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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

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Citations:  Bibtex RefMan EndNote GB/T7714


Tian-qi Wu


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


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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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.



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