计算机科学 ›› 2017, Vol. 44 ›› Issue (10): 216-221.doi: 10.11896/j.issn.1002-137X.2017.10.039

• 人工智能 • 上一篇    下一篇

基于模拟退火的自适应水波优化算法

王万良,陈超,李笠,李伟琨   

  1. 浙江工业大学计算机科学与技术学院 杭州311023,浙江工业大学计算机科学与技术学院 杭州311023,浙江工业大学计算机科学与技术学院 杭州311023,浙江工业大学计算机科学与技术学院 杭州311023
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受“十二五”国家科技支撑计划基金项目(2012BAD10B01),国家自然科学基金项目(61379123)资助

Adaptive Water Wave Optimization Algorithm Based on Simulated Annealing

WANG Wan-liang, CHEN Chao, LI Li and LI Wei-kun   

  • Online:2018-12-01 Published:2018-12-01

摘要: 水波优化算法(Water Wave Optimization,WWO)是一种基于浅水波理论的新兴智能优化算法。在简化水波优化算法(Simplified Water Wave Optimization,SimWWO)的基础上,提出水波优化算法的一个改进版本。针对WWO算法在寻优过程中未能有效利用水波历史状态和经验的问题,提出一种自适应的参数调整策略:根据水波进化过程中的性能改善指标自适应调整算法的波长系数,提高搜索效率;同时,针对算法后期容易陷入局部最优的情况,加入模拟退火的思想,以一定的概率接受劣质解,避免算法陷入局部最优。通过以上两个操作可以更好地平衡全局搜索和局部搜索。在CEC 2015函数测试集上进行比较,结果证明改进后的算法有效地提高了综合性能。

关键词: 进化算法,水波优化,自适应参数,模拟退火

Abstract: Water wave optimization (WWO) is a novel evolutionary algorithm inspired by the shallow wave theory.In this paper,we developed a modified version of simplified water wave optimization algorithm (SimWWO).To fully utilize the history information and experience of the waves,we proposed an adaptive parameter adjustment strategy.The performance of waves on the evolutionary process is used as a feedback to adjust the wave length coefficient adaptively to improve search efficiency.Meanwhile,to avoid the problem of easily being lost in local optimum,the thought of simulated annealing is adopted to accept inferior solution with a certain probability.Through the above two operations,the algorithm achieves better balance between global search and local search.Computational experiments on the CEC 2015 single-objective optimization test problems show that the modified algorithm effectively improves the overall performance.

Key words: Evolutionary algorithms,Water wave optimization,Adaptive parameter,Simulated annealing

[1] WEN Y,PAN D Z.Improved Genetic Algorithm for Traveling Salesman Problem[J].Computer Science,2016,3(6):90-92.(in Chinese) 文艺,潘大志.用于求解TSP问题的改进遗传算法[J].计算机科学,2016,3(6):90-92.
[2] SUN Z L,LI X Y,WANG Y.Improved Simple Particle Swarm Optimization Algorithm[J].Computer Science,2015,2(11):86-88.(in Chinese) 孙振龙,李晓晔,王颖.一种改进的简化粒子群优化算法[J].计算机科学,2015,2(11):86-88.
[3] DORIGO M,BIRATTARI M,STTZLE T.Ant colony optimi- zation[J].Computational Intelligence Magazine IEEE,2006,1(4):28-39.
[4] WOLPERT D H,MACREADY W G.No free lunch theorems for optimization[J].IEEE Transactions on Evolutionary Computation,1997,1(1):67-82.
[5] YANG X,HOSSEIN GANDOMI A.Bat algorithm:a novel approach for global engineering optimization[J].Engineering Computations,2012,29(5):464-483.
[6] RASHEDI E,NEZAMABADI-POUR H,SARAZDI S.GSA:agravitational search algorithm[J].Information Sciences,2009,179(13):2232-2248.
[7] OFTADEH R,MAHJOOB M J,S HARIATPANAHI M.A novel meta-heuristic optimization algorithm inspired by group hunting of animals:Hunting search[J].Computers & Mathematics with Applications,2010,60(7):2087-2098.
[8] SIMON D.Biogeography-based optimization[J].IEEE Transactions on Evolutionary Computation,2008,12(6):702-713.
[9] ZHENG Y J.Water wave optimization:a new nature-inspiredmetaheuristic[J].Computers & Operations Research,2015,55:1-11.
[10] WU X B,LIAO J,WANG Z C.Water Wave Optimization for the Traveling Salesman Problem[M]∥ Inteligent Computing Theories and Methodolgies.Springer International Publishing,2015:137-146.
[11] YANG F,HU C P,YAN X F.Particle swarm opti mization algorithm of self-adaptive parameter based on ant system and its application[J].Control Theory & Applications,2010,27(11):1479-1488.(in Chinese) 杨帆,胡春平,颜学峰.基于蚁群系统的参数自适应粒子群算法及其应用[J].控制理论与应用,2010,27(11):1479-1488.
[12] YANG X,YUAN J,et al.A modified particle swarm optimizer with dynamic adaptation[J].Applied Mathematics and Computation,2007,189(2):1205-1213.
[13] PANIGRAHI B K,PANDI V R,DAS S.Adaptive particleswarm optimization approach for static and dynamic economic load dispatch[J].Energy Conversion and Management,2008,49(6):1407-1415.
[14] NICKABADI A,EBADZADEH M M,SAFABAKHSH R.Anovel particle swarm optimization algorithm with adaptive inertia weight[J].Applied Soft Computing,2011,11(4):3658-3670.
[15] TANWEER M R,SURESH S,S UNDARARAJAN N.Self regulating particle swarm optimization algorithm[J].Information Sciences,2015,4(10):182-202.
[16] GAO Y,XIE S L.Particle swarm optimization algorithm based on Simulated annealing [J].Computer Engineering and Applications,2004,40(1):47-50.(in Chinese) 高鹰,谢胜利.基于模拟退火的粒子群优化算法[J].计算机工程与应用,2004,40(1):47-50.
[17] LIU A J,YANG Y,LI F,et al.Chaotic simulated annealing par- ticle swarm optimization algorithm research and its application[J].Journal of Zhejiang University (Engineering Science),2013,47(10):1722-1730.(in Chinese) 刘爱军,杨育,李斐,等.混沌模拟退火粒子群优化算法研究及应用[J].浙江大学学报(工学版),2013,47(10):1722-1730.
[18] DAI M,TANG D,GIRET A,et al.Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm[J].Robotics and Computer-Integrated Manufacturing,2013,29(5):418-429.
[19] ZHENG Y,ZHANG B.A simplified water wave optimization algorithm[C]∥2015 IEEE Congress on Evolutionary Computation (CEC).IEEE,2015:807-813.
[20] DOWSLAND K A,THOMPSON J M.Simulated annealing[M].Handbook of Natural Computing,Springer,2012:1623-1655.
[21] LIANG J J,QU B Y,SUGANTHAN P N,et al.Problem definitions and evaluation criteria for the CEC 2015 competition on learning-based real-parameter single objective optimization[R].Computational Intelligence Laboratory,2014.
[22] ZHANG B,ZHENG Y J.Convergence Analysis of Water Wave Optimization Algorithm [J].Computer Science,2016,43(4):41-44.(in Chinese) 张蓓,郑宇军.水波优化算法收敛性分析[J].计算机科学,2016,43(4):41-44.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!