Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems

被引:0
作者
Seyedali Mirjalili
机构
[1] Griffith University,School of Information and Communication Technology
[2] Queensland Institute of Business and Technology,undefined
来源
Neural Computing and Applications | 2016年 / 27卷
关键词
Optimization; Multi-objective optimization; Constrained optimization; Binary optimization; Benchmark; Swarm intelligence; Evolutionary algorithms; Particle swarm optimization; Genetic algorithm;
D O I
暂无
中图分类号
学科分类号
摘要
A novel swarm intelligence optimization technique is proposed called dragonfly algorithm (DA). The main inspiration of the DA algorithm originates from the static and dynamic swarming behaviours of dragonflies in nature. Two essential phases of optimization, exploration and exploitation, are designed by modelling the social interaction of dragonflies in navigating, searching for foods, and avoiding enemies when swarming dynamically or statistically. The paper also considers the proposal of binary and multi-objective versions of DA called binary DA (BDA) and multi-objective DA (MODA), respectively. The proposed algorithms are benchmarked by several mathematical test functions and one real case study qualitatively and quantitatively. The results of DA and BDA prove that the proposed algorithms are able to improve the initial random population for a given problem, converge towards the global optimum, and provide very competitive results compared to other well-known algorithms in the literature. The results of MODA also show that this algorithm tends to find very accurate approximations of Pareto optimal solutions with high uniform distribution for multi-objective problems. The set of designs obtained for the submarine propeller design problem demonstrate the merits of MODA in solving challenging real problems with unknown true Pareto optimal front as well. Note that the source codes of the DA, BDA, and MODA algorithms are publicly available at http://www.alimirjalili.com/DA.html.
引用
收藏
页码:1053 / 1073
页数:20
相关论文
共 101 条
[1]  
Mirjalili S(2014)Grey wolf optimizer Adv Eng Softw 69 46-61
[2]  
Mirjalili SM(2011)Wolf-pack ( Behav Process 88 192-197
[3]  
Lewis A(1994)) hunting strategies emerge from simple rules in computational simulations Anim Behav 47 175-178
[4]  
Muro C(1989)Swarm location in zooplankton as an anti-predator defence mechanism Naturwissenschaften 76 579-581
[5]  
Escobedo R(1996)Self-organized shortcuts in the Argentine ant Syst Man Cybern Part B Cybern IEEE Trans 26 29-41
[6]  
Spector L(2007)Ant system: optimization by a colony of cooperating agents J Global Optim 39 459-471
[7]  
Coppinger R(2009)A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm Evolut Comput IEEE Trans 13 913-918
[8]  
Jakobsen PJ(2004)A survey of particle swarm optimization applications in electric power systems Eng Sci 5 87-94
[9]  
Birkeland K(2012)Survey on particle swarm optimization algorithm Expert Syst Appl 39 4618-4627
[10]  
Johnsen GH(2014)A survey: ant colony optimization based recent research and implementation on several engineering domain Artif Intell Rev 42 21-57