基于自适应调整权重和搜索策略的鲸鱼优化算法

doi:10.12068/j.issn.1005-3026.2020.01.007

东北大学学报:自然科学版 ›› 2020, Vol. 41 ›› Issue (1): 35-43.DOI: 10.12068/j.issn.1005-3026.2020.01.007

基于自适应调整权重和搜索策略的鲸鱼优化算法

孔芝，杨青峰，赵杰，熊浚钧

(东北大学秦皇岛分校控制工程学院，河北秦皇岛066004)

收稿日期:2019-07-16 修回日期:2019-07-16 出版日期:2020-01-15 发布日期:2020-02-01
通讯作者: 孔芝
作者简介:孔芝(1979-)，女，辽宁北镇人，东北大学秦皇岛分校副教授.
基金资助:
国家自然科学基金资助项目(61402088)；河北省自然科学基金资助项目(F2017501041)；中央高校基本科研业务费专项资金资助项目(N172304030).

Adaptive Adjustment of Weights and Search Strategies-Based Whale Optimization Algorithm

KONG Zhi， YANG Qing-feng， ZHAO Jie， XIONG Jun-jun

School of Control Engineering， Northeastern University at Qinhuangdao， Qinhuangdao 066004， China.

Received:2019-07-16 Revised:2019-07-16 Online:2020-01-15 Published:2020-02-01
Contact: KONG Zhi
About author:-
Supported by:
-

摘要/Abstract

摘要： 针对鲸鱼优化算法(WOA)收敛速度慢、收敛精度低、易陷入局部最优的问题，提出一种基于自适应调整权重和搜索策略的鲸鱼优化算法(AWOA).设计一种随着鲸鱼种群变化情况而自适应调整权重的方法，提高了算法的收敛速度; 设计一种自适应调整搜索策略，提高了算法跳出局部最优的能力.利用23个标准测试函数，分别针对高维和低维问题进行测试，仿真结果表明，AWOA在收敛精度和收敛速度方面总体上明显优于其他多种改进的鲸鱼优化算法.

关键词: 鲸鱼优化算法, 自适应调整权重, 自适应调整搜索策略, 函数优化, 全局优化

Abstract: The whale optimization algorithm(WOA) has slow convergence speed and low convergence accuracy and tends to fall into local optimum.In order to solve these problems， a whale optimization algorithm(AWOA) based on adaptive adjustment of weight and search strategy was proposed.An adaptive adjustment of weight with the current distribution of whale population was designed to improve the convergence speed of the algorithm， and an adaptive adjustment of search strategy was designed to improve the ability of the algorithm to jump out of local optimum.Using 23 standard test functions， the algorithm was tested for high-dimensional and low-dimensional problems， respectively.The simulation results showed that the AWOA is generally superior to other improved whale optimization algorithms in terms of convergence accuracy and convergence speed.

Key words: whale optimization algorithm； adaptive adjustment of weight； adaptive adjustment of search strategy, function optimization, global optimization

中图分类号:

TP301.6

孔芝，杨青峰，赵杰，熊浚钧. 基于自适应调整权重和搜索策略的鲸鱼优化算法[J]. 东北大学学报:自然科学版, 2020, 41(1): 35-43.

KONG Zhi， YANG Qing-feng， ZHAO Jie， XIONG Jun-jun. Adaptive Adjustment of Weights and Search Strategies-Based Whale Optimization Algorithm[J]. Journal of Northeastern University Natural Science, 2020, 41(1): 35-43.

参考文献

[1]Kennedy J，Eberhart R.Particle swarm optimization［C］//Proceeding of the IEEE International Conference on Neural Networks.Perth，1995:1942-1948.
[2]Dorigo M，Maniezzo V，Colorni A.Ant system:optimization by a colony of cooperating agents［J］.IEEE Transactions on Systems，Man，and Cybernetics，Part B:Cybernetics，1996，26(1):29-41.
[3]Biswas S，Kundu S，Das S.Inducing niching behavior in differential evolution through local information sharing［J］.IEEE Transactions on Evolutionary Computation，2015，19(2):246-263.
[4]Yang X S.Firefly algorithm，stochastic test functions and design optimization［J］.International Journal of Bio-Inspired Computation，2010，2(2):78-84.
[5]Yang X S.A new metaheuristic bat-inspired algorithm［C］//Nature Inspired Cooperative Strategies for Optimization.Berlin:Springer，2010:65-74.
[6]Mirjalili S，Mirjalili S M，Lewis A.Grey wolf optimizer［J］.Advances in Engineering Software，2014，69(7):46-61.
[7]Seyedali M，Andrew L.The whale optimization algorithm［J］.Advances in Engineering Software，2016，95:51-67.
[8]郭振洲，王平，马云峰，等.基于自适应权重和柯西变异的鲸鱼优化算法［J］.微电子学与计算机，2017，34(9):20-25.(Guo Zhen-zhou，Wang Ping，Ma Yun-feng，et al.Whale optimization algorithm based on adaptive weight and Cauchy mutation［J］.Microelectronics & Computer，2017，34(9):20-25.)
[9]龙文，蔡绍洪，焦建军，等.求解大规模优化问题的改进鲸鱼优化算法［J］.系统工程理论与实践，2017，37(11):2983-2994.(Long Wen，Cai Shao-hong，Jiao Jian-jun，et al.Improved whale optimization algorithm for large scale optimization problem［J］.System Engineering—Theory & Practice，2017，37(11):2983-2994.)
[10]王坚浩，张亮，史超，等.基于混沌搜索策略的鲸鱼优化算法［J］.控制与决策，2019，34(9):1893-1900.(Wang Jian-hao，Zhang Liang，Shi Chao，et al.Whale optimization algorithm based on chaotic search strategy［J］.Control and Decision，2019，34(9):1893-1900.)
[11]张永，陈锋.一种改进的鲸鱼优化算法［J］.计算工程，2018，44(3):208-213.(Zhang Yong，Chen Feng.A modified whale optimization algorithm［J］.Computer Engineering，2018，44(3):208-213.)
[12]黄清宝，李俊兴，宋春宁，等.基于余弦控制因子和多项式变异的鲸鱼优化算法［EB/OL］.(2018-11-13)［2018-12-03］.https://doi.org/10.13195/j.kzyjc.2018.0463.(Huang Qing-bao，Li Jun-xing，Song Chun-ning，et al.Whale optimization algorithm based on cosine control factor and polynomial mutation［EB/OL］.(2018-11-13)［2018-12-03］.https://doi.org/10.13195/j.kzyjc.2018.0463.)
[13]Jangir P，Trivedi I N，Bhesdadiya R H，et al.Training multi-layer perception in neural network using whale optimization algorithm［J］.Indian Journal of Science and Technology，2016，9(19):28-36.
[14]Khaled M，Samir S，Abdelghani B.Whale optimization algorithm based optimal reactive power dispatch:a case study of the Algerian power system［J］.Electric Power Systems Research，2018，163:696-705.
[15]Prakash D B，Lakshminarayana C.Optimal siting of capacitors in radial distribution network using whale optimization algorithm［J］.Alexandria Engineering Journal，2017，56(4):499-509.
[16]Wang G G，Suash D，Gao X Z，et al.A new metaheuristic optimisation algorithm motivated by elephant herding behaviour［J］.International Journal of Bio-Inspired Computation，2016，8(6):394-409.
[17]Mirjalili S.SCA:a sine cosine algorithm for solving optimization problems［J］.Knowledge-Based Systems，2016，96:120-133.
[15]关守平，房少纯.一种新型的区间-粒子群优化算法［J］.东北大学学报(自然科学版)，2012，33(10):1381-1384.(Guan Shou-ping，Fang Shao-chun.A new particle swarm optimization algorithm［J］.Journal of Northeastern University(Natural Science)，2012，33(10):1381-1384.)(上接第34页)
[8]Hyndman R J，Athanasopoulos G.Forecasting:principles and practice［M］.Melbourne:OTexts，2018.
[9]Jaccard P.Etude comparative de la distribution florale dans une portion des Alpes et des Jura［J］.Bulletin del la Société Vaudoise des Sciences Naturelles，1901，37:547-579.
[10]Adamic L A，Adar E.Friends and neighbors on the web［J］.Social Networks，2003，25(3):211-230.
[11]Holland P W，Leinhardt S.Transitivity in structural models of small groups［J］.Social Networks，1977，2(2):49-66.
[12]Cohen J.Applied multiple regression/correlation analysis for the behavioral sciences［M］.Mahwah:Lawrence Erlbaum Associates，2003.
[13]Ricardo P，Soares S，Prudencio R.Time series based link prediction［C］//International Joint Conference on Neural Networks.Brisbane，2012:1-7.
[14]Xu M，Yin Y C.A similarity index algorithm for link prediction［C］//2017 12th International Conference on Intelligent Systems and Knowledge Engineering(ISKE).Nanjing，2017:1-6.
[15]Zhou T，Lyu L Y，Zhang Y C.Predicting missing links via local information［J］.European Physical Journal B，2009，71(4):623-630.
[16]Jeh G，Widom J.SimRank:a measure of structural-context similarity［C］//Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining—KDD′02.Edmonton:ACM Press，2002:538-543.
[15]关守平，房少纯.一种新型的区间-粒子群优化算法［J］.东北大学学报(自然科学版)，2012，33(10):1381-1384.(Guan Shou-ping，Fang Shao-chun.A new particle swarm optimization algorithm［J］.Journal of Northeastern University(Natural Science)，2012，33(10):1381-1384.)

基于自适应调整权重和搜索策略的鲸鱼优化算法

Adaptive Adjustment of Weights and Search Strategies-Based Whale Optimization Algorithm

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 11

编辑推荐

Metrics

本文评价

[1]	陆志国, 王逍. 基于B样条与鲸鱼优化算法的机械臂轨迹规划[J]. 东北大学学报（自然科学版）, 2024, 45(5): 683-689.
[2]	赵俊涛，罗小川，刘俊秘. 改进鲸鱼优化算法在机器人路径规划中的应用[J]. 东北大学学报（自然科学版）, 2023, 44(8): 1065-1071.
[3]	崔雪婷，李颖，范嘉豪. 全局混沌蝙蝠优化算法[J]. 东北大学学报:自然科学版, 2020, 41(4): 488-492.
[4]	刘婷婷，张长胜，张斌，孙若男. 一种基于反馈策略的自适应选择人工蜂群算法[J]. 东北大学学报:自然科学版, 2015, 36(5): 618-622.
[5]	关守平;房少纯;. 一种新型的区间-粒子群优化算法[J]. 东北大学学报(自然科学版), 2012, 33(10): 1381-1384.
[6]	卢峰;高立群;. 基于改进粒子群算法的优化策略[J]. 东北大学学报(自然科学版), 2011, 32(9): 1221-1224.
[7]	葛延峰;金文静;高立群;冯达;. 多种群并行的自适应差分进化算法[J]. 东北大学学报(自然科学版), 2011, 32(4): 481-484.
[8]	卢峰;高立群;. 基于多种群的自适应差分进化算法[J]. 东北大学学报(自然科学版), 2010, 31(11): 1538-1541.
[9]	李鸿儒;李海龙;. 一种基于填充函数的神经网络全局优化算法[J]. 东北大学学报(自然科学版), 2007, 28(9): 1247-1249.
[10]	杨成祥;冯夏庭;刘红亮;王士民. 非线性位移时间序列分析模型的进化识别[J]. 东北大学学报(自然科学版), 2004, 25(5): 497-500.
[11]	张纪会;徐心和. 基于均匀网格的进化规划算法及其应用[J]. 东北大学学报:自然科学版, 1998, 19(6): 4--.