Penalty Strategy in The Fitness Function of Grey Wolf Optimizer for Minimum Spanning Tree Problem

Autor:	N S Handayani, V Satriadi, Zainudin Zukhri, I V Paputungan
Rok vydání:	2021
Předmět:	Mathematical optimization education.field_of_study Fitness function Optimization problem Computer science Population Minimum spanning tree education New population
Zdroj:	IOP Conference Series: Materials Science and Engineering. 1077:012071
ISSN:	1757-899X 1757-8981
Popis:	The Grey Wolf Optimizer (GWO) is a relatively new population-based optimizer. Various optimization problems have been solved using GWO. This paper presents an experiment on how to apply GWO to solve the minimum spanning tree (MST) problem. MST is normally solved using revision strategy when formulating the fitness in other population-based algorithms. Another strategy, called Penalty strategy, is used in the experiment. Existing dataset for MST problem is tested using GWO. The experiment showed that implementation of penalty strategy in the fitness function of GWO can find a solution with almost 96% accuracy.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::92f5b7aa5e128f6676a8d6079da6c398 https://doi.org/10.1088/1757-899x/1077/1/012071 Zobrazit plný text záznamu