郑伟楠. 面向河道环境监测的群智感知参与者选择策略 Participant Selection Strategies Based on Crowd Sensing for River Environmental Monitoring 计算机科学, 2022, 49(5): 371-379. https://doi.org/10.11896/jsjkx.210200005 |
| [5] | 孙振强, 罗永龙, 郑孝遥, 章海燕. 一种融合用户情感与相似度的智能旅游路径推荐方法 Intelligent Travel Route Recommendation Method Integrating User Emotion and Similarity 计算机科学, 2021, 48(6A): 226-230. https://doi.org/10.11896/jsjkx.200900119 |
| [6] | 刘炜, 李东坤, 徐畅, 田钊, 佘维. 应急通信网络中基于粒子群优化的信道分配算法 Channel Assignment Algorithm Based on Particle Swarm Optimization in Emergency Communication Networks 计算机科学, 2021, 48(5): 277-282. https://doi.org/10.11896/jsjkx.200400042 |
| [7] | 栾凌, 潘连武, 闫雷, 武小琳. 基于边缘计算的输变电工程全环节单元确认的精准造价智能管控技术研究 Research on Intelligent Control Technology of Accurate Cost for Unit Confirmation in All Links of Power Transmission and Transformation Project Based on Edge Computing 计算机科学, 2021, 48(11A): 688-692. https://doi.org/10.11896/jsjkx.201100200 |
| [8] | 张天瑞, 魏铭琦, 高秀秀. 基于IPSO-WRF的选择性激光烧结件气泡溶解时间预测模型 Prediction Model of Bubble Dissolution Time in Selective Laser Sintering Based on IPSO-WRF 计算机科学, 2021, 48(11A): 638-643. https://doi.org/10.11896/jsjkx.210300080 |
| [9] | 汤洪涛, 闫伟杰, 陈青丰, 鲁建厦, 詹燕. 自动化立体仓库货位分配与作业调度集成优化 Integrated Optimization of Location Assignment and Job Scheduling in Automated Storage andRetrieval System 计算机科学, 2020, 47(5): 204-211. https://doi.org/10.11896/jsjkx.190400042 |
| [10] | 李宝胜, 秦传东. 基于粒子群优化的SVM多分类的电动车价格预测研究 Study on Electric Vehicle Price Prediction Based on PSO-SVM Multi-classification Method 计算机科学, 2020, 47(11A): 421-424. https://doi.org/10.11896/jsjkx.191200132 |
| [11] | 孟利民, 王锟, 郑增乾, 蒋维. 基于粒子群算法的D2D内容边缘缓存架构策略 Architecture Strategy of D2D Content Edge Cache Based on Particle Swarm Optimization 计算机科学, 2020, 47(11A): 345-348. https://doi.org/10.11896/jsjkx.200500079 |
| [12] | 王改云, 王磊杨, 路皓翔. 基于混合群智能算法优化的RSSI质心定位算法 RSSI-based Centroid Localization Algorithm Optimized by Hybrid Swarm Intelligence Algorithm 计算机科学, 2019, 46(9): 125-129. https://doi.org/10.11896/j.issn.1002-137X.2019.09.017 |
| [13] | 张娜,滕赛娜,吴彪,包晓安. 基于粒子群优化算法的测试用例生成方法 Test Case Generation Method Based on Particle Swarm Optimization Algorithm 计算机科学, 2019, 46(7): 146-150. https://doi.org/10.11896/j.issn.1002-137X.2019.07.023 |
| [14] | 胡鑫楠. 基于改进型混沌粒子群优化算法的FIR高通数字滤波器设计 FIR High Pass Digital Filter Design Based on Improved Chaos Particle Swarm Optimization Algorithm 计算机科学, 2019, 46(6A): 601-604. |
| [15] | 邵炜晖, 许维胜, 徐志宇, 王宁, 农静. 基于改进粒子群算法的电动汽车停车场V2G策略研究 Research of V2G Strategies for EV Parking Lot Based on Improved PSO 计算机科学, 2018, 45(11A): 92-96. |
|
||
首页