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
Discrete state transition algorithm for unconstrained integer optimization problems
TLDR
A recently new intelligent optimization algorithm called discrete state transition algorithm is considered, for solving unconstrained integer optimization problems, and a dynamic adjustment strategy called "risk and restoration in probability" is proposed to capture global solutions with high probability. Expand
A Statistical Study on Parameter Selection of Operators in Continuous State Transition Algorithm
TLDR
A new continuous STA with optimal parameter selection of operators in continuous STA is proposed to accelerate its search process and a comparison with other metaheuristics has demonstrated the effectiveness of the proposed method. Expand
State transition algorithm for constrained optimization problems
In this study, a population-based continuous state transition algorithm (STA) is investigated into continuous constrained optimization problems. After an analysis of the advantages and disadvantagesExpand
A multi-objective state transition algorithm for continuous optimization
This paper proposes a new approach called multi-objective state transition algorithm(MOSTA) which incorporates state transition algorithm with the concept of Pareto dominance to solve continuousExpand
Multiagent based state transition algorithm for global optimization
TLDR
A novel multiagent based state transition optimization algorithm with linear convergence rate named MASTA is constructed that computes the fitness values of all individuals and finds the best individuals in the new population. Expand
State Transition Simulated Annealing Algorithm for Discrete-Continuous Optimization Problems
TLDR
A new state transition SA (STASA) algorithm is proposed for combinatorial optimization and continuous optimization problems and the experimental results show that the proposed algorithm is efficient and reliable. Expand
State-transition simulated annealing algorithm for constrained and unconstrained multi-objective optimization problems
In this article, a novel multi-objective optimization algorithm based on a state-transition simulated annealing algorithm (MOSTASA) is proposed, in which four state-transition operators forExpand
A discrete state transition algorithm for the task assignment problem
A novel discrete State Transition Algorithm (STA) is proposed for solving the Task Assignment Problems (TAP) in this study. The commonly used formulation of TAP is reconstructed to transform theExpand
A State Transition Algorithm with Variable Local Candidate Solution Space and its application to Residue Hydrogenation Fractionation Process
Parameter Optimal State Transition Algorithm (POSTA) is a global metaheuristic method with adaptive parameter adjustment capabilities. However, its fixed local search candidate solution space easilyExpand
A Discrete State Transition Algorithm for Generalized Traveling Salesman Problem
Generalized traveling salesman problem (GTSP) is an extension of classical traveling salesman problem (TSP), which is a combinatorial optimization problem and an NP-hard problem. In this paper, anExpand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 44 REFERENCES
Initial Version of State Transition Algorithm
  • Xiaojun Zhou, C. Yang, W. Gui
  • Computer Science, Mathematics
  • 2011 Second International Conference on Digital Manufacturing & Automation
  • 2011
TLDR
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
  • Xiaojun Zhou, C. Yang, W. Gui
  • 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 dimensionalExpand
Continuous functions minimization by dynamic random search technique
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 searchExpand
Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization
TLDR
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
  • G. Rudolph
  • Mathematics, Computer Science
  • IEEE Trans. Neural Networks
  • 1994
TLDR
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
  • A. Wright
  • Mathematics, Computer Science
  • FOGA
  • 1990
TLDR
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
TLDR
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
TLDR
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
TLDR
This chapter is a short introduction to the main methods used in stochastic optimization. Expand
A study of particle swarm optimization particle trajectories
TLDR
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
...
1
2
3
4
5
...