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

doi:10.12068/j.issn.1005-3026.2020.01.007

Abstract

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

CLC Number:

TP301.6

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.

References

[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.)