# State Transition Algorithm

@article{Zhou2012StateTA, title={State Transition Algorithm}, author={Xiaojun Zhou and C. Yang and W. Gui}, journal={ArXiv}, year={2012}, volume={abs/1205.6548} }

In terms of the concepts of state and state transition, a new heuristic random search algorithm named state transition algorithm is proposed. For continuous function optimization problems, four special transformation operators called rotation, translation, expansion and axesion are designed. Adjusting measures of the transformations are mainly studied to keep the balance of exploration and exploitation. Convergence analysis is also discussed about the algorithm based on random search theory. In… Expand

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#### References

SHOWING 1-10 OF 44 REFERENCES

Initial Version of State Transition Algorithm

- Computer Science, Mathematics
- 2011 Second International Conference on Digital Manufacturing & Automation
- 2011

A new algorithm-State Transition Algorithm (STA) is proposed in order to probe into classical and intelLigent optimization algorithms on the basis of state and state transition, it becomes much simpler and easier to understand. Expand

A new transformation into state transition algorithm for finding the global minimum

- Mathematics
- 2011 2nd International Conference on Intelligent Control and Information Processing
- 2011

To promote the global search ability of the original state transition algorithm, a new operator called axesion is suggested, which aims to search along the axes and strengthen single dimensional… Expand

Continuous functions minimization by dynamic random search technique

- Mathematics
- 2007

Random search technique is the simplest one of the heuristic algorithms. It is stated in the literature that the probability of finding global minimum is equal to 1 by using the basic random search… Expand

Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization

- Mathematics, Computer Science
- IEEE Transactions on Evolutionary Computation
- 2009

This paper proposes a self- Adaptive DE (SaDE) algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions. Expand

Convergence analysis of canonical genetic algorithms

- Mathematics, Computer Science
- IEEE Trans. Neural Networks
- 1994

This paper analyzes the convergence properties of the canonical genetic algorithm with mutation, crossover and proportional reproduction applied to static optimization problems and shows variants of CGA's that always maintain the best solution in the population are shown to converge to the global optimum due to the irreducibility property of the underlying original nonconvergent CGA. Expand

Genetic Algorithms for Real Parameter Optimization

- Mathematics, Computer Science
- FOGA
- 1990

It is shown that k-point crossover can be viewed as a crossover operation on the vector of parameters plus perturbations of some of the parameters, which suggests a genetic algorithm that uses real parameter vectors as chromosomes, real parameters as genes, and real numbers as alleles. Expand

Real-coded genetic algorithm benchmarked on noiseless black-box optimization testbed

- Mathematics, Computer Science
- GECCO '10
- 2010

A real-coded genetic algorithm (RCGA), which employs an adaptive-range variant of the well-known non-uniform mutation, is furnished with a multiple independent restarts mechanism to benchmark the noise-free black-box optimization testbed. Expand

A heuristic approach for finding the global minimum: Adaptive random search technique

- Mathematics, Computer Science
- Appl. Math. Comput.
- 2006

This method, called Adaptive Random Search Technique (ARSET), is experimented on test problems, and successful results are obtained, and outcome of which is observed to be relatively better than other methods. Expand

Stochastic Optimization Algorithms

- Computer Science
- ArXiv
- 2007

This chapter is a short introduction to the main methods used in stochastic optimization. Expand

A study of particle swarm optimization particle trajectories

- Computer Science, Mathematics
- Inf. Sci.
- 2006

Current theoretical studies on particle swarm optimization are extended to investigate particle trajectories for general swarms to include the influence of the inertia term, and a formal proof that each particle converges to a stable point is provided. Expand