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Chaotic artificial algae algorithm for Solving Global Optimization With Real-World Space Trajectory Design Problems

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年第2期50卷 1279-1306页

作者： Turkoglu, Bahaeddin Uymaz, Sait Ali Kaya, Ersin Ankara Univ Fac Engn Dept Artificial Intelligence & Data Engn Ankara Turkiye Konya Tech Univ Fac Engn & Nat Sci Dept Comp Engn Konya Turkiye

The artificial algae algorithm (AAA) is a recently introduced metaheuristic algorithm inspired by the behavior and characteristics of microalgae. Like other metaheuristic algorithms, AAA faces challenges such as local optima and premature convergence. Various strategies to address these issues and enhance the performance of the algorithm have been proposed in the literature. These include levy flight, local search, variable search, intelligent search, multi-agent systems, and quantum behaviors. This paper introduces chaos theory as a strategy to improve AAA's performance. Chaotic maps are utilized to effectively balance exploration and exploitation, prevent premature convergence, and avoid local minima. Ten popular chaotic maps are employed to enhance AAA's performance, resulting in the chaotic artificial algae algorithm (CAAA). CAAA's performance is evaluated on thirty benchmark test functions, including unimodal, multimodal, and fixed dimension problems. The algorithm is also tested on three classical engineering problems and eight space trajectory design problems at the European Space Agency. A statistical analysis using the Friedman and Wilcoxon tests confirms that CAA demonstrates successful performance in optimization problems.

关键词： artificial algae algorithm Chaotic maps Global optimization Engineering design Space trajectory mission

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MAA: multi-objective artificial algae algorithm for workflow scheduling in heterogeneous fog-cloud environment

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JOURNAL OF SUPERCOMPUTING 2023年第10期79卷 11218-11260页

作者： Shukla, Prashant Pandey, Sudhakar Natl Inst Technol Dept Informat Technol Raipur 492010 Chhattisgarh India

With the development of current computing technology, workflow applications have become more important in a variety of fields, including research, education, health care, and scientific experimentation. A group of tasks with complicated dependency relationships constitute the workflow applications. It can be difficult to create an acceptable execution sequence while maintaining precedence constraints. Workflow scheduling algorithms (WSA) are gaining more attention from researchers as a real-time concern. Even though a variety of research perspectives have been demonstrated for WSAs, it remains challenging to develop a single coherent algorithm that simultaneously meets a variety of criteria. There is very less research available on WSA in the heterogeneous computing system. Classical scheduling techniques, evolutionary optimisation algorithms, and other methodologies are the available solution to this problem. The workflow scheduling problem is regarded as NP-complete. This problem is constrained by various factors, such as Quality of Service, interdependence between tasks, and user deadlines. In this paper, an efficient meta-heuristic approach named Multi-objective artificial algae (MAA) algorithm is presented for scheduling scientific workflows in a hierarchical fog-cloud environment. In the first phase, the algorithm pre-processes scientific workflow and prepares two task lists. In order to speed up execution, bottleneck tasks are executed with high priority. The MAA algorithm is used to schedule tasks in the following stage to reduce execution times, energy consumption and overall costs. In order to effectively use fog resources, the algorithm also utilises the weighted sum-based multi-objective function. The proposed approach is evaluated using five benchmark scientific workflow datasets. To verify the performance, the proposed algorithm's results are compared to those of conventional and specialised WSAs. In comparison to previous methodologies, the average re

关键词： Heterogeneous computing system Workflow scheduling artificial algae algorithm MAA algorithm

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An improved artificial algae algorithm integrated with differential evolution for job-shop scheduling problem

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JOURNAL OF INTELLIGENT MANUFACTURING 2023年第4期34卷 1763-1778页

作者： Ibrahim, Abdelmonem M. Tawhid, Mohamed A. Al Azhar Univ Fac Sci Dept Math Assiut Branch Assiut Egypt Thompson Rivers Univ Fac Sci Dept Math & Stat Kamloops BC V2C 0C8 Canada

For the past decades, practitioners and researchers have been fascinated by the job-shop scheduling problems (JSSP) and have proposed many pristine meta-heuristic algorithms to solve them. JSSP is an NP-hard problem and a combinatorial optimization problem. This paper proposes a highly efficient and superior performance strategy for the artificial algae algorithm (AAA) integrated with the differential evolution (DE), denoted AAADE, to solve JSSP. The new movement algae colonies using DE operators are introduced to the proposed hybrid artificial algae algorithm and DE (AAADE). To improve AAA's intensification ability, the movement using the DE mutation is implemented into the AAA. In the new hybrid method, the DE crossover can update its position based on both movements (helical and DE movements) to increase randomization. Two categories of problems verify the efficiency and validity of the proposed hybrid algorithm, AAADE, namely, CEC 2014 benchmark functions and different job-shop scheduling problems. The AAADE results are compared with other algorithms in the literature. Hence, comparisons numerical experiments validated and verified the quality of the proposed algorithm. Experimental results validate the effectiveness of the proposed hybrid method in producing excellent solutions that are promising and competitive to the state-of-the-art heuristic-based algorithms reported in the literature in most of the benchmark functions in CEC'14 and JSSP.

关键词： artificial algae algorithm Bio-inspired algorithm Differential evolution Job-shop scheduling problem Metaheuristics

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Clustering analysis through artificial algae algorithm

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2022年第4期13卷 1179-1196页

作者： Turkoglu, Bahaeddin Uymaz, Sait Ali Kaya, Ersin Konya Tech Univ Fac Engn & Nat Sci Dept Comp Engn G Block G-339 TR-42250 Konya Turkey

Clustering analysis is widely used in many areas such as document grouping, image recognition, web search, business intelligence, bio information, and medicine. Many algorithms with different clustering approaches have been proposed in the literature. As they are easy and straightforward, partitioning methods such as K-means and K-medoids are the most commonly used algorithms. These are greedy methods that gradually improve clustering quality, highly dependent on initial parameters, and stuck a local optima. For this reason, in recent years, heuristic optimization methods have also been used in clustering. These heuristic methods can provide successful results because they have some mechanism to escape local optimums. In this study, for the first time, artificial algae algorithm was used for clustering and compared with ten well-known bio-inspired metaheuristic clustering approaches. The proposed AAA clustering efficiency is evaluated using statistical analysis, convergence rate analysis, Wilcoxon's test, and different cluster evaluating measures ranking on 25 well-known public datasets with different difficulty levels (features and instances). The results demonstrate that the AAA clustering method provides more accurate solutions with a high convergence rate than other existing heuristic clustering techniques.

关键词： Data clustering Clustering analysis artificial algae algorithm

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Binary artificial algae algorithm for feature selection

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APPLIED SOFT COMPUTING 2022年第0期120卷 108630-108630页

作者： Turkoglu, Bahaeddin Uymaz, Sait Ali Kaya, Ersin Konya Tech Univ Fac Engn & Nat Sci Dept Comp Engn G Block G-339 TR-42250 Konya Turkey

In this study, binary versions of the artificial algae algorithm (AAA) are presented and employed to determine the ideal attribute subset for classification processes. AAA is a recently proposed algorithm inspired by microalgae's living behavior, which has not been consistently implemented to determine ideal attribute subset (feature selection) processes yet. AAA can effectively look into the feature space for ideal attributes combination minimizing a designed objective function. The proposed binary versions of AAA are employed to determine the ideal attribute combination that maximizes classification success while minimizing the count of attributes. The original AAA is utilized in these versions while its continuous spaces are restricted in a threshold using an appropriate threshold function after flattening them. In order to demonstrate the performance of the presented binary artificial algae algorithm model, an experimental study was conducted with the latest seven highperformance optimization algorithms. Several evaluation metrics are used to accurately evaluate and analyze the performance of these algorithms over twenty-five datasets with different difficulty levels from the UCI Machine Learning Repository. The experimental results and statistical tests verify the performance of the presented algorithms in increasing the classification accuracy compared to other state-of-the-art binary algorithms, which confirms the capability of the AAA algorithm in exploring the attribute space and deciding the most valuable features for classification problems. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Feature selection artificial algae algorithm Metaheuristics Binary optimization

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Hybrid convolutional neural network optimized with an artificial algae algorithm for glaucoma screening using fundus images

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JOURNAL OF INTERNATIONAL MEDICAL RESEARCH 2024年第9期52卷

作者： Eswari, M. Shanmuga Balamurali, S. Ramasamy, Lakshmana Kumar Kalasalingam Acad Res & Educ Dept Comp Applicat Krishnankoil Tamil Nadu India Higher Coll Technol Ras Al Khaymah U Arab Emirates

Objective We developed an optimized decision support system for retinal fundus image-based glaucoma *** We combined computer vision algorithms with a convolutional network for fundus images and applied a faster region-based convolutional neural network (FRCNN) and artificial algae algorithm with support vector machine (AAASVM) classifiers. Optic boundary detection, optic cup, and optic disc segmentations were conducted using TernausNet. Glaucoma screening was performed using the optimized FRCNN. The Softmax layer was replaced with an SVM classifier layer and optimized with an AAA to attain enhanced *** Using three retinal fundus image datasets (G1020, digital retinal images vessel extraction, and high-resolution fundus), we obtained accuracy of 95.11%, 92.87%, and 93.7%, respectively. Framework accuracy was amplified with an adaptive gradient algorithm optimizer FRCNN (AFRCNN), which achieved average accuracy 94.06%, sensitivity 93.353%, and specificity 94.706%. AAASVM obtained average accuracy of 96.52%, which was 3% ahead of the FRCNN classifier. These classifiers had areas under the curve of 0.9, 0.85, and 0.87, *** Based on statistical Friedman evaluation, AAASVM was the best glaucoma screening model. Segmented and classified images can be directed to the health care system to assess patients' progress. This computer-aided decision support system will be useful for optometrists.

关键词： TernausNet faster region-based convolutional neural network artificial algae algorithm support vector machine glaucoma screening fundus

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artificial neural network training using a multi selection artificial algae algorithm

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2024年 53卷

作者： Karakoyun, Murat Necmettin Erbakan Univ Comp Engn Fac Engn Konya Turkiye

artificial neural network (ANN), developed by modeling the nervous system of the human brain, is an important and effective data processing method used today. The most important and difficult process of the artificial neural network is the training process. The main purpose of the training process is to optimize the weights in the network. The fact that the number of weights increases depending on the number of connections in the neural network makes this problem difficult and complex. Many algorithms and approaches have been presented from past to present in order to overcome this problem. One of the approaches used recently for ANN training is meta- heuristic algorithms. In this study, the artificial algae algorithm (AAA), one of the meta-heuristic algorithms, was used for ANN training. By adding a new selection mechanism to the position update structure of the basic AAA, a new AAA variant named multi selection AAA (MsAAA) has been developed. The multi-selection mechanism provides AAA with different options during the position update process, enabling a more effective and highquality search. With this improvement, the success of the algorithm has been increased by reducing the risk of stuck into local best. The performance of the proposed algorithm was compared with the performance of the basic AAA and 7 different meta-heuristic algorithms. The experimental results obtained on 21 different data sets were presented comparatively with four different metrics such as sensitivity, specificity, precision and f1 -score. Additionally, the performance of the algorithms was compared one -to -one, statistically and with visual graphics. The experimental results have shown that the proposed MsAAA is quite successful and outperforms other algorithms.

关键词： artificial algae algorithm artificial neural network Meta-heuristic algorithms Multi -layer perceptron Optimization

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Semen quality predictive model using Feed Forwarded Neural Network trained by Learning-Based artificial algae algorithm

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2021年第2期24卷 310-318页

作者： Yibre, Abdulkerim M. Kocer, Baris Konya Tech Univ Fac Engn Dept Comp Engn Konya Turkey

Recent scientific studies have noted that the seminal quality of males is significantly decreasing due to lifestyle and environmental factors. Clinical diagnosis of sperm quality is one important aspect of identifying the potential of semen for the occurrence of pregnancy. Due to the advances in machine learning algorithms, especially the reliable and high classification accuracy of neural network in health related problems, it is becoming possible to predict seminal quality from lifestyle data. In this respect, a few attempts were made in predicting seminal quality. These studies were conducted using imbalanced data sets, where the performance outcomes tend to be biased towards the majority class. Other studies implemented the gradient descent technique for training the neural network. The gradient descent is a local training technique that is prone to get stuck to local minima. On the contrary, meta-heuristic algorithms enable searching solutions both locally and globally. Therefore, in this study, artificial algae algorithm that is improved using a Learning-Based fitness evaluation method is proposed for training Feed Forward Neural Network (FFNN). In addition, the SMOTE data balancing method was employed to balance normal and abnormal instances. Experimental analyses were carried out to evaluate the predictive accuracy of the FFNN trained using Learning-Based artificial algae algorithm (FFNN-LBAAA). The results were compared with well-known machine learning algorithms, namely: Multi-layer Perceptron Neural Network (MLP), Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) algorithms. The proposed approach showed superior performance in discriminating normal and abnormal semen quality instances over the other compared algorithms. (C) 2020 Karabuk University. Publishing services by Elsevier B.V.

关键词： artificial algae algorithm Feed Forwarded Neural Network Imbalanced data classification Machine learning Seminal quality

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Improving Classification Accuracy Using Hybrid of Extreme Learning Machine and artificial algae algorithm with Multi-Light Source

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INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2020年第2期28卷 213-236页

作者： Devarajan, D. Raj, R. Joshua Samuel Presidency Univ Dept Comp Sci & Engn Bengaluru 560064 India CMR Inst Technol Dept Informat Sci & Engn Bengaluru 560037 India

Among other machine learning techniques, the extreme learning machine has evidently proved its diagnostic accuracy on many cases in medical domain. Its accuracy mainly depends on the optimal parameters that are used in training. The proposed work is based on optimizing the extreme learning machine using the recently proposed meta-heuristic optimization technique named artificial algae algorithm with multi-light source. In this work, two experiments are conducted using four binary classification datasets related to medical domain. The feasible number of hidden neurons is found from the first experiment using relevant performance parameters. In the second experiment, the classifier with feasible number of hidden neurons is further evaluated with the ten-fold cross-validation method based on its computation time and classification accuracy. In both the experiments, the proposed classifier performance compared with that of other four similar hybrid approaches. It is also statistically compared using Friedman test and Wilcoxon signed rank test based on the area under curve and accuracy values respectively. It is found that the proposed classifier produces better results than the other classifiers.

关键词： Machine learning classification extreme learning machine meta-heuristic optimization artificial algae algorithm

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Improving artificial algae algorithm performance by predicting candidate solution quality

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 150卷 113298-000页

作者： Yibre, Abdulkerim Mohammed Kocer, Baris Konya Tech Univ Fac Engn Dept Comp Engn Selcuklu Konya Turkey

The success of optimization algorithms is most of the time directly proportional to the number of fitness evaluations. However, not all fitness evaluations lead to successful fitness updates. Besides, the maximum number of fitness evaluations is limited and also balance of exploration and exploitation is still challenging. Best possible solution should be found in a reasonable time. Surely it can be said more fitness evaluation takes more time. Since methods are tested under fixed numbers of maximum fitness evaluation and the duration of each fitness evaluation of a problem may vary depending on the characteristic of the problem, finding best result with fewer fitness evaluations is challenging in optimization algorithms. For that reason in this study, we proposed a new method that predicts the quality of a candidate solution before evaluation of its fitness employing Gaussian-based Naive Bayes probabilistic model. If the candidate solution is predicted to generate good result then that solution is evaluated by the objective function. Otherwise new candidate solution is created as usual. The primary purpose of the proposed method is improving the performance of AAA and at the same time preventing unnecessary fitness evaluation. The proposed method is evaluated using standard benchmark functions and CEC'05 test suite. The obtained results suggests that the new method outperformed the basic AAA and other state-of-the-art meta-heuristic algorithms with fewer fitness evaluations. Thus, the new method can be extended to cost sensitive industrial problems. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Swarm intelligence artificial algae algorithm Naive Bayes Candidate solution prediction

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