Computational intelligence based on the behavior of cats
01 Jan 2007-
TL;DR: A new optimization algorithm, namely, Cat Swarm Optimization (CSO) is proposed, which is generated by observing the behavior of cats, and composed of two sub-models by simulating thebehavior of cats.
Abstract: Optimization problems are very important in many fields To the present, many optimization algorithms based on computational intelligence have been proposed, such as the Genetic Algorithm, Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO) In this paper, a new optimization algorithm, namely, Cat Swarm Optimization (CSO) is proposed CSO is generated by observing the behavior of cats, and composed of two sub-models by simulating the behavior of cats According to the experiments, the results reveal that CSO is superior to PSO
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TL;DR: An up-to-date review of all major nature inspired metaheuristic algorithms employed till date for partitional clustering and key issues involved during formulation of various metaheuristics as a clustering problem and major application areas are discussed.
Abstract: The partitional clustering concept started with K-means algorithm which was published in 1957. Since then many classical partitional clustering algorithms have been reported based on gradient descent approach. The 1990 kick started a new era in cluster analysis with the application of nature inspired metaheuristics. After initial formulation nearly two decades have passed and researchers have developed numerous new algorithms in this field. This paper embodies an up-to-date review of all major nature inspired metaheuristic algorithms employed till date for partitional clustering. Further, key issues involved during formulation of various metaheuristics as a clustering problem and major application areas are discussed.
457 citations
Cites methods from "Computational intelligence based on..."
...Cat Swarm Optimization (CSO) – The CSO algorithm is proposed by Chu and Tsai [269,270] by observing the natural hunting...
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...Tsai and Lin [257] have reported improved solution provided by FSA compared to PSO for several optimization problems....
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...[269] S.C. Chu, P.W. Tsai, J.S. Pan, Cat Swarm Optimization, in: Proceedings of Nineth Pacific Rim International Conference on Artificial Intelligence, Lecture Notes in Computer Science, vol. 4099, Springer-Verlag, 2006, pp. 854–858....
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...The CSO algorithm is proposed by Chu and Tsai [269,270] by observing the natural hunting skill of cats....
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...[257] H.C. Tsai, Y.H. Lin, Modification of the fish swarm algorithm with particle swarm optimization formulation and communication behavior, Appl....
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01 Jan 2009
TL;DR: An enhanced Artificial Bee Colony (ABC) optimization algorithm, which is called the Interactive ArtificialBee Colony (IABC) optimization, for numerical optimiza- tion problems, is proposed in this paper and the experimental results manifest the superiority in accuracy of the proposed IABC to other methods.
Abstract: An enhanced Artificial Bee Colony (ABC) optimization algorithm, which is called the Interactive Artificial Bee Colony (IABC) optimization, for numerical optimiza- tion problems, is proposed in this paper. The onlooker bee is designed to move straightly to the picked coordinate indicated by the employed bee and evaluates the fitness values near it in the original Artificial Bee Colony algorithm in order to reduce the computa- tional complexity. Hence, the exploration capacity of the ABC is constrained in a zone. Based on the framework of the ABC, the IABC introduces the concept of universal grav- itation into the consideration of the affection between employed bees and the onlooker bees. By assigning different values of the control parameter, the universal gravitation should be involved for the IABC when there are various quantities of employed bees and the single onlooker bee. Therefore, the exploration ability is redeemed about on average in the IABC. Five benchmark functions are simulated in the experiments in order to com- pare the accuracy/quality of the IABC, the ABC and the PSO. The experimental results manifest the superiority in accuracy of the proposed IABC to other methods.
237 citations
Cites methods from "Computational intelligence based on..."
...Later then, many swarm intelligence algorithms for solving problems of optimization are proposed such as the Cat Swarm Optimization (CSO) [5-6], the Parallel Cat Swarm Optimization (PCSO) [23], the Artificial Bee Colony (ABC) [15-16], the Particle Swarm Optimization (PSO) [2, 4, 14, 22], the Fast Particle Swarm Optimization (FPSO) [3], and the Ant Colony Optimization (ACO) [7-9]....
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TL;DR: In this article, an optimization technique based on cat swarm optimization (CSO) algorithm is proposed to estimate the unknown parameters of single and double diode models, and the evaluation for the quality of identified parameters is also given.
231 citations
TL;DR: A novel multi-strategy ensemble ABC (MEABC) algorithm, where a pool of distinct solution search strategies coexists throughout the search process and competes to produce offspring.
221 citations
Cites background from "Computational intelligence based on..."
...In the past decades, some swarm intelligence algorithms, inspired by the social behaviors of birds, fish or insects, have been proposed to solve NP-complete optimization problems [8], such as particle swarm optimization (PSO) [29], ant colony optimization (ACO) [10], artificial bee colony (ABC) [22], cat swarm optimization (CSO) [5], and firefly algorithm (FA) [64]....
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TL;DR: This paper makes a comprehensive survey of workflow scheduling in cloud environment in a problem–solution manner and conducts taxonomy and comparative review on workflow scheduling algorithms.
Abstract: To program in distributed computing environments such as grids and clouds, workflow is adopted as an attractive paradigm for its powerful ability in expressing a wide range of applications, including scientific computing, multi-tier Web, and big data processing applications. With the development of cloud technology and extensive deployment of cloud platform, the problem of workflow scheduling in cloud becomes an important research topic. The challenges of the problem lie in: NP-hard nature of task-resource mapping; diverse QoS requirements; on-demand resource provisioning; performance fluctuation and failure handling; hybrid resource scheduling; data storage and transmission optimization. Consequently, a number of studies, focusing on different aspects, emerged in the literature. In this paper, we firstly conduct taxonomy and comparative review on workflow scheduling algorithms. Then, we make a comprehensive survey of workflow scheduling in cloud environment in a problem---solution manner. Based on the analysis, we also highlight some research directions for future investigation.
206 citations
Cites methods from "Computational intelligence based on..."
...Instead of PSO-based optimization, they used cat swarm optimization (CSO) method to obtain schedule in less number of iterations [41]....
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References
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Book•
01 Sep 1988
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Abstract: From the Publisher:
This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields
Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required
52,797 citations
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Abstract: There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.
41,772 citations
06 Aug 2002
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.
35,104 citations
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed The relationships between particle swarm optimization and both artificial life and genetic algorithms are described
18,439 citations