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CLC number: TP391

On-line Access: 2016-08-05

Received: 2015-09-04

Revision Accepted: 2016-03-15

Crosschecked: 2016-07-19

Cited: 2

Clicked: 5002

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Tian-qi Wu

http://orcid.org/0000-0003-1547-3479

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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.8 P.717-729

http://doi.org/10.1631/FITEE.1500287


Dolphin swarm algorithm


Author(s):  Tian-qi Wu, Min Yao, Jian-hua Yang

Affiliation(s):  School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   TainchWu@gmail.com

Key Words:  Swarm intelligence, Bio-inspired algorithm, Dolphin, Optimization


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Tian-qi Wu, Min Yao, Jian-hua Yang. Dolphin swarm algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(8): 717-729.

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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.

The proposal of an optimisation algorithm based on the dolphin is very interesting.

海豚群算法

概要: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。
关键词:群体智能;仿生算法;海豚;优化

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