HETEROGENEOUS SERVER RETRIAL QUEUEING MODEL WITH FEEDBACK AND WORKING VACATION USING ARTIFICIAL BEE COLONY OPTIMIZATION ALGORITHM

Artificial Bee Colony (ABC) optimization algorithm has captured much attention of researchers from various fields, in recent times. Moreover, various comparative studies clearly reports robust convergence of ABC algorithm than other bio-inspired optimization algorithms. Nevertheless, like other optimization algorithms, ABC suffers from slower convergence and tendency towards local optima trappings. Therefore, various amendments have been proposed to avertthe flaws of ABC algorithm. Nonetheless, the variants are either computationally intensive or could not avert the flaws of the algorithms. Hence, this research work proposes an efficient variant of ABC algorithm. The proposed variant capitalizes on the global-best food-source. The proposed variant has been compared with various existing variants of ABC algorithm on a few benchmark functions. Significance of the proposed variant has also been analyzed statistically. Results show the best convergence of the proposed variant among all the compared optimization algorithms on all benchmark functions.

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.

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

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.

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.

Forecasting the level of reservoir has been a significant subject in the management of reservoirs and water resource. For many years, estimation of reservoir water level was primary based on operator’s experience, curves and mathematical models. Recently, Artificial Intelligence (AI) methods are developed in several hydrological aspects, such as classification and forecasting parameters. The major advantage of AI modeling is the considerable ability to map input-output pattern without requiring prior knowledge about the factors that affect the forecasting parameters. This study attempts to forecast the daily level of Klang Gate dam using adaptive neuro fuzzy interface system (ANFIS) in two different scenarios and various time delays in inputs. In the first scenario, daily rainfall is used solely as an input in different time delays from the time (t) to the time (t-4) that is illustrated in spite of the reasonable performance of error, less than 10% of solely rainfall data could not have reasonable response in fluctuations to forecast accurately. Increasing the level of reservoir beside precipitation as inputs in both sets of models could enhance the fitness of the estimated and observed data dramatically. Due to the fact that the distance of gauges of stations are unknown, using various models in different time delays of inputs could demonstrate the distance between gauges; moreover, it shows the reasonable duration in inputs and outputs to have accurate prediction. Key words: Klang Gate, adaptive neuro fuzzy interface system (ANFIS), forecasting model.

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

Network Coding (NC) has been a solution scheme for data exchange in communications that amalgamate data packets, especially in multi-hop wireless networks. Existing studies demonstrated the use of NC in wireless networks by evaluating its key advantages. The store-code-forward approach of network coding at relay nodes brings out the most relevant benefits in the performance metrics, such as power, throughput, and goodput in communication. COPE is a fundamental protocol, which exploits link diversity and the broadcast nature of a wireless medium using the XOR criteria for data packet amalgamation. COPE is unsuccessful in the exploration of maximum compatibility packets through which opportunistic routing can be maximized. Hence, to identify maximum compatibility packets at relay nodes, the paper presents the uses of Artificial Bee Colony (ABC) optimization algorithm integrated with shortest path planning technique for optimal routing. The authors have successfully demonstrated the validation of Routing-aware Heuristic-based Opportunistic Network Coding (RHONC) mechanism in the multi-hop wireless network. The paper evaluates the proposed mechanism, and enhances throughput gain, goodput gain, and reduced energy consumption against COPE.

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.

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.

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.

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.