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A Self-Adaptive Weights for K-Means Classification Algorithm
(2025-09-01)
Chenghu, Cui
;
Thammano, Arit
This paper presents an improved K-means clustering algorithm that addresses the traditional algorithm’s sensitivity to outlier and susceptibility to local optima by introducing an adaptive weight adjustment mechanism. It employs an exponential decay function to dynamically reduce the feature weights of outlier data points, effectively suppressing outliers while preserving the structure of the normal data. The proposed method retains the computational efficiency of standard K-means. Key contributions include: (a) A novel distance-based weighting strategy that progressively reduces the influence of noisy points, mitigating the impact of outliers on clustering performance. (b) An innovative form of "local dimensionality reduction" for outlier points via weight decay, which interferes only with the feature space of noisy regions while preserving the global topological structure of clean data. Extensive experiments on three benchmark datasets Iris (4-dimensional, balanced classes), Wine (13-dimensional, correlated features), and Wisconsin Breast Cancer Diagnosis (30-dimensional, imbalanced data) demonstrate the effectiveness of the approach. Compared to standard K-means, the proposed algorithm achieves accuracy improvements of 7.47% on Iris, 13.89% on Wine, and 19% on WBCD. This adaptive strategy offers a practical and efficient solution for clustering in noisy, high-dimensional environments, without the added complexity of mixture models.
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Solving Optimization Problems by a Hybrid Algorithm Based on Sand Cat Swarm Optimization and Invasive Weed Optimization Algorithm
(2025-01-01)
Pravesjit, Sakkayaphop
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Kantawong, Krittika
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Jitkongchuen, Duangjai
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Thammano, Arit
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Longpradit, Panchit
This paper addresses an optimization problem using hybrid algorithms of the Sand Cat Swarm Optimization (SCSO) and Invasive Weed Optimization (IWO). In this study, the reproduction step in the IWO algorithm was incorporated after the initial population step of the SCSO. The proposed algorithm was compared against the following: Intersection Mutation Differential Evolution (IMDE), Differential Evolution (DE), SCSO, and Whale Optimization Algorithm (WOA), whereby the performance was tested on six benchmark functions using a 10-fold cross validation. The results indicate that the proposed algorithm yielded the optimal solution for two out of the six benchmark functions. Additionally, when compared with the other four chosen algorithms, it yielded the best overall results. The findings suggest that the proposed algorithm is able to generate solutions similar to those obtained from the previous methods, essentially for the continuous step function, the multimodal function, and the discontinuous step function.
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Swarm Intelligence Algorithms and Their Applications
(2024-09-01)
Tansui, Daranat
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Thammano, Arit
Background and objectives: Algorithms inspired by the behavior of living organisms have been developed to apply principles from nature to solve problems in various aspects of daily life, e.g., in science, medicine, engineering, technology, and business management. The developed algorithms follow two main ideas. First, evolutionary algorithms use Darwin's principle of natural selection to choose the most suitable organisms in an unforgiving environment. Second, Swarm Intelligence Algorithms mimic the collaborative behavior of groups of living things, such as flocks of birds or schools of fish. The applications of these algorithms include medical data analysis for disease diagnosis, resource allocation planning in construction projects, traffic system design for improved safety, genetic data analysis to find relationships between genes and diseases, and business data analysis to predict customer buying behavior. These algorithms help increase efficiency, accuracy, and sustainability in solving various problems by applying principles inspired by nature. This article aims to explain working principles and performance, the advantages, disadvantages, and applications of Swarm Intelligence algorithms. Methodology: The main research steps were the following: 1. Studying and collecting research documents on 10 algorithms: Ant Colony Optimization, Artificial Bee Colony Algorithm, Bat Algorithm, Particle Swarm Optimization, Flower Pollination Algorithm, Weed Optimization Algorithm, Bee Colony Optimization, Cuckoo Search Algorithm, Bird Flocking Algorithm, and Chicken Swarm Optimization. 2. Analysis of working principles and efficiency of 10 algorithms. 3. Analyzing the advantages and disadvantages of the 10 algorithms. 4. Summarizing the applications of the 10 algorithms. 5. Research results and conclusions. Key findings: This article summarizes the applications of algorithms inspired by the behavior of living organisms to solve various daily life problems in fields such as science, medicine, engineering, technology, and business management. It also aims to explain the advantages and disadvantages of these algorithms in solving complex problems in various aspects of daily life, especially to efficiently achieve sustainable development in society and business. Some applications are such as the following: using intelligent algorithms in medical data analysis for disease diagnosis and environmental management; applying algorithms to analyze medical images for efficient diagnosis and treatment; utilizing algorithms in engineering and technology for infrastructure planning and design to enhance safety and operational efficiency; and using algorithms in business management for data analysis to predict customer behavior and marketing strategies. Implications: Swarm Intelligence algorithms, inspired by the collective behavior of living organisms, are valuable across various fields. In science, they can be utilized to locate underground oil or mineral resources through a network of sensors that collaborate to enhance efficiency in resource detection. In medicine, they aid in analyzing medical images for disease diagnosis and in complex surgeries by directing the movement of surgical robots, emulating precise, cooperative animal actions. In engineering, swarm algorithms improve intelligent traffic systems, simulating vehicle flows to alleviate congestion and plan safer routes. For technology, swarm-inspired collaborative robots excel in manufacturing, coordinating seamlessly with one another in intricate production lines. In agriculture, they are employed for managing smart farms and controlling drones for pest management. In business, swarm algorithms assist in analyzing consumer behavior by examining purchasing patterns, enabling accurate forecasting and adjustments to marketing strategies to better meet customer needs. Conclusions: This review found that Swarm Intelligence algorithms were that the 10 swarm intelligence algorithms have different working principles and performance when applied in various tasks, along with their own advantages and disadvantages and limitations in solving problems, with some algorithms being more effective in certain areas. This article also concludes that some types of algorithms can solve complex problems and can be applied in 5 different areas comprehensively. This review may be beneficial to algorithm developers and users alike.
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A Novel Classification Model Based on Hybrid K-Means and Neural Network for Classification Problems
(2024-09-01)
Chenghu, Cui
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Thammano, Arit
We propose a new classification model—a new classification model for clustering overlapping problems based on K-Means and neural networks. K-means clustering algorithm belongs to unsupervised learning. It is a classic algorithm for solving clustering problems. Since this algorithm calculates its categories based on distance, the results tend to converge to the local optimal solution and have poor boundary clustering properties. The K-Means classification algorithm defines clusters by the distance between the cluster center value and the target object, and the optimal result is obtained through continuous iteration. Therefore, clustering results are overlapped, and there are often outliers that do not belong to the current cluster, resulting in unsatisfactory clustering results. Our model offers a new method to segment non-ideal data in overlapping regions. Since clustering algorithms cannot effectively identify and classify this part of the data, we split this part of the data and train it using a neural network. The results are then integrated into the clustered data. In the experiment, the k-fold cross-validation method ensures the model stability of the results. We used the accuracy to evaluate the quality of the model, and we used standard deviation and mean deviation to detect clustering results. Five sets of experimental data from the cross-experiment show that compared with the K-Means classification model, the accuracy of our model is effectively improved.
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An Adaptive Differential Evolution with Multiple Crossover Strategies for Optimization Problems
(2024-06-01)
Farda, Irfan
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Thammano, Arit
The efficiency of a Differential Evolution (DE) algorithm largely depends on the control parameters of the mutation strategy. However, fixed-value control parameters are not effective for all types of optimization problems. Furthermore, DE search capability is often restricted, leading to limited exploration and poor exploitation when relying on a single strategy. These limitations cause DE algorithms to potentially miss promising regions, converge slowly, and stagnate in local optima. To address these drawbacks, we proposed a new Adaptive Differential Evolution Algorithm with Multiple Crossover Strategy Scheme (ADEMCS). We introduced an adaptive mutation strategy that enabled DE to adapt to specific optimization problems. Additionally, we augmented DE with a powerful local search ability: a hunting coordination operator from the reptile search algorithm for faster convergence. To validate ADEMCS effectiveness, we ran extensive experiments using 32 benchmark functions from CEC2015 and CEC2016. Our new algorithm outperformed nine state-of-the-art DE variants in terms of solution quality. The integration of the adaptive mutation strategy and the hunting coordination operator significantly enhanced DE's global and local search capabilities. Overall, ADEMCS represented a promising approach for optimization, offering adaptability and improved performance over existing variants.
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Differential Evolution for Classification: A Novel Classifier Technique in Data Mining
(2024-01-01)
Farda, Irfan
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Thammano, Arit
In the realm of Data Mining, the pursuit of innovative classification methodologies remains crucial for advancing robust techniques in handling complex and diverse datasets. This paper explores the application of Differential Evolution (DE), a powerful optimization algorithm, as a unique and effective optimization-based standalone classifier. Our exploration focused on harnessing DE's intrinsic capabilities, adapting it into a classifier while preserving its distinctiveness. The fundamental principle of DE for classification involves iteratively optimizing the center point for each class using DE operators, which are specialized mechanisms for exploring and refining solutions, and utilizing these optimized points for making predictions. The effectiveness of our model was evaluated on ten classification datasets from the UCI Machine Learning Repository and compared against three other classification methods: KNN, ZMP, and BPNN. Experimental results underscore the competitive performance of our proposed model, emphasizing the potential of DE in effectively addressing classification challenges.
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Differential Evolution with Self-Adaptive Mutation and Population Improvement Strategy for Optimization Problems
(2024-01-01)
Farda, Irfan
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Thammano, Arit
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Morris, John
Differential Evolution (DE) algorithms are widely recognized as effective metaheuristic techniques used to solve continuous optimization problems. However, DE algorithms encounter limitations in exploration and exploitation due to their sensitivity to parameter settings and reliance on mutation strategies. To overcome these drawbacks, we introduced a self-adaptive mutation and population improvement strategy in differential evolution, SAMPIDE. The algorithm adjusts the mutation operator automatically according to the successful mutation operator value from the previous generation. Additionally, to enhance the algorithm's convergence, we adopted a random learning mechanism to update the population. SAMPIDE was compared with nine state-of-the-art DE variants and four other metaheuristic algorithms across thirty-two benchmark functions. SAMPIDE consistently outperformed others on unimodal, step and noisy functions, while maintaining competitive performance on multimodal functions. Additionally, when tested on five real-world problems, SAMPIDE outperformed other metaheuristic algorithms. Thus, the self-adaptive mutation and population improvement strategy incorporated into SAMPIDE positioned it as a promising solution for a wide range of optimization problems.
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Modification of Sand Cat Swarm Optimization for Classification Problems
(2024-01-01)
Pravesjit, Sakkayaphop
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Kantawong, Krittika
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Hunta, Sathien
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Jitkongchuen, Duangjai
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Thammano, Arit
The proposed system represents an enhanced version in search of food of Sand Cat based on Levy distribution and Firework algorithm for the image classification of grape leaf diseases. In the preprocessing step, the proposed system utilizes convolution kernels to transform images into input data within the range of (0,1). Successively, the Levy distribution and Firework algorithm are incorporated into the SCSO model as an exploration search mechanism. The study employed a grape leaf dataset sourced from the Plant Village project (www.plantvillage.org), comprising 4062 labeled images measuring 256 by 256 pixels and categorized into 4 distinct classes: healthy, Black Rot, Black Measles, and Isariopsis leaf spot, which was utilized to evaluate the efficacy of the proposed system. The experimental findings demonstrate that the proposed system outperforms the analyses of VGG16, GLCM with SVM, Low contrast haze reduction-neighborhood component analysis with SVM, and SCSO.
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An Improvement on Exploration Step of Whale Optimization Algorithm with Levy Distribution for Classification Problems
(2024-01-01)
Pravesjit, Sakkayaphop
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Kantawong, Krittika
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Kamkhad, Natdanai
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Sabaiporn, Saksit
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Monchanuan, Jantawan
The proposed system represents an enhanced movement in search of food of Whale Optimization Algorithm (WOA), based on Levy distribution for image classification of grape leaf disease. In the preprocessing, the proposed system uses convolution kernels to transform images into input data within the range of (0,1). Thereafter, the Levy distribution is incorporated into the WOA model as an exploration search mechanism. A grape leaf dataset from the Plant Village project (www.plantvillage.org), consisting of 4062 labeled images with dimensions of 256 by 256 pixels and divided into four classes -healthy, Black Rot, Black Measles, and Isariopsis leaf spot -is used to evaluate the performance of the proposed system. Experimental results show that the proposed system is better than Visual Geometry Group (VGG16), Gray Level Co-occurrence Matrix (GLCM) with SVM, Low contrast haze reduction-neighborhood component analysis with SVM, and whale optimization algorithm (WOA).
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An Improved Differential Evolution Algorithm for Numerical Optimization Problems
(2023-06-01)
Farda, Irfan
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Thammano, Arit
The differential evolution algorithm has gained popularity for solving complex optimization problems because of its simplicity and efficiency. However, it has several drawbacks, such as a slow convergence rate, high sensitivity to the values of control parameters, and the ease of getting trapped in local optima. In order to overcome these drawbacks, this paper integrates three novel strategies into the original differential evolution. First, a population improvement strategy based on a multi-level sampling mechanism is used to accelerate convergence and increase the diversity of the population. Second, a new self-adaptive mutation strategy balances the exploration and exploitation abilities of the algorithm by dynamically determining an appropriate value of the mutation parameters; this improves the search ability and helps the algorithm escape from local optima when it gets stuck. Third, a new selection strategy guides the search to avoid local optima. Twelve benchmark functions of different characteristics are used to validate the performance of the proposed algorithm. The experimental results show that the proposed algorithm performs significantly better than the original DE in terms of the ability to locate the global optimum, convergence speed, and scalability. In addition, the proposed algorithm is able to find the global optimal solutions on 8 out of 12 benchmark functions, while 7 other well-established metaheuristic algorithms, namely NBOLDE, ODE, DE, SaDE, JADE, PSO, and GA, can obtain only 6, 2, 1, 1, 1, 1, and 1 functions, respectively.

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