Book ChapterDOI
Cat swarm optimization
Shu-Chuan Chu,Pei-Wei Tsai,Jeng-Shyang Pan +2 more
- pp 854-858
TLDR
Experimental results using six test functions demonstrate that CSO has much better performance than Particle Swarm Optimization (PSO).Abstract:
In this paper, we present a new algorithm of swarm intelligence, namely, Cat Swarm Optimization (CSO). CSO is generated by observing the behaviors of cats, and composed of two sub-models, i.e., tracing mode and seeking mode, which model upon the behaviors of cats. Experimental results using six test functions demonstrate that CSO has much better performance than Particle Swarm Optimization (PSO).read more
Citations
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Posted Content
A Brief Review of Nature-Inspired Algorithms for Optimization
TL;DR: A relatively comprehensive list of all the algorithms based on swarm intelligence, bio-inspired, physics-based and chemistry-based, depending on the sources of inspiration, that have become popular tools for solving real-world problems.
Journal ArticleDOI
Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm
Maziar Yazdani,Fariborz Jolai +1 more
TL;DR: A new population based algorithm, the Lion Optimization Algorithm (LOA), is introduced, special lifestyle of lions and their cooperation characteristics has been the basic motivation for development of this optimization algorithm.
Computational intelligence based on the behavior of cats
Shu-Chuan Chu,Pei-Wei Tsai +1 more
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.
Journal ArticleDOI
Swarm Intelligence Algorithms for Feature Selection: A Review
TL;DR: A unified SI framework is proposed and used to explain different approaches to FS and guidelines on how to develop SI approaches for FS are provided to support researchers and analysts in their data mining tasks and endeavors.
Journal ArticleDOI
Enhanced artificial bee colony optimization
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.
References
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Book
Genetic algorithms in search, optimization, and machine learning
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.
Journal ArticleDOI
Optimization by Simulated Annealing
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.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Proceedings ArticleDOI
A new optimizer using particle swarm theory
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
Journal ArticleDOI
Ant colony system: a cooperative learning approach to the traveling salesman problem
TL;DR: The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and it is concluded comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs.