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Reliable and dynamic composition of web services is considered as essential for ensuring continuous services to the users since they are responsible for integrating varsity of applications in spite of their independency. The significant development in web services domain in the last two decades enable the option of devising novel service composition and service selection schemes for optimal performance and success rate of dynamic web service composition. Majority of the research works proposed for dynamic composition of web services was confirmed to be formulated using the characteristics of Quality of Service (QoS) or Transactional features of workflow. Improved Cosine Similarity-based Artificial Bee Colony Optimization Scheme for Web Service Composition (ICS-ABCO-WSC) is proposed for integrating the characteristics of QoS and transactional features for determining optimal candidate service solution from the workflow modeled graph generated during reactive service composition. ICS-ABCO-WSC is proved to enhance the rate of exploitation and exploration by incorporating opposition learning in employee bee phase and, combinatorial search strategical equations and enhance rate factor in the employee and onlooker bee phase respectively. The success rate and optimality index derived using experimental investigation of ICS-ABCO scheme is proved to be 26% and 32% excellent to compared baseline graph-modeled web service composition techniques. This improvement is realized in ICS-ABCO-WSC due to its potential of enhancing the precision and acceleration rate of converging solution achieved in Artificial Bee Colony Optimization technique.

In this paper, the modified artificial bee colony optimization algorithm, in which the accept–reject method is used as a part of the solution process in order to limit the random search space, is p...

The spread rate of COVID-19 is expected to be high in the wake of the virus's mutated strain found recently in a few countries. Fast diagnosis of the disease and knowing its severity are the two significant concerns of all physicians. Even though positive or negative diagnosis can be obtained through the RT-PCR test, an automatic model that predicts severity and the diagnosis will help medical practitioners to a great extend for affirming medication. Machine learning is an efficient tool that can process vast volume of data deposited in various formats, including clinical symptoms. In this work, we have developed machine learning models for analysing a clinical data set comprising 65000 records of patients, consisting of 26 features. An optimum set of features was derived from this data set by the proposed variant of artificial bee colony optimization algorithm. By making use of these features, a binary classifier is modelled with support vector machine for the screening of COVID-19 patients. Different models were tested for this purpose and the support vector machine has showcased the highest accuracy of 96%. Successively, severity prediction in COVID positive patients was also performed successfully by the logistic regression model. The model managed to predict three severity status viz mild, moderate, and severe. The confusion matrix and the precision-recall values (0.96 and 0.97) of the binary classifier indicate the classifier's efficiency in predicting positive cases correctly. The receiver operating curve generated for the severity predicting model shows the highest accuracy, 96.0% for class 1 and 85.0% for class 2 patients. Doctors can infer these results to finalize the type of treatment/care/facilities that need to be given to the patients from time to time.

A framework for Computer-Aided Diagnosis (CAD) to diagnose Cavitary TB and Miliary TB from chest Computed Tomography (CT) slices has been designed and implemented. The lung tissues from the CT slic...

With the continuous development of network technology and the continuous expansion of network scale, the security of the network has suffered more threats, and the attacks facing them have become more and more extensive. The frequent occurrence of network security incidents has caused huge losses. Facing an increasingly severe situation, it is necessary to adopt various network security technologies to solve the problem. Intrusion detection technology can detect internal and external network attacks, respond before the intrusion occurs, and send out alarm information for timely and effective processing. This article mainly introduces the research of cloud computing intrusion detection technology based on BP neural network (BP-NN), and intends to provide ideas and directions for the development of cloud computing intrusion detection technology based on BP-NN. This paper proposes research methods of cloud computing intrusion detection technology based on BP-NN, including BP-NN algorithm, neural network cloud computing intrusion detection technology and artificial bee colony optimization algorithm, which are used to conduct cloud computing intrusion detection technology experiment based on BP-NN; Proposed an artificial bee colony optimization neural network algorithm; designed a cloud computing intrusion detection system based on BP-NN. Experimental result shows that the average detection rate of the ABC-BP network algorithm is 92.67 %, which can effectively distinguish normal data from abnormal data.

The throughput of wireless multi-channel networks are enhanced using artificial intelligence algorithm. The performance of the network may be improved while reducing the interference. This technique involves three steps namely creation of wireless environment specific model, performance optimization using the right tools and improvement of routing by selecting the performance indicators cautiously. Artificial bee colony optimization algorithm and its evaluative features positively affects communication in wireless networks. The simple behavior of bee agents in this algorithm assist in making synchronous and decentralized routing decisions. The advantages of this algorithm is evident from the MATLAB simulations. The nature inspired routing algorithm offers improved performance when compared to the existing state-of-the-art models. The simple agent model can improve the performance values of the network. The breadth first search variant is utilized for discovery and deterministic evaluation of multiple-paths in the network increasing the overall routing protocol output.

A traditional array (TA) multiple-input multiple-output (MIMO) architecture in mmWave with hybrid beamforming suffers from high power consumption and hardware overhead. Therefore, a lens antenna subarray (LAS)-MIMO architecture has been recently proposed as a promising technology for a power-efficient system and reducing hardware cost and complexity. Additionally, the LAS-MIMO can offer spectral efficiency (SE) performance close to TA-MIMO and higher than single-lens antenna array (SLA)-MIMO. In this paper, we propose a hybrid precoding algorithm for the LAS-MIMO in mmWave to efficiently control the LAS design. The precoding problem is formulated as a sparse reconstruction problem due to the sparse behavior of mmWave channel. The proposed algorithm is an iterative process developed jointly using artificial bee colony (ABC) optimization with orthogonal matching pursuit (OMP) algorithms. In each iteration, the algorithm first selects the switches for each lens randomly using ABC and then uses OMP to approximate optimal unconstrained precoders. This process continues until achieving maximum SE. The simulation results show that LAS has around a 30% increase in SE compared to SLA while providing a significant gain in energy efficiency (EE) for single radio-frequency (RF) chain and multi RF chain scenarios. Huawei ; Nokia ; Pix Moving ; Samsung ; Technology Innovation Institute (TII)

A novel technique is proposed for selecting iterative updates and step sizes based on adaptive function values to compensate for the slow convergence rate of artificial bee colony optimization (ABCO). On this basis, a blind source separation (BSS) algorithm is proposed based on adaptive ABCO and kurtosis, which does not impose any hypothetical requirements on the source signal. By using kurtosis as the objective function, the algorithm can separate signals that follow any distribution. BSS results from various test distributions demonstrate the superior performance of the proposed algorithm compared to conventional methods.

Over the last few years, it is been experienced wireless sensor networks technologies are grabbing huge attention in almost every aspect of human lives due to its vast coverage in real-life applications. It has emerged as one of the important and very promising technologies with lots of potential from every section due to its importance in wireless information transmission. WSNs due to their useful characteristics are being considered vulnerable to several possible security attacks which may affect the performance of the system. Among these issues, most challenging issues such as sinkhole which is considered as the most dangerous attack in WSN to reduce network performance. It prevents the base station in the process of gathering complete and unmodified data from its source. This work inspired by the integration of FADE and ABC presents a new variant of the metaheuristics fuzzy adaptive differential evolution based optimization algorithm to improve the performance of a wireless sensor network.

With the tremendous development of the online information, the term “Cloud Computing” offers multiple kinds of outsourcing and storage services to the user. Data security and privacy issues introduces multiple hurdles into it such as data leakage, calculating the key for data encryption, lack of proper security policies and renewal of application in cloud environment. One of the main issues is how to store encrypted data, decrypt it when required and renewal of application to face the security challenges. In this work we have proposed a secured and timestamp value based approach using RSA-MABC algorithm for encryption and decryption process. The work has been simulated in Cloudsim and results show our approach takes less time for encryption and decryption with added security features of renewing the secret key based on timestamp. Our results of encryption and decryption time along with the results of ttest shows that our work is better than exiting approaches.

This article proposes a new intelligent control scheme that uses the Fuzzy Super Twisting Sliding Mode Concept (FSTSMC) and PID controller tuned with the Artificial Bee Colony (ABC) algori...

In recent times, the applications of cluster-based Wireless Sensor Networks (WSNs) have shown rapid growth. Many researchers are working on approaches for an efficient way of cluster formation and ...

This paper presents a real-time machine learning control (MLC) of articulated robotic manipulators using artificial bee colony optimization (ABC) algorithm incorporated with fuzzy theory. The modified ABC with dynamic weight is used to optimize the fuzzy structure and fractional order. The fractional parameters, fuzzy membership functions and rule base are determined by means of the ABC computation. This ABC-fuzzy hybrid learning algorithm is applied to real-time MLC of robotic manipulators by including fractional order proportional-integral-derivative (FOPID) control strategy. The MLC's control gain parameters are online tuned via the ABC-fuzzy optimization. With the kinematics analysis of a six-degree-of-freedom (DOF) articulated arm via reverse coordinates approach, an ABC-fuzzy MLC is developed to achieve motion control. A real-time operating system (RTOS) on a microprocessor collaborates with the ABC-fuzzy MLC to meet critical timing constraint by considering the dynamics of actuators. Finally, the mechatronic design and experimental setup of a six-DOF articulated robotic manipulator are constructed. Experimental results and comparative works are provided to demonstrate the merit of the proposed methods. Compared with the conventional control schemes, the proposed ABC-fuzzy MLC has theoretical and practice significance in term of real-time capability, online parameter tuning, convergent behavior and hybrid MLC. The proposed MLC methodologies are applicable to designing real-time modern controllers in both industry and academia.

This paper proposed an artificial bee colony optimization (ABC) algorithm based despeckling framework to overcome the effect of speckle noise present in real ultrasound images. A low pass filter and fast non-local mean filter along with Artificial Bee Colony (ABC) optimization algorithm are used for the quality enhancement of ultrasound images. The output results obtained for the real ultrasound images filtered with the proposed approach and the other most studied approaches discussed in the literature. The outperformance of the proposed method is verified by calculation of peak signal to noise ratio (PSNR), mean square error (MSE), mean absolute error (MAE), and structure similarity index (SSIM) quality measures. The proposed filtering approach is tested on eight real clinical ultrasound images of adrenal gland, appendicitis, bladder, pancreas, parathyroid gland, scrotal gland, thoracic wall, and uterus. The experimental results yield that the quantitative and qualitative results of the proposed framework are better than benchmark despeckling methods compared to real ultrasound images. Further, the proposed framework also preserves the fine details in real ultrasound images.

The worldwide demand for reduction of CO2 pollution, with more penetration of renewable energy sources and an increased number of electric vehicles (EVs), demonstrates the importance of economic dispatch (ED) with taking into account the reduction of CO2 emission. ED is a classical problem in which EVs impose more penetration as a dynamic load, and its impact as vehicle-to-grid (V2G) is the possible future trend with cost minimization. Based on the integration of EVs and hybrid renewable sources concerning both economic dispatch and pollution minimization, the multi-objective function is converted into a single comprehensive objective by using the judgment matrix methodology. In this paper, the investigation involves the minimization of the cost of all three objectives viz. operation cost, pollution cost, and carbon emissions with ED by incorporating V2G technology. The algorithms which include particle swarm optimization, as well as artificial bee colony, are applied under various operation and control strategies. The proposed models are verified and analyzed with different case studies. In terms of operation economics, the simulation results validate the superior performance of EVs based microgrid (MG) model in the coordinated charging and discharging mode. Further, the comparison of both algorithms shows better results with the ABC algorithm in terms of cost minimization of all objectives. ABC is better in V2G based microgrid with coordinated charging and discharging mode while its performance is significant during a large number of EVs (i.e., 700 EVs). Moreover, the load shedding scenarios are integrated which enables the MG system to operate in dual mode (i.e., seamless transition). In this paper, the main contribution involves penetration of EVs as dynamic load and its V2G impact in a coordinated or uncoordinated way, application of ABC algorithm for this particular load problem with improved results, and inclusion of short-term load shedding scenarios.

Classification is an important data analysis and data mining technique. Taking into account the comprehensibility of the classifier generated, an adaptive hybrid ant colony optimization algorithm called A_HACO is proposed which can effectively solve classification problem and get the comprehensible classification rules at the same time. The algorithm incorporates the artificial bee colony optimization strategy into the ant colony algorithm. The ant colony global optimization process is used to adaptively select the appropriate rule evaluation function for the data set given. Based on the classification rules obtained, the artificial bee colony optimization strategy is used to tackle the continuous attributes for further optimization of classification rules. This approach is evaluated experimentally using different standard real datasets, and compared with some proposed related classification algorithms. It shows that A_HACO can adaptively select the appropriate rule evaluation function and has better accuracy compared with related works.

The problem related to speech recognition system becomes challenging if vocabularies are having too many similar-sounding words. To overcome these types of challenges, an effective speech recognition system using artificial neural network (ANN) with optimization technique is proposed. In this system, distinct words spoken by different people are considered as input speech signal. The features of these input speech signals are extracted using amplitude modulation spectrogram. The extracted features are then the input to the ANN for training. The trained ANN inputs are used for predicting the isolated words during testing. In this work, the default structure of ANN is redesigned using Levenberg–Marquardt algorithm, to retrieve optimal prediction rate with accuracy. The hidden layers and neurons of the hidden layers are further optimized using the opposition artificial bee colony optimization technique. The outcome of the system demonstrates that the sensitivity, specificity, and accuracy of the proposed technique is 90.41%, 99.66%, and 99.36%, respectively, which is better than all the existing methods.

Plants are one of the important sources of food to the mankind. Plant diseases affect the economy of the country and also reduce the quality of agricultural product. Throughout the world, grapes are treated as a major fruit source, which contains nutrients like vitamin C. Diseases in the grape plants seriously affect the quality and production of grapes. Computer vision-based techniques have been used successfully in the last decades, to detect and classify the plants diseases. This paper deals with the identification and classification of diseases in grape leaves by using artificial bee colony (ABC) based feature selection. Initially the input images are applied with pre-processing steps which eliminate noises and background of the images. The features of color, texture and shape are extracted. ABC based attribute selection is used to find the optimal feature set. The selected features are given to the support vector machine classifier for foliar disease detection of grapes. The proposed method is experimented and compared with the other feature selection algorithms. The comparison results depict, the efficacy of the proposed method through the performance metrics such as recall, precision and classification accuracy.

We propose hybrid Artificial Bee Colony (ABC) optimization algorithms for the well-known Quadratic Assignment Problem (QAP). Large problem instances of the QAP are still very challenging. Scientists have not discovered any method to obtain the exact solutions for these difficult problems yet. The ABC has been reported to be an efficient meta-heuristic for the solution of many intractable problems. It has promising results making it a good candidate to obtain (near)-optimal solutions for well-known NP-Hard problems. The proposed ABC algorithm (ABC-QAP) and its parallel version (PABC-QAP) are the first applications of the ABC meta-heuristic together with Tabu search to the optimization of the QAP. The behavior of employed, onlooker and scout bees are modeled by using the distributed memory parallel computation paradigm for large problem instances of the QAP. Scout bees search for food sources, employed bees go to food source and return to hive and share their information on the dance area, onlooker bees watch the dance of employed bees and choose food sources depending on the dance. Robust Tabu search method is used to simulate exploration and exploitation processes of the bees. 125 of 134 benchmark problem instances are solved optimally from the QAPLIB library and 0.27% deviation is reported for 9 large problem instances that could not be solved optimally. The performance of the ABC optimization algorithms is competitive with state-of-the-art meta-heuristic algorithms in literature. (C) 2019 Elsevier B.V. All rights reserved.

Although IoT technology is advanced, wireless systems are prone to faults and attacks. The replaying information about routing in the case of multi-hop routing has led to the problem of identity deception among nodes. The devastating attacks against the routing protocols as well as harsh network conditions make the situation even worse. Although most of the research in the literature aim at making the IoT system more trustworthy and ensuring faultlessness, it is still a challenging task. Motivated by this, the present proposal introduces a trust-aware routing mechanism (TARM), which uses an edge node with mobility feature that can collect data from faultless nodes. The edge node works based on a trust evaluation method, which segregates the faulty and anomalous nodes from normal nodes. In TARM, a modified gray wolf optimization (GWO) is used for forming the clusters out of the deployed sensor nodes. Once the clusters are formed, each cluster’s trust values are calculated, and the edge node starts collecting data only from trustworthy nodes via the respective cluster heads. The artificial bee colony optimization algorithm executes the optimal routing path from the trustworthy nodes to the mobile edge node. The simulations show that the proposed method exhibits around a 58% hike in trustworthiness, ensuring the high security offered by the proposed trust evaluation scheme when validated with other similar approaches. It also shows a detection rate of 96.7% in detecting untrustworthy nodes. Additionally, the accuracy of the proposed method reaches 91.96%, which is recorded to be the highest among the similar latest schemes. The performance of the proposed approach has proved that it has overcome many weaknesses of previous similar techniques with low cost and mitigated complexity.

As the current education industry has gradually exposed many teaching difficulties, especially students' aversion to learning, the teaching effect has become more obvious. How to use the advantages of information technology to solve students' learning difficulties, reduce students' learning load, improve students' learning effects, and promote students' sustainable development is a problem that we urgently need to solve. Under the background of deepening the reform of speculative education value, through the research and analysis of previous literature, we can get the constant change of speculative education value. Through the analysis, demonstration and evaluation of the students, the artificial bee colony optimization algorithm is applied to the value analysis of speculative pedagogy. Through the comparative evaluation, the understanding is consolidated, and the sublimation is expanded in the exploration, understanding and persuading students. It also provides guidance for people's belief in the basic value standard.

Dialectics is one of the key points of TCM diagnosis. It takes the clinical symptoms of traditional Chinese medicine through dialectical diagnosis and treatment to obtain the patient’s pathological syndrome. However, this unique method of diagnosis and treatment of Chinese medicine is difficult to quantify and express, not only because of the personal subjective experience of Chinese doctors in diagnosis and treatment, but also because of the various languages and dialectical principles of Chinese medicine that are difficult to describe in detail. The development of modern information technology has made possible the intelligent of TCM syndrome differentiation. The method of optimizing BP neural network based on artificial bee colony algorithm can find the mapping relationship between TCM symptoms and TCM syndrome types, and then realize the information of TCM syndrome differentiation auxiliary diagnosis and treatment. The intelligent auxiliary diagnosis and treatment system of Chinese medicine can realize the diagnosis and treatment of syndromes from symptoms to syndrome types, and provide reference and convenience for Chinese doctors to see a doctor.

The problem of vehicle delay is one of the higher complaint rates among the passenger opinions of bus companies, and it is also one of the important reasons why citizens give up choosing public transportation. Estimating bus arrival time is of great significance for improving passenger satisfaction and improving the problem of urban traffic congestion. In this paper, the vehicle GPS data were used to classify the data according to different scenarios, and then the BP neural network was trained and compared with the artificial colony optimization BP neural network. The comparison results show that artificial bee colony optimization BP neural network algorithm is superior to BP neural network algorithm, so we select the better artificial bee colony optimization BP neural network algorithm to establish a vehicle arrival time prediction model.

Automatic text summarization methods are increasingly needed nowadays. Extractive multi-document summarization approaches aim to obtain the main content of a document collection at the same time that the redundant information is reduced. This can be addressed from an optimization point of view. There is a lack of multi-objective approaches applied in this context. In this paper, a Multi-Objective Artificial Bee Colony (MOABC) algorithm has been designed and implemented for this task. Experiments have been performed based on datasets from Document Understanding Conference (DUC) and model performances have been evaluated with Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics, as is usual in this knowledge field. The results of the proposed approach show important improvements, i.e., in average, 31.09% (8.43%) and 18.63% (6.09%) of improvement in ROUGE-2 (ROUGE-L) have been obtained with respect to the best single-objective and multi-objective results in the scientific literature. Even more, the proposed approach has been proven to produce more concentrated ROUGE values when the algorithm execution is repeated (between 620.63% and 1333.95% of reduction in the relative dispersion, that is, between 6 and 13 times better), leading to more robust results.

In cluster analysis, determining number of clusters is an important issue because information about the most appropriate number of clusters do not exist in the real-world problems. Automatic clustering is a clustering approach which is able to automatically find the most suitable number of clusters as well as divide the instances into the corresponding clusters. This study proposes a novel automatic clustering algorithm using a hybrid of improved artificial bee colony optimization algorithm and K-means algorithm (iABC). The proposed iABC algorithm improves the onlooker bee exploration scheme by directing their movements to a better location. Instead of using a random neighborhood location, the improved onlooker bee considers the data centroid to find a better initial centroid for the K-means algorithm. To increase efficiency of the improvement, the updating process is only applied on the worst cluster centroid. The proposed iABC algorithm is verified using some benchmark datasets. The computational result indicates that the proposed iABC algorithm outperforms the original ABC algorithm for automatic clustering problem. Furthermore, the proposed iABC algorithm is utilized to solve the customer segmentation problem. The result reveals that the iABC algorithm has better and more stable result than original ABC algorithm.

In this work, a new technique is introduced for optimal incorporation of shunt capacitors (SCs) in distribution networks. This technique has been compared to other sensitivity-based approaches such as loss sensitivity factor, index vector method, power loss index, and index of voltage stability. In the proposed technique, the optimal positions as well as the ratings of SCs are identified through an optimization algorithm. In sensitivity-based approaches, the positions of SCs are determined through a sensitivity approach and the optimal ratings of SCs are computed through an optimization algorithm. The main target of this study is to minimize the total annual cost and power loss of the network under load growth. This has been done through the population-based Gbest-guided artificial bee colony (GABC) optimization technique. Furthermore, the outcomes obtained through the GABC algorithm are compared to those from the iteration particle swarm optimization algorithm. The whole work along with the proposed methodology has been demonstrated on standard 34-bus and 118-bus distribution networks for SC placement. The results show that it reduces the total annual expense of the network to a great value. Consequently, it improves the total power loss reduction, enhances the voltage profile and power factor, and reduces the total voltage deviation. The obtained numerical outcomes through the proposed technique have been compared with the published literature outcomes to show the viability and superiority of the algorithm.

Most primaryconcern in wireless sensor networks (WSN) is management of energy of the small nodes deployed for sensing physical or environmental conditions of an area. However appropriate use of routing protocols can increase the network life. Protocols based on clustering such as LEACH is a competent approach, in which cluster has been planned; each cluster has plentiful nodes and lone cluster head (CH). Node transmits their sensed information to CH; CH cumulates that information and forwards that to sink. Soft-computing (SC) technique such as nature inspired algorithms are use to deal with the complex constraints in WSNs. One of such technique is Artificial Bee Colony Optimization (ABCO) that is based on swarm intelligence. In this paper, ABCO based LEACH algorithm is tested broadly on diverse scenarios of WSNs, changing maximum number of rounds (rmax) as well as number of sensor nodes (n). Multiple numbers of parameters such as dead nodes per round, alive nodes per round and packet to base station (BS) per round, are taken into concern for performance evaluation. Comparison of each parameter from conventional LEACH is also presented. The simulation has been done in MATLAB.

Water spray is an economical and environment-friendly alternative to mitigate gas explosion. This study conducts explosion risk analysis (ERA)-based water spray mitigation analysis of ultra-deep-water semi-submersible platform. 5000 gas explosion simulations with and without water spray are conducted by using FLACS codes. The outcome demonstrates that water spray with droplet size 700 μm and water application rate (WAR) 26 L/m2 min is most effective to mitigate explosion scenarios. The droplet size is the significant factor to stimulate the mitigation effect of water spray under middle and small exceedance frequencies, e.g., 1E-05 and 1E-06. WAR becomes dominate to awaken the water spray's negative effect under large exceedance frequency, e.g., 1E-04. A precise and efficient approach of BRANN integrated with artificial bee colony optimization algorithm is used to determine the optimal water spray configuration given the desirable exceedance frequency and the expected design accidental load that the ultra-deep-water semi-submersible platform structures could resist.

Economical and reliable provision of electricity has been one of the most significant research objectives since decades. With time, various economic load dispatch (ELD) techniques have emerged in power market. Apart from using these methods, changes in the use of conventional source of energy and incorporating non-conventional sources have emerged in recent years. Solar photovoltaic (PV) generation helps reducing emissions and dependency on fossil fuels. This chapter presents combined economic emission dispatch (CEED) of a hybrid thermal solar PV system. Artificial bee colony (ABC) algorithm is used as optimization tool for the scenario involving six thermal plants and thirteen solar plants. The effectiveness of this method is compared and validated with other methods available in recent literature.

A NP-hard problem, vehicular routing is a combinatorial optimization problem. Vehicular routing problem with time windows indicates vehicular routing with specified start and end time. There will be “n” number of vehicles starting from the depot to cater to the needs of “m” customers. In this paper, Gehring and Homberger benchmark problems are considered wherein the size of customers is taken to be 1000. Artificial Bee Colony Optimization algorithm is executed on these 60 datasets and the number of vehicles along with total distance covered is recorded. The modified version of Deterministic Finite Automata is applied along with the Artificial Bee Colony Optimization and the results produce 25.55% efficient routes and 15.42% efficient distance compared to simple Artificial Bee Colony Optimization algorithm.

Enhanced Artificial Bee Colony Optimization (EABCO) algorithm is presented in the paper for economic power dispatch of both thermal plants and hydro power units (fixed head) together. Different constraints considered in this paper are transmission losses, ramp rate limits of thermal generators. Two different cases are taken and the numerical results obtained are studied to test the effectiveness of this suggested technique. The results of the recommended technique are analized with the results already acquired from existing algorithms like differential evolution and evolutionary programming. This comparison draws the conclusion that the EABCO can provide better results and solution than the existing methods in terms of minimum time of computation and low fuel cost.

In this paper, a linear phase FIR filter is designed through recently proposed nature inspired optimization algorithm known as Cuckoo search (CS). A comparative study of Cuckoo search (CS), particle swarm optimization (PSO) and artificial bee colony (ABC) nature inspired optimization methods in the field of linear phase FIR filter design is also presented. For this purpose, an improved L1 weighted error function is formulated in frequency domain, and minimized through CS, PSO and ABC respectively. The error or objective function has a controlling parameter wt which controls the amount of ripple in the desired band of frequency. The performance of FIR filter is examined through three key parameters; Maximum Pass Band Ripple (MPR), Maximum Stopband Ripple (MSR) and Stopband Attenuation (As). Comparative study and the simulation results reveal that the designed filter with CS gives better performance in terms of Maximum Stopband Ripple (MSR), and Stopband Attenuation (As) for low order filter design, and for higher order it also gives better performance in term of Maximum Passband Ripple (MPR). Superiority of the proposed technique is also shown through comparison with other recently proposed methods.