Open Access

ARTICLE

Convergence Track Based Adaptive Differential Evolution Algorithm (CTbADE)

Qamar Abbas¹, Khalid Mahmood Malik², Abdul Khader Jilani Saudagar^3,*, Muhammad Badruddin Khan³, Mozaherul Hoque Abul Hasanat³, Abdullah AlTameem³, Mohammed AlKhathami³

1 Department of Computer Science and Software Engineering, International Islamic University, Islamabad, 44000, Pakistan
2 Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA
3 Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

* Corresponding Author: Abdul Khader Jilani Saudagar. Email:

(This article belongs to this Special Issue: Data Science in Ubiquitous Computing: Data Analytics, Data Mining and Data Security)

Computers, Materials & Continua 2022, 72(1), 1229-1250. https://doi.org/10.32604/cmc.2022.024211

Received 09 October 2021; Accepted 13 December 2021; Issue published 24 February 2022

Abstract

One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima. A novel convergence track based adaptive differential evolution (CTbADE) algorithm is presented in this research paper. The crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima. A more diverse population improves the global searching capability and helps to escape from the local optima problem. Tracking the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying problems. An adaptive powerful parameter-controlled sequences utilized learning period-based memory and following convergence track over time are introduced in this paper. The proposed algorithm will be helpful in maintaining the equilibrium between an algorithm's exploration and exploitation capability. A comprehensive test suite of standard benchmark problems with different natures, i.e., unimodal/multimodal and separable/non-separable, was used to test the convergence power of the proposed CTbADE algorithm. Experimental results show the significant performance of the CTbADE algorithm in terms of average fitness, solution quality, and convergence speed when compared with standard differential evolution algorithms and a few other commonly used state-of-the-art algorithms, such as jDE, CoDE, and EPSDE algorithms. This algorithm will prove to be a significant addition to the literature in order to solve real time problems and to optimize computational models with a high number of parameters to adjust during the problem-solving process.

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