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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Intelligent Informatics
Hussein Fouad Almazini
Shatt Al-Arab University College
Computer Science & IT Engineering

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Enhanced feature clustering method based on ant colony optimization for feature selection Hassan Almazini; Ku Ruhana Ku-Mahamud; Hussein Fouad Almazini
International Journal of Advances in Intelligent Informatics Vol 9, No 1 (2023): March 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i1.987

Abstract

The popular modified graph clustering ant colony optimization (ACO) algorithm (MGCACO) performs feature selection (FS) by grouping highly correlated features. However, the MGCACO has problems in local search, thus limiting the search for optimal feature subset. Hence, an enhanced feature clustering with ant colony optimization (ECACO) algorithm is proposed. The improvement constructs an ACO feature clustering method to obtain clusters of highly correlated features. The ACO feature clustering method utilizes the ability of various mechanisms, such as local and global search to provide highly correlated features. The performance of ECACO was evaluated on six benchmark datasets from the University California Irvine (UCI) repository and two deoxyribonucleic acid microarray datasets, and its performance was compared against that of five benchmark metaheuristic algorithms. The classifiers used are random forest, k-nearest neighbors, decision tree, and support vector machine. Experimental results on the UCI dataset show the superior performance of ECACO compared with other algorithms in all classifiers in terms of classification accuracy. Experiments on the microarray datasets, in general, showed that the ECACO algorithm outperforms other algorithms in terms of average classification accuracy. ECACO can be utilized for FS in classification tasks for high-dimensionality datasets in various application domains such as medical diagnosis, biological classification, and health care systems.
Modified gorilla troops optimization for the quadratic assignment problem Hussein Fouad Almazini; Salah Mortada; Hassan Al-Mazini
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2153-2165

Abstract

Balancing exploration and exploitation remain a fundamental challenge in artificial intelligence-based optimization, particularly when addressing discrete combinatorial problems such as the quadratic assignment problem (QAP). The gorilla troops optimizer (GTO), inspired by the collective social behavior of gorillas, has shown promising results in continuous domains but faces limitations when directly applied to discrete optimization. To address this, the present study introduces a modified gorilla troops optimizer (MGTO), a novel discrete adaptation designed specifically for the QAP. The proposed MGTO strategically integrates a swapping-based diversification mechanism to enhance exploration within discrete solution spaces, while a modified uniform crossover operator promotes effective exploitation of high-quality solutions. Extensive experiments on benchmark instances from the quadratic assignment problem library (QAPLIB) show that MGTO achieves superior convergence behavior and solution quality compared with several state-of-the-art algorithms. These results demonstrate MGTO’s capacity to maintain a balanced equilibrium between exploration and exploitation, effectively navigating complex discrete landscapes to yield high-quality solutions with strong computational efficiency.
Co-Authors Hassan Al-Mazini Hassan Almazini Ku Ruhana Ku-Mahamud Salah Mortada