MTGWA: A Multithreaded Gray Wolf Algorithm with Strategies Based on Simulated Annealing and Genetic Algorithms

Felix Martinez-Rios; Alfonso Murillo-Suarez; Cesar Raul Garcia-Jacas; Juan Manuel Guerrero-Valadez

Computer Science and Health Engineering in Health Services. 4th EAI International Conference, COMPSE 2020, Virtual Event, November 26, 2020, Proceedings

Research Article

MTGWA: A Multithreaded Gray Wolf Algorithm with Strategies Based on Simulated Annealing and Genetic Algorithms

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@INPROCEEDINGS{10.1007/978-3-030-69839-3_11,
    author={Felix Martinez-Rios and Alfonso Murillo-Suarez and Cesar Raul Garcia-Jacas and Juan Manuel Guerrero-Valadez},
    title={MTGWA: A Multithreaded Gray Wolf Algorithm with Strategies Based on Simulated Annealing and Genetic Algorithms},
    proceedings={Computer Science and Health Engineering in Health Services. 4th EAI International Conference, COMPSE 2020, Virtual Event, November 26, 2020, Proceedings},
    proceedings_a={COMPSE},
    year={2021},
    month={7},
    keywords={Nature-inspired algorithm Optimization Multi-threaded execution Optimization techniques Metaheuristics Gray Wolf algorithm},
    doi={10.1007/978-3-030-69839-3_11}
}

Felix Martinez-Rios
Alfonso Murillo-Suarez
Cesar Raul Garcia-Jacas
Juan Manuel Guerrero-Valadez
Year: 2021
MTGWA: A Multithreaded Gray Wolf Algorithm with Strategies Based on Simulated Annealing and Genetic Algorithms
COMPSE
Springer
DOI: 10.1007/978-3-030-69839-3_11

Felix Martinez-Rios¹, Alfonso Murillo-Suarez¹, Cesar Raul Garcia-Jacas², Juan Manuel Guerrero-Valadez¹

1: Universidad Panamericana
2: CICESE

Abstract

In this paper, we present an improvement of the Gray Wolf algorithm (GWO) based on a multi-threaded implementation of the original algorithm. The paper demonstrates how to combine the solutions obtained in each of the threads to achieve a final solution closer to the absolute minimum or even equal to it. To properly combine the solutions of each of the threads of execution, we use strategies based on simulated annealing and genetic algorithms. Also, we show the results obtained for twenty-nine functions: unimodal, multimodal, fixed dimension and composite functions. Experiments show that our proposed improves the results of the original algorithm.

Keywords: Nature-inspired algorithm Optimization Multi-threaded execution Optimization techniques Metaheuristics Gray Wolf algorithm

Published: 2021-07-16
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-030-69839-3_11