Enhanced Global-Best Artificial Bee Colony Optimization Algorithm

This paper addresses the usefulness of Artificial Bee Colony (ABC) optimization algorithm for assessment of distribution system reliability. The penalty cost functions are formulated which is related to the failure rate and repair time of each distribution segment. And also, satisfy the reliability constraint such as SAIFI, SAIDI, CAIDI and AENS. The finest values of failure rate and repair time cost function are evaluated using ABC algorithm for reliability enhancement. The performance comparison between ABC and Particle Swarm Optimization (PSO) algorithm also has been done in this paper. In addition, for showing the effectiveness of proposed methodology the numerical results have been compared to the different intelligent techniques that are available in the published literature.

Inspired by the intelligent foraging behaviour of honeybees swarm, Artificial Bee Colony (ABC) has been introduced by Karagoba in 2005. ABC algorithm has exhibited superior performance compared to other algorithms such as Genetic Algorithm (GA), Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. Despite its outstanding performance, ABC suffers from slow convergence rate and premature convergence. Hence, researchers have proposed various ABC variants but none among the variants could have averted both problems simultaneously. Hence, a new ABC algorithm has been proposed which aims to overcome the limitations. The proposed algorithm focuses on enhancing average fitness of population by mutating poor possible solutions around the fittest solution. The presented results show that the proposed algorithm is capable to avert local optima traps at faster convergence speed.

Computational swarm intelligence has been demonstrably shown to efficiently solve high-dimensional optimization problems due to its flexibility, robustness, and (low) computational cost. Despite these features, swarm-based algorithms are black boxes whose dynamics may be hard to understand. In this paper, we delve into the Fish School Search (FSS) algorithm by looking at how fish interact within the fish school. We find that the network emerging from these interactions is structurally invariant to the optimization of three benchmark functions: Rastrigin, Rosenbrock and Schwefel. However, at the same time, our results also reveal that the level of social interactions among the fish depends on the problem. We show that the absence of highly-influential fish leads to a slow-paced convergence in FSS and that the changes in the intensity of social interactions enable good performance on both unimodal and multimodal problems. Finally, we examine two other swarm-based algorithms---the Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) algorithms---and find that for the same three benchmark functions, the structural invariance characteristic only occurs in the FSS algorithm. We argue that FSS, ABC, and PSO have distinctive signatures of interaction structure and flow.

This paper presents the comparison results on the performance of the Artificial Bee Colony (ABC) algorithm for constrained optimization problems. The ABC algorithm has been firstly proposed for unconstrained optimization problems and showed that it has superior performance on these kind of problems. In this paper, the ABC algorithm has been extended for solving constrained optimization problems and applied to a set of constrained problems .

Bu çalışmada, klasik oransal-integral-türev (PID) kontrolörlerin gelişmiş hali olan kesir dereceli oransal-integral-türev (FOPID) kontrolörlerden faydalanılarak basitleştirilmiş gaz türbinli bir santral modeli için denetlemenin hassas şekilde yapılabilmesi sağlanmıştır. Klasik PID kontrolörler (denetleyiciler) üç parametre içerirken, kesir dereceli PID kontrolörler beş parametre içerir. Parametre sayısının fazla olması daha hassas denetlemenin yapılabilmesine olanak sağlar, ancak bu durum kontrolörün optimizasyonunu zorlaştırır. Kesir dereceli PID kontrolörlerin geleneksel matematiksel yöntemler ile optimizasyonu zor olduğu için; bu çalışmada, doğadan esinlenen (meta-sezgisel) optimizasyon algoritmaları arasında yer alan balina optimizasyon algoritması (BOA), salp sürüsü algoritması (SSA), yapay arı kolonisi (YAK) ve atom arama optimizasyon algoritması (AAO) kullanılmıştır. Bu dört farklı algoritmayla optimize edilen FOPID kontrolör parametreleri, basitleştirilmiş gaz türbini enerji santrali modeline uygulanmış ve sistem çıkış sinyallerinin geçici yanıt performansları karşılaştırılmıştır. Bu amaçla yerleşme süresi ve yüzde en büyük aşım, karşılaştırma kriteri olarak kullanılmıştır. Simulasyon sonuçları, yapay arı kolonisi (YAK) algoritmasıyla optimize edilmiş FOPID kontrolörün, bu santral modeli için yerleşme süresi ve yüzde en büyük aşım kriterleri açısından diğer algoritmalarla optimize edilmiş FOPID kontrolörlere göre daha iyi performans sergilediğini göstermektedir.

Designing the fuzzy controllers by using evolutionary algorithms and reinforcement learning is an important subject to control the robots. In the present article, some methods to solve reinforcement fuzzy control problems are studied. All these methods have been established by combining Fuzzy-Q Learning with an optimization algorithm. These algorithms include the Ant colony, Bee Colony and Artificial Bee Colony optimization algorithms. Comparing these algorithms on solving Track Backer-Upper problem –a reinforcement fuzzy control problem– shows that Artificial Bee Colony Optimization algorithm has the best efficiency in combining with fuzzy- Q Learning.

Classifying hyperspectral images (HSI) is inherently challenging due to their high-dimensional nature, which involves numerous spectral bands and a scarcity of labelled training samples. Deep learning techniques, particularly deep neural networks, have shown significant promise for feature extraction and effective classification of HSI. Nonetheless, the presence of redundant information across spectral bands and interference pixels within spatial patches impedes discriminative feature extraction. To address these issues, a novel approach, the Levy Artificial Bee Colony Optimized Kernel Vision Transformer-based Cascaded Neural Network (LABCO-KVCNN) is proposed to enhance and classify hyperspectral images. This methodology employs various preprocessing techniques including Gaussian filtering, interpolation, and normalization, and leverages dimensionality reduction through Kernel Principal Component Analysis (KPCA). Feature extraction is performed using the ICViT model while the LF-ABC algorithm is used for optimized band selection. Further improve classification accuracy and efficiency an optimal subset of spectral bands selected the Artificial Bee Colony (ABC) optimization algorithm. The final step is the classification of hyperspectral images with the Cascaded Convolutional neural Network (CCNN) model. Moreover, five Benchmark datasets are used for evaluation. The test outcomes show that the developed model performs better than other approaches and attained 98.6% accuracy and 96.16% average accuracy.

Unit commitment (UC) plays a key role in power system operation and control. This paper deals with UC problem using hybrid Priority List (PL) method. Evolution Programming (EP), Hybrid Evolution Programming (HEP) and Gbest based Artificial Bee Colony (ABC) optimization algorithms are applied to 10 thermal unit system and are executed to achieve minimum operating cost by optimal scheduling of thermal units over a scheduled period. It is found from the comparison of these three algorithms that the Gbest based Artificial Bee Colony (ABC) yields the best optimum solution.

Visually lossless compression for Bayer color filter array using optimized Vector Quantization

In recent decades, the classification of fake fingerprints and real fingerprint images has become an attractive research topic because of the most advanced threats. A number of research works have been carried out to classify fake and real fingerprints. But, most of the existing techniques did not utilize swarm intelligence techniques in their fingerprint classification system. Swarm intelligence has been widely used in various applications due to its robustness and potential in solving a complex optimization problem. The proposed classification methodology comprises of four steps, namely image preprocessing, feature extraction, feature selection and classification. This work uses efficient min-max normalization and median filtering for preprocessing, and multiple static features are extracted from Gabor filtering. To perform multiple static feature selection, Artificial Bee Colony (ABC) and Modified Artificial Bee Colony (MABC) optimization algorithm is used in this work to choose the best optimal static features on the basis of specific fitness values. This approach uses Fuzzy Feed Forward Neural Network (FFFNN) for classification and classification of feature vector into fake and real fingerprint images by the semi supervised graph based classification approach which enables efficient classification of real and fake fingerprints and its performance is compared with the SVM classifier. Evaluation of the performance has been carried out by taking the fingerprint images that had been collected from the FVC2000 and synthetically generated database by employing the SFinGE. The results showed that the proposed work provided improvised and enhanced results with respective to characteristics like sensitivity, precision, specification as well as classification accuracy.

The exponential growth of cloud computing has resulted in the generation and exchange of massive volumes of data, making security a major concern for both users and service providers. To overcome these limitations, the proposed methodology introduces an intelligent Intrusion Detection System (IDS) for secure data transmission in cloud environments using the KDD dataset. The process begins with Dataset Preprocessing, which involves cleaning, normalization, and feature selection to eliminate redundant or noisy attributes, ensuring improved learning efficiency. The refined data is categorized into five distinct classes: DoS, Normal, Probe, R2L, and U2R attacks. The classification phase employs an Artificial Neural Network (ANN), which effectively models complex, nonlinear relationships between input features and attack categories. To further enhance the ANN's learning performance, the Artificial Bee Colony (ABC) optimization algorithm is integrated for fine-tuning network weights and biases, achieving faster convergence and better generalization. This hybrid approach significantly improves detection accuracy, minimizes computational overhead, and reduces the false positive rate compared to conventional IDS models. As a result, the proposed IDS ensures robust data protection and reliable intrusion detection, making it highly suitable for dynamic cloud computing environments.

This research delves into the dynamics of a retrial queueing system featuring heterogeneous servers with intermittent availability, incorporating feedback and working vacation mechanisms. Employing a matrix geometric approach, this study establishes the steady-state probability distribution for the queue size in this complex heterogeneous service model. Additionally, a range of system performance metrics is developed, alongside the formulation of a cost function to evaluate decision variable optimization within the service system. The Artificial Bee Colony (ABC) optimization algorithm is harnessed to determine service rates that minimize the overall cost. This work includes numerical examples and sensitivity analyses to validate the model's effectiveness. Also, a comparison between the numerical findings and the neuro-fuzzy results has been examined by the adaptive neuro fuzzy interface system (ANFIS).

Cost Optimization of an Unreliable server queue with two stage service process under hybrid vacation policy

In the Internet of Things (IoT), billions of physical devices, distributed over a large geographic area, provide a near real-time state of the world. By adopting a service-oriented paradigm, the capabilities of mobile or static devices can be abstracted as IoT services and delivered to users in a demand-driven way. In service environments, a particular service provisioning tends to be specified in a service level agreement (SLA), which can be further used to monitor and guarantee the quality of service (QoS). Automatic SLA negotiation can be used to resolve possible conflicts between trading parties, but existing SLA negotiation approaches do not consider the characteristics of an IoT environment. In this paper, we present an automated negotiation strategy for multi-round bilateral negotiation that caters for the level of dynamicity in an IoT environment. The negotiation strategy makes concessions based on the artificial bee colony (ABC) optimization algorithm. The simulation results demonstrate that our proposal provides a better balance between success rate and negotiation utility, compared to other approaches.

The Internet of Things (IoT) represents a cutting-edge technical domain, encompassing billions of intelligent objects capable of bridging the physical and virtual worlds across various locations. IoT services are responsible for delivering essential functionalities. In this dynamic and interconnected IoT landscape, providing high-quality services is paramount to enhancing user experiences and optimizing system efficiency. Service composition techniques come into play to address user requests in IoT applications, allowing various IoT services to collaborate seamlessly. Considering the resource limitations of IoT devices, they often leverage cloud infrastructures to overcome technological constraints, benefiting from unlimited resources and capabilities. Moreover, the emergence of fog computing has gained prominence, facilitating IoT application processing in edge networks closer to IoT sensors and effectively reducing delays inherent in cloud data centers. In this context, our study proposes a cloud-/fog-based service composition for IoT, introducing a novel fuzzy-based hybrid algorithm. This algorithm ingeniously combines Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) optimization algorithms, taking into account energy consumption and Quality of Service (QoS) factors during the service selection process. By leveraging this fuzzy-based hybrid algorithm, our approach aims to revolutionize service composition in IoT environments by empowering intelligent decision-making capabilities and ensuring optimal user satisfaction. Our experimental results demonstrate the effectiveness of the proposed strategy in successfully fulfilling service composition requests by identifying suitable services. When compared to recently introduced methods, our hybrid approach yields significant benefits. On average, it reduces energy consumption by 17.11%, enhances availability and reliability by 8.27% and 4.52%, respectively, and improves the average cost by 21.56%.