@article{Auwatanamongkol_2022, title={A Real-Value Parameter Function Optimization Algorithm using Repeated Adaptive Local Search}, volume={21}, url={https://computingonline.net/computing/article/view/2519}, DOI={10.47839/ijc.21.1.2519}, abstractNote={<p>A simple and easy to implement but very effective algorithm for solving real-value parameter optimization problems is introduced in this paper. The main idea of the algorithm is to perform a local search repeatedly on a prospective subregion where the optimal solution may be located. The local search randomly samples a number of solutions in a given subregion. If a new best-so- far solution has been found, the center of the search subregion is moved based on the new best-so-far solution and the size of the search subregion is gradually reduced by a predefined shrinking rate. Otherwise, the center of the search is not moved and the size of the search subregion is reduced using a predefined shrinking rate. This process is repeated for a number of instances so that the search is focused on a gradually smaller and smaller prospective subregion. To enhance the likelihood of achieving an optimal solution, many rounds of this repeated local search are performed. Each round starts with a smaller and smaller initial search space. According to the experiment results, the proposed algorithm, though very simple, can outperform some well-known optimization algorithms on some testing functions.</p>}, number={1}, journal={International Journal of Computing}, author={Auwatanamongkol, Surapong}, year={2022}, month={Mar.}, pages={69-75} }