State Transition Algorithm

Abstract

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 the meanwhile, to strengthen the search ability in high dimensional space, communication strategy is introduced into the basic algorithm and intermittent exchange is presented to prevent premature convergence. Finally, experiments are carried out for the algorithms. With 10 common benchmark unconstrained continuous functions used to test the performance, the results show that state transition algorithms are promising algorithms due to their good global search capability and convergence property when compared with some popular algorithms.

18 pages, 28 figures

Cited by in corpus (10)

• A Statistical Study on Parameter Selection of Operators in Continuous State Transition Algorithm
• Discrete State Transition Algorithm for Unconstrained Integer Optimization Problems
• Nonlinear system identification and control using state transition algorithm
• A dynamic state transition algorithm with application to sensor network localization
• Optimal Design of Water Distribution Networks by Discrete State Transition Algorithm
• A Multiobjective State Transition Algorithm Based on Decomposition
• A Multiobjective State Transition Algorithm for Single Machine Scheduling
• Global solutions to a class of CEC benchmark constrained optimization problems
• A matlab toolbox for continuous state transition algorithm
• A Comparative Study of STA on Large Scale Global Optimization