Cuckoo Search Algorithm

IF 2.1 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm Intelligence Pub Date : 2018-08-13 DOI:10.4324/9780429459719-9

Saowanee Asawaroj, Eugene Clark

引用次数: 24

Abstract

Sometimes there are difficult optimization problems that it is impossible to find the best strategy or perhaps even not exist at all. For this kind of problems, researchers developed efficient methods called metaheuristic algorithms to find near optimal solutions under an acceptable run-time. Since there isn’t any single metaheuristic algorithm to solve all optimization problems with different types or structures, researchers develop new metaheuristic algorithms with an increasing pace. Most of these algorithms are nature or bio-inspired, mimicking the successful characteristics of nature. Cuckoo search (CS) is one of the relatively new algorithms proposed by Yang and Deb (2009).

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杜鹃搜索算法

有时存在难以找到最佳策略的优化问题，甚至可能根本不存在。对于这类问题，研究人员开发了一种名为元启发式算法的有效方法，以在可接受的运行时间内找到接近最优的解决方案。由于没有任何一种元启发式算法来解决不同类型或结构的所有优化问题，研究人员开发新的元启发式算法的速度越来越快。这些算法大多是受自然或生物启发的，模仿自然的成功特征。杜鹃搜索（CS）是Yang和Deb（2009）提出的一种相对较新的算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Swarm Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ROBOTICS

CiteScore

5.70

自引率

11.50%

发文量

审稿时长

>12 weeks

期刊介绍： Swarm Intelligence is the principal peer-reviewed publication dedicated to reporting on research and developments in the multidisciplinary field of swarm intelligence. The journal publishes original research articles and occasional review articles on theoretical, experimental and/or practical aspects of swarm intelligence. All articles are published both in print and in electronic form. There are no page charges for publication. Swarm Intelligence is published quarterly. The field of swarm intelligence deals with systems composed of many individuals that coordinate using decentralized control and self-organization. In particular, it focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. It is a fast-growing field that encompasses the efforts of researchers in multiple disciplines, ranging from ethology and social science to operations research and computer engineering. Swarm Intelligence will report on advances in the understanding and utilization of swarm intelligence systems, that is, systems that are based on the principles of swarm intelligence. The following subjects are of particular interest to the journal: • modeling and analysis of collective biological systems such as social insect colonies, flocking vertebrates, and human crowds as well as any other swarm intelligence systems; • application of biological swarm intelligence models to real-world problems such as distributed computing, data clustering, graph partitioning, optimization and decision making; • theoretical and empirical research in ant colony optimization, particle swarm optimization, swarm robotics, and other swarm intelligence algorithms.