Abstract—Metaheuristic approaches have been widely used to solve large scale, complex global optimization problems. In this paper, a Hybrid simulated annealing based on discrete radius particle swarm optimization (H-DRPSOSA) with adaptive mutation is proposed. The proposed algorithm takes the advantage of the global search of the RPSO and the local search strategy of the SA algorithm to quickly generate good solutions.The paper also explains the framework design to solve the large scale multidimensional knapsack problems (LCO-MKPs). Additional, we present a random transfer mechanism for the feasible solution of the discrete search region. The proposed hybrid is compared to state-of-the-art solution techniques by applying them to the multidimensional knapsack dataset. Computational results demonstrate that the proposed algorithm is capable of producing competitive solutions.
Index Terms—Large Scale Complex Problems (LCOs), Radius Particle Swarm Optimization (RPSO), Simulated Annealing (SA), Multidimensional Knapsack Problems (MKPs).
E. Songkroh is with Rajamangala University of Technology Suvarnabhumi, Thailand (e-mail: firstname.lastname@example.org).
M. Anantathanavit was with Mahanakorn University of Technology, Thailand (e-mail: email@example.com).
Cite: E. Songkroh and M. Anantathanavit, "New Discrete Metaheuristic Approach for Large Scale Problem," International Journal of Machine Learning and Computing vol. 12, no. 4, pp. 119-125, 2022.Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).