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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Indonesian Journal of Electrical Engineering and Computer Science
Hayder Naser Khraibet AL-Behadili
Shatt Alarab University College
Computer Science & IT Electrical & Electronics Engineering Engineering

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Improved discrete plant propagation algorithm for solving the traveling salesman problem Hussein Fouad Almazini; Salah Mortada; Hassan Fouad Abbas Al-Mazini; Hayder Naser Khraibet AL-Behadili; Jawad Alkenani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i1.pp13-22

Abstract

The primary goal of traveling salesman problem (TSP) is for a salesman to visit many cities and return to the starting city via a sequence of potential shortest paths. Subsequently, conventional algorithms are inadequate for large-scale problems; thus, metaheuristic algorithms have been proposed. A recent metaheuristic algorithm that has been implemented to solve TSP is the plant propagation algorithm (PPA), which belongs to the rose family. In this research, this existing PPA is modified to solve TSP. Although PPA is claimed to be successful, it suffers from the slow convergence problem, which significantly impedes its applicability for getting good solution. Therefore, the proposed partial-partitioned greedy algorithm (PPGA) offers crossover and three mutation operations (flip, swap, and slide), which allow local and global search and seem to be wise methods to help PPA in solving the TSP. The PPGA performance is evaluated on 10 separate datasets available in the literature and compared with the original PPA. In terms of distance, the computational results demonstrate that the PPGA outperforms the original PPA in nine datasets which assures that it is 90% better than PPA. PPGA produces good solutions when compared with other algorithms in the literature, where the average execution time reduces by 10.73%.
Adaptive Parameter Control Strategy for Ant-Miner Classification Algorithm Hayder Naser Khraibet Al-Behadili; Rafid Sagban; Ku Ruhana Ku-Mahamud
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 8, No 1: March 2020
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (908.687 KB) | DOI: 10.52549/ijeei.v8i1.1423

Abstract

Pruning is the popular framework for preventing the dilemma of overfitting noisy data. This paper presents a new hybrid Ant-Miner classification algorithm and ant colony system (ACS), called ACS-AntMiner. A key aspect of this algorithm is the selection of an appropriate number of terms to be included in the classification rule. ACS-AntMiner introduces a new parameter called importance rate (IR) which is a pre-pruning criterion based on the probability (heuristic and pheromone) amount. This criterion is responsible for adding only the important terms to each rule, thus discarding noisy data. The ACS algorithm is designed to optimize the IR parameter during the learning process of the Ant-Miner algorithm. The performance of the proposed classifier is compared with related ant-mining classifiers, namely, Ant-Miner, CAnt-Miner, TACO-Miner, and Ant-Miner with a hybrid pruner across several datasets. Experimental results show that the proposed classifier significantly outperforms the other ant-mining classifiers.
Annealing strategy for an enhance rule pruning technique in ACO-Based rule classification Hayder Naser Khraibet AL-Behadili; Ku Ruhana Ku-Mahamud; Rafid Sagban
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 3: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i3.pp1499-1507

Abstract

Ant colony optimization (ACO) was successfully applied to data mining classification task through ant-mining algorithms. Exploration and exploitation are search strategies that guide the learning process of a classification model and generate a list of rules. Exploitation refers to the process of intensifying the search for neighbors in good regions, whereas exploration aims towards new promising regions during a search process. The existing balance between exploration and exploitation in the rule construction procedure is limited to the roulette wheel selection mechanism, which complicates rule generation. Thus, low-coverage complex rules with irrelevant terms will be generated. This work proposes an enhancement rule pruning procedure for the ACO algorithm that can be used in rule-based classification. This procedure, called the annealing strategy, is an improvement of ant-mining algorithms in the rule construction procedure. Presented as a pre-pruning technique, the annealing strategy deals first with irrelevant terms before creating a complete rule through an annealing schedule. The proposed improvement was tested through benchmarking experiments, and results were compared with those of four of the most related ant-mining algorithms, namely, Ant-Miner, CAnt-Miner, TACO-Miner, and Ant-Miner with hybrid pruner. Results display that our proposed technique achieves better performance in terms of classification accuracy, model size, and computational time. The proposed annealing schedule can be used in other ACO variants for different applications to improve classification accuracy.
Co-Authors Hassan Fouad Abbas Al-Mazini Hussein Fouad Almazini Jawad Alkenani Ku Ruhana Ku-Mahamud Ku Ruhana Ku-Mahamud Rafid Sagban Salah Mortada