Dolphin swarm algorithm

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, gen...

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Vydané v:	Frontiers of information technology & electronic engineering Ročník 17; číslo 8; s. 717 - 729
Hlavní autori:	Wu, Tian-qi, Yao, Min, Yang, Jian-hua
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Hangzhou Zhejiang University Press 01.08.2016 Springer Nature B.V
Predmet:	Algorithms Ant colony optimization Benchmarking Benchmarks Communications Engineering Computer Hardware Computer Science Computer Systems Organization and Communication Networks Convergence Dolphins Electrical Engineering Electronics and Microelectronics Genetic algorithms Instrumentation Networks Optimization Optimization algorithms Particle swarm optimization Phases Problem solving Search algorithms Swarm intelligence 优化问题海豚生活习惯生物学特性粒子群优化算法群体智能群算法遗传算法 Swarm intelligence TP391 Dolphin Optimization Bio-inspired algorithm
ISSN:	2095-9184, 2095-9230
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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.
AbstractList	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. 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.
Author	Tian-qi WU Min YAO Jian-hua YANG
AuthorAffiliation	School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
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Cites_doi	10.1007/s11721-007-0002-0 10.7551/mitpress/3927.001.0001 10.1016/j.ins.2010.07.005 10.1504/IJBIC.2011.038700 10.1007/BF00175354 10.1016/j.eswa.2011.09.076 10.1109/MHS.1995.494215 10.1007/s11721-007-0004-y 10.1007/s10462-012-9328-0 10.1016/j.eswa.2011.07.123 10.1093/oso/9780195131581.001.0001 10.1109/3477.484436 10.1007/s11721-010-0040-x 10.1109/4235.771163 10.1007/s10898-007-9149-x
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Notes	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. Swarm intelligence; Bio-inspired algorithm; Dolphin; Optimization 33-1389/TP ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
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SubjectTerms	Algorithms Ant colony optimization Benchmarking Benchmarks Communications Engineering Computer Hardware Computer Science Computer Systems Organization and Communication Networks Convergence Dolphins Electrical Engineering Electronics and Microelectronics Genetic algorithms Instrumentation Networks Optimization Optimization algorithms Particle swarm optimization Phases Problem solving Search algorithms Swarm intelligence 优化问题海豚生活习惯生物学特性粒子群优化算法群体智能群算法遗传算法
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Title	Dolphin swarm algorithm
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