海豚群算法

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

doi:10.1631/FITEE.1500287

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海豚群算法

常规文章 | Updated：2022-05-19

- 海豚群算法
- Dolphin swarm algorithm
- 信息与电子工程前沿（英文） 2016年17卷第8期页码：717-729
- Affiliations：
  
  School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
- Author bio：
  
  E-mail:TainchWu@gmail.com
- Funds：
  
  Project supported by the National Key Technology R&D Program of China (No. 2014BAD10B02)
- DOI：10.1631/FITEE.1500287
  中图分类号： TP391
- 收稿：2015-09-04，
  
  录用：2016-5-15，
  
  纸质出版：2016-08
- Accepted：
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海豚群算法[J]. 信息与电子工程前沿（英文）, 2016,17(8):717-729.

Tian-qi WU, Min YAO, Jian-hua YANG. Dolphin swarm algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(8): 717-729.
海豚群算法[J]. 信息与电子工程前沿（英文）, 2016,17(8):717-729. DOI： 10.1631/FITEE.1500287.

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.

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