Cat Swarm Optimization

@inproceedings{Chu2006CatSO,
  title={Cat Swarm Optimization},
  author={S. C. Chu and Pei-wei Tsai and Jeng-Shyang Pan},
  booktitle={PRICAI},
  year={2006}
}
In this paper, we present a new algorithm of swarm intelligence, namely, Cat Swarm Optimization (CSO). [] Key Method 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).
Review on Swarm Intelligence for Optimization
TLDR
The concept of four swarm intelligence methods, including Bat Algorithm (BA), Evolved Bat Al algorithm (EBA), Cat Swarm Optimization (CSO), and Parallel Cat Swarmoptimization (PCSO) are given in a comprehensive way are given.
Average-Inertia Weighted Cat Swarm Optimization
TLDR
A new algorithm is proposed namely, Average-Inertia Weighted CSO (AICSO), which added a new parameter to the position update equation as an inertia weight and used a new form of the velocity update equation in the tracing mode of algorithm.
Enhanced Parallel Cat Swarm Optimization (EPCSO)
TLDR
In this review, three hybrid swarm intelligence algorithms, namely, Adaptive Simulated Annealing Particle Swarm Optimization (ASA-PSO), Bacterial-GA Foraging, and Enhanced Parallel Cat Swarmoptimization (EPCSO), for solving numerical problems in optimization are summarized.
A Survey on Cat Swarm Optimization Algorithm
TLDR
This paper presents a review for Cat Swarm Optimization (CSO), which is a powerful metaheuristic swarm-based optimization algorithm inspired by behaviors of cats in the Nature for solving optimization problems.
The Fast Modification of Evolutionary Bioinspired Cat Swarm Optimization Method
This paper discusses the optimization problem based on cat swarm optimization by introducing elements of a random search in a stochastic modification of the basic procedure into the seeking and
A Comprehensive Review on Hybrid Swarm Intelligence for Optimization
TLDR
Three hybrid swarm intelligence algorithms, namely, Adaptive Simulated Annealing - Particle Swarm Optimization (ASA-PSO), Bacterial-GA Foraging, and Enhanced Parallel Cat Swarmoptimization (EPCSO), for solving numerical problems in optimization are summarized.
Performance Comparison of Cat Swarm Optimization and Genetic Algorithm on Optimizing Functions
TLDR
This study found out the best performance resulted from Cat Swarm Optimization (CSO) and Genetic Algorithm and showed that CSO is better than GA due to its performance in terms of iterations and time.
Improvement Cat Swarm Optimization for Efficient Motion Estimation
TLDR
An improvement structure of cat swarm optimization (ICSO) is presented, capable of improving search efficiency within the problem space under the conditions of a small population size and a few iteration numbers, and gets higher accuracy than the existing methods and requires less computational time.
A Novel Cat Swarm Optimization Algorithm for Unconstrained Optimization Problems
TLDR
Experimental results show that in comparison with the pure CSO, the proposed CSO can takes a less time to converge and can find the best solution in less iteration.
Nature Inspired Methods and Their Industry Applications—Swarm Intelligence Algorithms
TLDR
This paper presents the particle swarm optimization (PSO) algorithm and the ant colony optimization (ACO) method as the representatives of the SI approach and mentions some metaheuristics belonging to the SI.
...
...

References

SHOWING 1-10 OF 10 REFERENCES
A new optimizer using particle swarm theory
  • R. Eberhart, J. Kennedy
  • Computer Science
    MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science
  • 1995
The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented
A Parallel Particle Swarm Optimization Algorithm with Communication Strategies
TLDR
A parallel version of the particle swarm optimization (PPSO) algorithm together with three communication strategies which can be used according to the independence of the data, which demonstrates the usefulness of the proposed PPSO algorithm.
Empirical study of particle swarm optimization
  • Y. Shi, R. Eberhart
  • Computer Science
    Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)
  • 1999
TLDR
The experimental results show that the PSO is a promising optimization method and a new approach is suggested to improve PSO's performance near the optima, such as using an adaptive inertia weight.
Ant colony system with communication strategies
Ant colony system: a cooperative learning approach to the traveling salesman problem
TLDR
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.
Vector quantization based on genetic simulated annealing
Application of parallel genetic algorithm and property of multiple global optima to VQ codevector index assignment for noisy channels
TLDR
A parallel genetic algorithm is applied to assign the codevector indices for noisy channels so as to minimise the distortion caused by bit errors and results confirm this approach.
Optimization by Simulated Annealing
TLDR
A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems.
Genetic Algorithm in Search
Genetic Algorithm in Search. Optimization and Machine Learning
  • Genetic Algorithm in Search. Optimization and Machine Learning
  • 1989