A Real-Value Parameter Function Optimization Algorithm using Repeated Adaptive Local Search

Authors

  • Surapong Auwatanamongkol

DOI:

https://doi.org/10.47839/ijc.21.1.2519

Keywords:

Real-value Parameters, Function Optimization, Repeated Adaptive Local Search, Shrunk Subregion

Abstract

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.

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Published

2022-03-30

How to Cite

Auwatanamongkol, S. (2022). A Real-Value Parameter Function Optimization Algorithm using Repeated Adaptive Local Search. International Journal of Computing, 21(1), 69-75. https://doi.org/10.47839/ijc.21.1.2519

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Articles