State Transition Algorithm

TL;DR

The paper introduces a novel heuristic search algorithm called the state transition algorithm, designed for continuous optimization, featuring unique transformations, convergence analysis, and strategies to enhance high-dimensional search performance.

Contribution

It proposes a new state transition heuristic algorithm with specific transformations and strategies, and provides convergence analysis and experimental validation.

Findings

Demonstrates strong global search capability.

Shows promising convergence properties.

Performs well on benchmark functions.

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.