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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Matematika UNAND
As-Shidiq, Ikhsan Rizqi Az-Zukruf
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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On ON LOCATING-DOMINATING SET OF THE CARTESIAN PRODUCT OF COMPLETE BIPARTITE GRAPHS AND A PATH OF ORDER TWO Saputro, Suhadi Wido; As-Shidiq, Ikhsan Rizqi Az-Zukruf
Jurnal Matematika UNAND Vol. 14 No. 3 (2025)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.14.3.208-215.2025

Abstract

A set D is called as a locating-dominating set of a graph G if D is a dominating set of G such that every vertex outside D is uniquely identified by its neighbourhood within D. The locating-dominating number of G is the minimum cardinality of all locating-dominating sets of G. In this paper, we consider a Cartesian product graphs between a complete bipartite graphs Km1,m2 and a path of order two P2, denoted by Km1,m2P2. In particular, we determine the locating-dominating number of Km1,m2P2 for any values m1 and m2 at least 2.
ENHANCED GIANT TREVALLY OPTIMIZER FOR ENGINEERING DESIGN AND EPIDEMIOLOGICAL MODEL As-Shidiq, Ikhsan Rizqi Az-Zukruf; Kurniawan, E. Andry Dwi; Sidarto, Kuntjoro Adji
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1229-1250

Abstract

Metaheuristic algorithms are widely used for solving complex optimization problems, but their performance often depends on the initialization strategy. This study proposes an enhanced Giant Trevally Optimizer (GTO) by introducing quasi-random Sobol sequences in the initialization phase, yielding the Sobol-initialized Giant Trevally Optimizer (SGTO). The algorithm was tested on forty benchmark functions, five engineering design problems, and an epidemiological model case study. Experimental results show that SGTO consistently outperforms the original GTO in terms of achieving optimal solutions, convergence, and its ability to maintain a consistent solution across multiple independent runs. Furthermore, the epidemiological case study demonstrates the adaptability of SGTO for tackling more complex real-world problems. This work is the first to adapt Sobol sequences for the GTO and apply it to an epidemiological model. These findings confirm that quasi-random initialization substantially improves exploration and exploitation, establishing SGTO as a versatile and reliable optimization tool.
Co-Authors Kuntjoro Adji Sidarto Kurniawan, E. Andry Dwi Saputro, Suhadi Wido