Dolphin swarm algorithm

Tian-qi WU; Min YAO; Jian-hua YANG

doi:10.1631/FITEE.1500287

Your Location：

Home >

Browse articles >

Dolphin swarm algorithm

Regular Papers | Updated：2022-05-19

- Dolphin swarm algorithm
- 海豚群算法
- Frontiers of Information Technology & Electronic Engineering Vol. 17, Issue 8, Pages: 717-729(2016)
- Affiliations：
  
  School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
- Author bio：
  
  E-mail:TainchWu@gmail.com
- Funds：
- DOI：10.1631/FITEE.1500287
  CLC： TP391
- Received：04 September 2015，
  
  Accepted：15 May 2016，
  
  Published：2016-08
- Accepted：
Scan QR Code
Tian-qi WU, Min YAO, Jian-hua YANG. Dolphin swarm algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(8): 717-729.
DOI：

Tian-qi WU, Min YAO, Jian-hua YANG. Dolphin swarm algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(8): 717-729. DOI： 10.1631/FITEE.1500287.

摘要

群体智能算法采取分布式解决问题的策略，已成功应用于很多传统算法难以解决的优化问题。目前已有粒子群算法、遗传算法、蜂群算法、蚁群算法等已经成功实现且效果良好的算法，但在优化对象日益复杂的今天，这些算法越来越难以满足人们对精度和时间的要求，而改进这些算法所带来的收益也越来越低。在这种情况下，设计一种新的算法来更好地解决优化问题变得越来越有意义。海豚有很多值得关注的生物特性和生活习性，如回声定位、信息交流、合作分工等。通过将这些生物特性和生活习性与群体智能的思想结合起来，引入优化问题中，我们提出了一种新的算法——海豚群算法，并给出了算法的相关定义，详细阐述了算法中搜寻、呼叫、接受、捕猎四个关键阶段。为了验证海豚群算法的效果，使用了10个性质各异的基准函数对海豚群算法以及粒子群算法、遗传算法、蜂群算法进行实验，并将4个函数的收敛速度和基准函数结果进行比较。实验结果表明，海豚群算法在大多数情况下，特别是在低维单峰函数、高维多峰函数、步长函数、带随机变量的函数中表现良好，具有收敛速度先慢后快、阶段性收敛、不易陷入局部最优、对基准函数具体性质没有要求等特点，尤其适用于适应度函数调用次数较多、使用个体较少的优化问题。

Abstract

By adopting the distributed problem-solving strategy

swarm intelligence algorithms have been successfully applied to many optimization problems that are difficult to deal with using traditional methods. At present

there are many well-implemented algorithms

such as particle swarm optimization

genetic algorithm

artificial bee colony algorithm

and ant colony optimization. These algorithms have already shown favorable performances. However

with the objects becoming increasingly complex

it is becoming gradually more difficult for these algorithms to meet human's demand in terms of accuracy and time. Designing a new algorithm to seek better solutions for optimization problems is becoming increasingly essential. Dolphins have many noteworthy biological characteristics and living habits such as echolocation

information exchanges

cooperation

and division of labor. Combining these biological characteristics and living habits with swarm intelligence and bringing them into optimization problems

we propose a brand new algorithm named the 'dolphin swarm algorithm' in this paper. We also provide the definitions of the algorithm and specific descriptions of the four pivotal phases in the algorithm

which are the search phase

call phase

reception phase

and predation phase. Ten benchmark functions with different properties are tested using the dolphin swarm algorithm

particle swarm optimization

genetic algorithm

and artificial bee colony algorithm. The convergence rates and benchmark function results of these four algorithms are compared to testify the effect of the dolphin swarm algorithm. The results show that in most cases

the dolphin swarm algorithm performs better. The dolphin swarm algorithm possesses some great features

such as first-slow-then-fast convergence

periodic convergence

local-optimum-free

and no specific demand on benchmark functions. Moreover

the dolphin swarm algorithm is particularly appropriate to optimization problems

with more calls of fitness functions and fewer individuals.

关键词

Keywords

references

E Bonabeau , , , M Dorigo , , , G Theraulaz . . Swarm Intelligence: from Natural to Artificial Systems . . Oxford University Press , , 1999 . . .

T Cura . . A particle swarm optimization approach to clustering . . Expert Syst. Appl. , , 2012 . . 39 ( ( 1 ): ): 1582 - - 1588 . . DOI: 10.1016/j.eswa.2011.07.123 http://doi.org/10.1016/j.eswa.2011.07.123 . .

M Dorigo , , , M Birattari . . Ant colony optimization. In: Sammut C, Webb, G.I. (Eds.), Encyclopedia of Machine Learning . . 2010 . . Springer . . 36 - - 39 . . DOI: 10.1007/978-0-387-30164-8_22 http://doi.org/10.1007/978-0-387-30164-8_22 . .

M Dorigo , , , V Maniezzo , , , A Colorni . . Ant system: optimization by a colony of cooperating agents . . IEEE Trans. Syst. Man Cybern. B , , 1996 . . 26 ( ( 1 ): ): 29 - - 41 . . DOI: 10.1109/3477.484436 http://doi.org/10.1109/3477.484436 . .

F Ducatelle , , , GA di Caro , , , LM Gambardella . . Principles and applications of swarm intelligence for adaptive routing in telecommunications networks . . Swarm Intell. , , 2010 . . 4 ( ( 3 ): ): 173 - - 198 . . DOI: 10.1007/s11721-010-0040-x http://doi.org/10.1007/s11721-010-0040-x . .

RC Eberhart , , , J Kennedy . . A new optimizer using particle swarm theory. Proc. 6th Int. Symp . . 1995 . . on Micro Machine and Human Science . . 39 - - 43 . . DOI: 10.1109/mhs.1995.494215 http://doi.org/10.1109/mhs.1995.494215 . .

RC Eberhart , , , YH Shi . . Particle swarm optimization: developments, applications and resources . . 2001 . . Proc. Congress on Evolutionary Computation . . 81 - - 86 . . DOI: 10.1109/CEC.2001.934374 http://doi.org/10.1109/CEC.2001.934374 . .

S Garnier , , , J Gautrais , , , G Theraulaz . . The biological principles of swarm intelligence . . Swarm Intell. , , 2007 . . 1 ( ( 1 ): ): 3 - - 31 . . DOI: 10.1007/s11721-007-0004-y http://doi.org/10.1007/s11721-007-0004-y . .

D Karaboga . . An Idea Based on Honey Bee Swarm for Numerical Optimization . . Technical Report-TR06, Erciyes University, Turkey , , 2005 . . .

D Karaboga , , , B Basturk . . A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm . . J. Glob. Optim. , , 2007 . . 39 ( ( 3 ): ): 459 - - 471 . . DOI: 10.1007/s10898-007-9149-x http://doi.org/10.1007/s10898-007-9149-x . .

D Karaboga , , , B Gorkemli , , , C Ozturk , , , 等 . . A comprehensive survey: artificial bee colony (ABC) algorithm and applications . . Artif. Intell. Rev. , , 2014 . . 42 ( ( 1 ): ): 21 - - 57 . . DOI: 10.1007/s10462-012-9328-0 http://doi.org/10.1007/s10462-012-9328-0 . .

J Kennedy . . Particle swarm optimization. In: Sammut C, Webb, G.I. (Eds.), Encyclopedia of Machine Learning . . 2010 . . Springer . . 760 - - 766 . . DOI: 10.1007/978-0-387-30164-8_630 http://doi.org/10.1007/978-0-387-30164-8_630 . .

M Mitchell . . An Introduction to Genetic Algorithms . . MIT Press , , 1998 . . .

BC Mohan , , , R Baskaran . . A survey: ant colony optimization based recent research and implementation on several engineering domains . . Expert Syst. Appl. , , 2012 . . 39 ( ( 4 ): ): 4618 - - 4627 . . DOI: 10.1016/j.eswa.2011.09.076 http://doi.org/10.1016/j.eswa.2011.09.076 . .

RS Parpinelli , , , HS Lopes . . New inspirations in swarm intelligence: a survey . . Int. J. Bio-inspired Comput. , , 2011 . . 3 ( ( 1 ): ): 1 - - 16 . . DOI: 10.1504/IJBIC.2011.038700 http://doi.org/10.1504/IJBIC.2011.038700 . .

R Poli , , , J Kennedy , , , T Blackwell . . Particle swarm optimization . . Swarm Intell. , , 2007 . . 1 ( ( 1 ): ): 33 - - 57 . . DOI: 10.1007/s11721-007-0002-0 http://doi.org/10.1007/s11721-007-0002-0 . .

M Saleem , , , GA di Caro , , , M Farooq . . Swarm intelligence based routing protocol for wireless sensor networks: survey and future directions . . Inform. Sci. , , 2011 . . 181 ( ( 20 ): ): 4597 - - 4624 . . DOI: 10.1016/j.ins.2010.07.005 http://doi.org/10.1016/j.ins.2010.07.005 . .

D Whitley . . A genetic algorithm tutorial . . Stat. Comput. , , 1994 . . 4 ( ( 2 ): ): 65 - - 85 . . DOI: 10.1007/BF00175354 http://doi.org/10.1007/BF00175354 . .

X Yao , , , Y Liu , , , GM Lin . . Evolutionary programming made faster . . IEEE Trans. Evol. Comput. , , 1999 . . 3 ( ( 2 ): ): 82 - - 102 . . DOI: 10.1109/4235.771163 http://doi.org/10.1109/4235.771163 . .

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

An optimized grey wolf optimizer based on a mutation operator and eliminating-reconstructing mechanism and its application^#

A swarm intelligence design based on a workshop of meta-synthetic engineering

HAPE3D—a new constructive algorithm for the 3D irregular packing problem*

Optimizing urban traffic control using a rational agent

Related Author

Zheng-feng MING

Xiao-qing ZHANG

Hui-yang QU

Chao RUAN

Jun-hua ZHOU

Guo-qiang SHI

Xiang ZHAI

Bao-cun HOU

Related Institution

School of Mechano-Electronic Engineering, Xidian University

School of Physics and Electronic Engineering, Xianyang Normal University

The Second Academy of China Aerospace Science and Technology Corporation

The State Key Laboratory of Intelligent Manufacturing System for Complex Products

Beijing Aerospace Intelligent Manufacturing Technology Development Co., Ltd.

Chat

Address：Zhejiang University Press, 148 Tianmushan Road, Hangzhou, China Postal code：310028
Tel：+86-571-88273162 Email：fitee@zju.edu.cn
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰