|
|
|
|
|
Abdullahi T. Sulaiman, Habeeb Bello-Salau, Adeiza J. Onumanyi, Muhammed B. Mu?azu, Emmanuel A. Adedokun, Ahmed T. Salawudeen and Abdulfatai D. Adekale
The particle swarm optimization (PSO) algorithm is widely used for optimization purposes across various domains, such as in precision agriculture, vehicular ad hoc networks, path planning, and for the assessment of mathematical test functions towards ben...
ver más
|
|
|
|
|
|
|
|
|
|
|
Manli Dai and Zhongyi Jiang
An improved slime mold algorithm (IMSMA) is presented in this paper for a multiprocessor multitask fair scheduling problem, which aims to reduce the average processing time. An initial population strategy based on Bernoulli mapping reverse learning is pr...
ver más
|
|
|
|
|
|
|
|
|
|
|
Azad A. Ameen, Tarik A. Rashid and Shavan Askar
Child drawing development optimization (CDDO) is a recent example of a metaheuristic algorithm. The motive for inventing this method is children?s learning behavior and cognitive development, with the golden ratio being employed to optimize the aesthetic...
ver más
|
|
|
|
|
|
|
|
|
|
|
Mohamad Norherman Shauqee, Parvathy Rajendran and Nurulasikin Mohd Suhadis
This paper presents an optimization algorithm named Random Explosion Algorithm (REA). The fundamental idea of this algorithm is based on a simple concept of the explosion of an object. This object is commonly known as a particle: when exploded, it will r...
ver más
|
|
|
|
|
|
|
|
|
|
|
Zenab Mohamed Elgamal, Norizan Mohd Yasin, Aznul Qalid Md Sabri, Rami Sihwail, Mohammad Tubishat and Hazim Jarrah
The rapid growth in biomedical datasets has generated high dimensionality features that negatively impact machine learning classifiers. In machine learning, feature selection (FS) is an essential process for selecting the most significant features and re...
ver más
|
|
|
|
|
|