Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2026.087316
Journal Menu
Special Issues
Table of Content

All issues

Online First

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

Elite Population-Driven Backtracking Search Algorithm

Yiying Zhang*
College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China
* Corresponding Author: Yiying Zhang. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.087316

Received 14 June 2026; Accepted 19 August 2026; Published online 14 September 2026

Abstract

Backtracking search algorithm (BSA) is a very simple and efficient metaheuristic algorithm. However, BSA only relies on the random crossover vectors generated between the historical population and the current population to guide the search direction of the population, lacking purposefulness. In view of this, this paper proposes an improved backtracking search algorithm (IBSA) that designs three search strategies by creating two dynamic elite populations. Compared to BSA, IBSA has stronger purposefulness in the search process. To validate the effectiveness of the proposed algorithm, 30 challenging test functions and four complex constrained engineering design problems are solved by employing IBSA, BSA, and four other excellent metaheuristic algorithms. Experimental evidence attests to the superior effectiveness of IBSA in optimization tasks, which also proves the effectiveness of the improved strategy.

Keywords

Elite population; backtracking search algorithm; global optimization; engineering optimization

  • 52

    View

  • 13

    Download

  • 0

    Like

Related articles
Copyright© 2026 Tech Science Press
© 1997-2026 TSP (Henderson, USA) unless otherwise stated

Share Link