State Transition Algorithm
This addresses optimization problems in continuous domains, offering a novel approach with potential applications in various fields, though it appears incremental as it builds on existing heuristic methods.
The authors proposed a new heuristic random search algorithm called the state transition algorithm for continuous function optimization, which demonstrated good global search capability and convergence properties when tested on 10 benchmark functions.
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.