Global Search of Genetic Algorithm Enhanced by Multi-basin Dynamic Neighbor Sampling

Authors

  • Misuk Kim Pohang University of Science and Technology
  • Gyu-Sik Han Chonbuk National University

DOI:

https://doi.org/10.23055/ijietap.2015.22.1.1227

Keywords:

Genetic Algorithm, Global Optimal Solution, Multi-basin Dynamic Neighbor Sampling, Heston Model

Abstract

We propose a pioneering enhanced genetic algorithm to find a global optimal solution without derivatives information. A new neighbor sampling method driven by a multi-basin dynamics framework is used to efficiently divert from one existing local optimum to another. The method investigates the rectangular-box regions constructed by dividing the interval of each axis in the search domain based on information of the constructed multi-basins, and then finds a better local optimum. This neighbor sampling and the local search are repeated alternately throughout the entire search domain until no better neighboring local optima could be found. We improve the quality of solutions by applying genetic algorithm with the resulting point as an initial population generator. We fulfill two kinds of simulations, benchmark problems and a financial application, to verify the effectiveness of our proposed approach, and compare the performance of our proposed method with that of direct search, genetic algorithm, particle swarm optimization, and multi-starts.

Author Biographies

Misuk Kim, Pohang University of Science and Technology

Department of Industrial Engineering

Gyu-Sik Han, Chonbuk National University

Division of Business Administration, Assistant Professor

Published

2014-09-02

How to Cite

Kim, M., & Han, G.-S. (2014). Global Search of Genetic Algorithm Enhanced by Multi-basin Dynamic Neighbor Sampling. International Journal of Industrial Engineering: Theory, Applications and Practice, 22(1). https://doi.org/10.23055/ijietap.2015.22.1.1227

Issue

Section

Special Issue: 2013 IJIE Conference at Busan, Korea