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It is very difficult and expensive to replace sensor node battery in wireless sensor network in many critical conditions such as bridge supervising, resource exploration in hostile locations, and wildlife safety, etc. The natural choice in such situations is to maximize network lifetime. One such approach is to divide the sensing area of wireless sensor network into clusters to achieve high energy efficiency and to prolong network lifetime. In this paper, an Artificial Bee Colony Inspired Clustering Solution (ABCICS) is introduced. The proposed protocol selects the head of the cluster with optimal fitness function. The fitness function comprises the residual energy of node, node degree, node centrality, and distance from base station to node. When cluster-head with high energy node transmits the data to the base station, it further minimizes the energy consumption of the sensor network. The presented protocol is compared with LEACH, HSA-PSO, and MHACO-UC. Simulation experiments show the effectiveness of our approach to enhance the network lifetime.

Abstract: Data clustering is a popular data analysis technique needed in many fields. Recent years, some swarm intelligence-based approaches for clustering were proposed and achieved encouraging results. This paper presents a Hybrid Artificial Bee Colony (HABC) algorithm for data clustering. The incentive mechanism of HABC is enhancing the information exchange (social learning) between bees by introducing the crossover operator of Genetic Algorithm (GA) to ABC. With a test on ten benchmark functions, the proposed HABC algorithm is proved to have significant improvement over canonical ABC and several other comparison algorithms. The HABC algorithm is then employed for data clustering. Six real datasets selected from the UCI machine learning repository are used. The results show that the HABC algorithm achieved better results than other algorithms and is a competitive approach for data clustering. [Copyright &y& Elsevier]

Abstract: In this paper, a novel multi-objective artificial bee colony algorithm is presented to solve the distribution system reconfiguration and hybrid (photo voltaic/wind turbine/fuel cell) energy system sizing. The purposes of the multi-objective optimization problem include the total power loss, the total electrical energy cost, and the total emission produced by hybrid energy system and the grid minimization, and the voltage stability index (VSI) of distribution system maximization. In the proposed algorithm, an external archive of non-dominated solutions is kept which is updated in each iteration. In addition, for preserving the diversity in the archive of Pareto solutions, the crowding distance operator is used. This algorithm is tested on 33 bus distribution systems and obtained non-dominated solutions are compared with the well-known NSGA-II and MOPSO methods. The solutions obtained by the MOABC algorithm have a good quality and a better diversity of the Pareto front compared with those of NSGA-II and MOPSO methods. [Copyright &y& Elsevier]

Abstract: Selection of the optimal values of different process parameters, such as pulse duration, pulse frequency, duty factor, peak current, dielectric flow rate, wire speed, wire tension, effective wire offset of wire electrical discharge machining (WEDM) process is of utmost importance for enhanced process performance. The major performance measures of WEDM process generally include material removal rate, cutting width (kerf), surface roughness and dimensional shift. Although different mathematical techniques, like artificial neural network, gray relational analysis, simulated annealing, desirability function, Pareto optimality approach, etc. have already been applied for searching out the optimal parametric combinations of WEDM processes, but in most of the cases, sub-optimal or near-optimal solutions have been arrived at. In this paper, an attempt is made to apply six most popular population-based non-traditional optimization algorithms, i.e. genetic algorithm, particle swarm optimization, sheep flock algorithm, ant colony optimization, artificial bee colony and biogeography-based optimization for single and multi-objective optimization of two WEDM processes. The performance of these algorithms is also compared and it is observed that biogeography-based optimization algorithm outperforms the others. [Copyright &y& Elsevier]

This paper deals with methods for clustering of continuous signals such as time series data sets. Centers of classes are determined with the help of the neural network with process input, which is an extension of the traditional artificial neural network into the time domain. Collaborative Artificial Bee Colony algorithm is based on the search of food behaviour of honey bees for training in a non-trajectory way. An Enhancement has been done to the original Artificial Bee Colony (ABC) algorithm and was used to discover suitable domain specific architectures. The C-ABC has great explorative search features and better convergence compared to the original algorithm and it was proved empirically that it avoids local minima by promoting exploration of the search space. In SPNN(Self Organizing Process Neural Network), the inputs and weights are related to instantaneous conditions. The proposed algorithm results in clustering the data sets with reduced error rate and better convergence rate. The tests are conducted on empirical data in matlab. [ABSTRACT FROM AUTHOR]

MANET (Mobile Ad-hoc Network) is a mobile multi-hop wireless self-organized distributed ad-hoc network thatdoes not require the basic internal construction. Routing in MANET is a challenging problem which draws researcher's vision, due to nodes mobility, dynamic topology and lack of central point like base station or servers. Clustering of devices in MANET could reduce overhead, flooding and collision in communication and make the network topology more stable. The ABC (Artificial Bee Colony) algorithm is a new meta-heuristic population based optimization technique inspired by the intelligent foraging behavior of honeybee swarms. In this paper, a new general framework has been proposed for achieving QoS which provides a cluster based routing and applies Artificial Bee Colony optimization Technique for effective optimal route discovery in MANET. It works on the principle of collective intelligence and emergence of artificial honey bees. It's application in ad-hoc networks involves swarming agents (honey bees) hopping node to node, analyzing a set of variables exposed by the nodes and provide the optimal path and minimize the routing overhead and also provides QoS guarantees with an ability for minimal end-to-end delay and enhanced scalability. [ABSTRACT FROM AUTHOR]

This paper proposes a financial time series prediction model, namely enhanced Artificial Bee Colony-Least Squares Support Vector Machines (eABC-LSSVM). As the performance of LSSVM highly depends on its hyper parameters, namely regularization parameter, γ and kernel parameter, σ², a great deal of attention was paid in determining the parameters of interest. The mutation strategy is applied in improving standard ABC which also acts as a prevention of premature convergence. To see the effectiveness of the proposed model, it was compared with standard ABC and Cross-Validation (CV). The models were demonstrated using three correlated metals price time series, which are gold, silver and palladium. By using time series from 2008-2011, the prediction performance was evaluated using mean Absolute Percentage Error (MAPE). Analysis of the experimental results revealed that LSSVM model achieves better prediction performance when hybrid with eABC. The encouraging results showed that eABC-LSSVM could be applied as an effective prediction tool in the context study. [ABSTRACT FROM AUTHOR]

As the usage and development of wireless sensor networks increases, problems related to these networks are being discovered. Dynamic deployment is one of the main issues that directly affect the performance of wireless sensor networks. In this paper, an artificial bee colony algorithm is applied to the dynamic deployment of mobile sensor networks to gain better performance by trying to increase the coverage area of the network. The good performance of the algorithm shows that it can be utilized in the dynamic deployment of wireless sensor networks. [ABSTRACT FROM AUTHOR]

In this paper, a methodology to execute the best unit sizing for Distributed Energy Resources (DER) in MicroGird (MG), has been improved. Based on Artificial Bee Colony (ABC) a renovated optimal unit sizing technique was improved to estimate the optimum system formation that can obtain the customers needed Loss of Power Supply Probability (LPSP) with a minimum Cost of Energy (COE). The suggested method consequences are confirmed for single source DG and hybrid DG with results obtained from HOMER and Genetic Algorithm (GA) for the similar test systems. reserved. [ABSTRACT FROM AUTHOR]

Abstract: To automatically extract T–S fuzzy models with enhanced performance from data is an interesting and important issue for fuzzy system modeling. In this paper, a novel methodology is proposed for this issue based on a three-step procedure. Firstly, the idea of variable length genotypes is introduced to the artificial bee colony (ABC) algorithm to derive a so-called Variable string length Artificial Bee Colony (VABC) algorithm. The VABC algorithm can be used to solve a kind of optimization problems where the length of the optimal solutions is not known as a priori. Secondly, fuzzy clustering without knowing cluster number as a priori is viewed as such kind of optimization problem. Thus, a novel version of Fuzzy C-Means clustering technique (VABC-FCM), holding powerful global search ability, is proposed based on the VABC algorithm. Use of VABC allows the encoding of variable cluster number. This makes VABC-FCM not require a priori specification of the cluster number. Finally, the proposed VABC-FCM algorithm is used to extract T–S fuzzy model from data. Such VABC-FCM based convenient T–S fuzzy model extraction methodology does not require a specification of rule number as a priori. Some artificial data sets are applied to validate the performance of the convenient T–S fuzzy model. The experimental results show that the proposed convenient T–S fuzzy model has low approximation error and high prediction accuracy with appreciate rule number. Moreover, the convenient T–S fuzzy model is used to model the characteristics of superheated steam temperature in power plant, and the results suggest the powerful performance of the proposed method. [Copyright &y& Elsevier]

Abstract: The ABC algorithm has been used in many practical cases and has demonstrated good convergence rate. It produces the new solution according to the stochastic variance process. In this process, the magnitudes of the perturbation are important since it can affect the new solution. In this paper, we propose a self adaptive artificial bee colony, called self adaptive ABC, for the global numerical optimization. A new self adaptive perturbation is introduced in the basic ABC algorithm, in order to improve the convergence rates. 23 benchmark functions are employed in verifying the performance of self adaptive ABC. Experimental results indicate our approach is effective and efficient. Compared with other algorithms, self adaptive ABC performs better than, or at least comparable to the basic ABC algorithm and other state-of-the-art approaches from literature when considering the quality of the solution obtained. [Copyright &y& Elsevier]

Mobile ad hoc networks (MANETs) are multi-hop wireless networks of mobile nodes constructed dynamically without the use of any fixed network infrastructure. Due to inherent characteristics of these networks, malicious nodes can easily disrupt the routing process. A traditional approach to detect such malicious network activities is to build a profile of the normal network traffic, and then identify an activity as suspicious if it deviates from this profile. As the topology of a MANET constantly changes over time, the simple use of a static profile is not efficient. In this paper, we present a dynamic hybrid approach based on the artificial bee colony (ABC) and negative selection (NS) algorithms, called BeeID, for intrusion detection in AODV-based MANETs. The approach consists of three phases: training, detection, and updating. In the training phase, a niching artificial bee colony algorithm, called NicheNABC, runs a negative selection algorithm multiple times to generate a set of mature negative detectors to cover the nonself space. In the detection phase, mature negative detectors are used to discriminate between normal and malicious network activities. In the updating phase, the set of mature negative detectors is updated by one of two methods of partial updating or total updating. We use the Monte Carlo integration to estimate the amount of the nonself space covered by negative detectors and to determine when the total updating should be done. We demonstrate the effectiveness of BeeID for detecting several types of routing attacks on AODV-based MANETs simulated using the NS2 simulator. The experimental results show that BeeID can achieve a better tradeoff between detection rate and false-alarm rate as compared to other dynamic approaches previously reported in the literature. [ABSTRACT FROM AUTHOR]

This paper presents solution of optimal power flow (OPF) problem of practical power system via an Artificial Bee Colony (ABC) algorithm. The objective is to minimize the total fuel cost of generation and environmental pollution caused by fossil based thermal generating units and also maintaining an acceptable system performance in terms of limits on generator reactive power outputs, bus voltages, Static VAR Compensator (SVC) parameters and overload in transmission lines. CPU times can be reduced by decomposing the problem in two subproblems, the first subproblem minimize the fuel cost of generation and environmental pollution and the second one is a reactive power dispatch so optimum bus voltages can be determined and reduce the losses by controlling tap changes of the transformers and the static Var Compensators (SVC). To verify the proposed approach and for comparison purposes, we perform simulations on IEEE 30-bus system with six generating units and on the Algerian network with 114 buses, 175 branches (lines and transformers) and 15 generators. The obtained results indicate that ABC is an easy to use, fast, robust and powerful optimization technique compared to the other global optimization method (PSO). [ABSTRACT FROM AUTHOR]

Abstract: In this paper a core reloading technique using Artificial Bee Colony algorithm, ABC, is presented in the context of finding an optimal configuration of fuel assemblies. The proposed method can be used for in-core fuel management optimization problems in pressurized water reactors. To evaluate the proposed technique, the power flattening of a VVER-1000 core is considered as an objective function although other variables such as Keff , power peaking factor, burn up and cycle length can also be taken into account. The proposed optimization method is applied to a core design optimization problem previously solved with Genetic and Particle Swarm Intelligence Algorithm. The results, convergence rate and reliability of the new method are quite promising and show that the ABC algorithm performs very well and is comparable to the canonical Genetic Algorithm and Particle Swarm Intelligence, hence demonstrating its potential for other optimization applications in nuclear engineering field as, for instance, the cascade problems. [Copyright &y& Elsevier]

Abstract: The Artificial Bee Colony (ABC) algorithm is inspired by the behavior of honey bees. The algorithm is one of the Swarm Intelligence algorithms explored in recent literature. ABC is an optimization technique, which is used in finding the best solution from all feasible solutions. However, ABC can sometimes be slow to converge. In order to improve the algorithm performance, we present a modified method for solution update of the onlooker bees in this paper. In our method, the best feasible solutions found so far are shared globally among the entire population. Thus, the new candidate solutions are more likely to be close to the current best solution. In other words, we bias the solution direction toward the best-so-far position. Moreover, in each iteration, we adjust the radius of the search for new candidates using a larger radius earlier in the search process and then reduce the radius as the process comes closer to converging. Finally, we use a more robust calculation to determine and compare the quality of alternative solutions. We empirically assess the performance of our proposed method on two sets of problems: numerical benchmark functions and image registration applications. The results demonstrate that the proposed method is able to produce higher quality solutions with faster convergence than either the original ABC or the current state-of-the-art ABC-based algorithm. [ABSTRACT FROM AUTHOR]

Abstract: The main goal of the structural optimization is to minimize the weight of structures while satisfying all design requirements imposed by design codes. In this paper, the Artificial Bee Colony algorithm with an adaptive penalty function approach (ABC-AP) is proposed to minimize the weight of truss structures. The ABC algorithm is swarm intelligence based optimization technique inspired by the intelligent foraging behavior of honeybees. Five truss examples with fixed-geometry and up to 200 elements were studied to verify that the ABC algorithm is an effective optimization algorithm in the creation of an optimal design for truss structures. The results of the ABC-AP compared with results of other optimization algorithms from the literature show that this algorithm is a powerful search and optimization technique for structural design. [ABSTRACT FROM AUTHOR]

Over the past few years, swarm intelligence based optimization techniques such as ant colony optimization and particle swarm optimization have received considerable attention from engineering researchers and practitioners. These algorithms have been used in the solution of various engineering problems. Recently, a relatively new swarm based optimization algorithm called the Artificial Bee Colony (ABC) algorithm has begun to attract interest from researchers to solve optimization problems. The aim of this study is to present an optimization algorithm based on the ABC algorithm for the discrete optimum design of truss structures. The ABC algorithm is a meta-heuristic optimization technique that mimics the process of food foraging of honeybees. Originally the ABC algorithm was developed for continuous function optimization problems. This paper describes the modifications made to the ABC algorithm in order to solve discrete optimization problems and to improve the algorithm's performance. In order to demonstrate the effectiveness of the modified algorithm, four structural problems with up to 582 truss members and 29 design variables were solved and the results were compared with those obtained using other well-known meta-heuristic search techniques. The results demonstrate that the ABC algorithm is very effective and robust for the discrete optimization designs of truss structural problems. [ABSTRACT FROM AUTHOR]

Abstract: Clustering is a popular data analysis and data mining technique. In this paper, an artificial bee colony clustering algorithm is presented to optimally partition N objects into K clusters. The Deb’s rules are used to direct the search direction of each candidate. This algorithm has been tested on several well-known real datasets and compared with other popular heuristics algorithm in clustering, such as GA, SA, TS, ACO and the recently proposed K–NM–PSO algorithm. The computational simulations reveal very encouraging results in terms of the quality of solution and the processing time required. [Copyright &y& Elsevier]

Abstract: The effective optimization of machining process parameters affects dramatically the cost and production time of machined components as well as the quality of the final products. This paper presents optimization aspects of a multi-pass milling operation. The objective considered is minimization of production time (i.e. maximization of production rate) subjected to various constraints of arbor strength, arbor deflection, and cutting power. Various cutting strategies are considered to determine the optimal process parameters like the number of passes, depth of cut for each pass, cutting speed, and feed. The upper and lower bounds of the process parameters are also considered in the study. The optimization is carried out using three non-traditional optimization algorithms namely, artificial bee colony (ABC), particle swarm optimization (PSO), and simulated annealing (SA). An application example is presented and solved to illustrate the effectiveness of the presented algorithms. The results of the presented algorithms are compared with the previously published results obtained by using other optimization techniques. [Copyright &y& Elsevier]

• Activation and allocation of flood control capacity for the cascade reservoir. • The 'reverse order impoundment method of cascade reservoir' mode was proposed. • The improved artificial bee colony algorithm was adopted to solve the optimal model. • The flood control standard downstream was improved greatly. • The peak inflow to the Three Gorges Reservoir was reduced. Flood control in the Yangtze River Basin has always been a thorny issue in Chinese history. Along with the development of water science and technology, a large number of reservoirs have been constructed in the Yangtze River Basin, especially the progress of the reservoir operation theory, the joint flood control simulation and decision-making of reservoir groups has been applied extensively, which proved an effective way to reduce the flood risks and losses in the middle and lower reaches of the Yangtze River Basin. However, an increase in the constraints of the reservoir operation (or a single reservoir becoming a multiple-reservoir system) results in an exponential increase in the computational dimensions of the joint flood control simulation, often referred to as the 'curse of dimensionality'. Therefore, based on the aggregation-decomposition (AGDP) method, this paper proposes a cascaded reservoir group integrated operation mode, namely, the 'reverse order impoundment method of cascade reservoir' (ROIMCR) mode, and adopts the improved artificial bee colony (IABC) algorithm to solve the optimization problem of the ROIMCR mode (IABC-ROIMCR) and establish an optimal operation model for cascade reservoir joint flood control. The results show that: 1. With the 'conventional operation' (CO) mode, the ROIMCR mode has more advantages, the average peak flow reduction rate with 32.89% is higher than that of the CO mode 2.91%. 2. In solving the flood optimization operation problem of the cascade reservoir system, the application of the IABC algorithm can effectively reduce the dimensions in the mathematical mode, and has evident effects. The average peak flow reduction rate in the IABC-ROIMCR mode is 44.87%, and 14.89% higher than those of the CO mode. 3. With the IABC-ROIMCR mode, the flood crest standard of cascade reservoirs can be reduced from a 100-year return period to less than a 5-year period, and the flood crest standard of the Lizhuang Hydrological Station from more than a 50-year return period to less than 5-year period. The flood crest standard of the Zhutuo Hydrological Station can be reduced from a 100-year return period to approximately a 5-year period, thereby greatly improving the flood control standard in the upper and middle reaches of the Yangtze River Basin. In addition, the peak inflow to the Three Gorges Reservoir is reduced, and as a result, the flood control pressure in the lower reaches of the Yangtze River Basin is alleviated to a certain extent. [ABSTRACT FROM AUTHOR]

Truck-drone hybrid delivery is a hybrid one combining the advantages including large capacity of truck and high travel speed of drone together. Vehicle routing problem with drones (VRP-D) is a common one in the above delivery system. In this paper, VRP-D is addressed and a new dynamical artificial bee colony (DABC) is employed to minimize the overall operational cost. Two bee swarms are produced and an effective evaluation process is used to determine employed bee swarm and onlooker bee swarm dynamically. Variable neighborhood descent is constructed by using 15 neighborhood structures and adopted in employed bee phase and onlooker bee phase in different ways. A number of experiments are conducted on 112 instances and the computational results reveal that DABC provides new best solutions for 37 instances and has promising advantages on VRP-D. [ABSTRACT FROM AUTHOR]

A survey of the relevant literature shows that there have been many studies of the portfolio optimization problem, and that the number of these studies that have been based on heuristic techniques is quite high. We present a heuristic approach to the portfolio optimization problem using the artificial bee colony technique. As a test dataset, we use weekly prices from March 1992 to September 1997 from the following indices: Hang Seng in Hong Kong, DAX 100 in Germany, FTSE 100 in the UK, S&P 100 in the USA and Nikkei in Japan. This test dataset also includes daily prices from May 2013 to April 2016 from the XU030 and XU100 indices in Turkey. In this study, the cardinality-constrained mean–variance portfolio optimization model is treated as a mixed quadratic and integer programming problem, for which heuristic approaches are appropriate. The results of this study are compared with those of genetic algorithms, tabu search, simulated annealing, particle swarm optimization, a differential evaluation algorithm, a greedy randomized adaptive search procedure, an artificial bee colony, ant colony optimization, and a variable neighborhood search algorithm. The purpose of this paper is to present a relatively efficient and effective heuristic method to the portfolio optimization problem. The results show that the proposed artificial bee colony approach achieves these aims. [ABSTRACT FROM AUTHOR]

In this paper, we consider the Homicidal Chauffeur (HC) problem as an interesting and practical differential game. At first, we introduce a bilevel optimal control problem (BOCP) and prove that a saddle point solution for this game exists if and only if this BOCP has an optimal solution in which the optimal value of the objective function is equal to $1$. Then, BOCP is discretized and converted to a nonlinear bilevel programming problem. Finally, an Artificial Bee Colony (ABC) algorithm is used for solving this problem, in which the lower-level problem will be considered as a constraint and solved by an NLP-solver. Finally, to demonstrate the effectiveness of the presented method, various cases of HC problem are solved and the simulation results are reported.

An islanded microgrid (IMG) having renewable energy sources (RES), dispatchable sources and battery energy storage (BES) is an effective approach to supply the electricity in remote areas. The reliable operation of IMG is quite complex and challenging task, due to uncertain behaviour of RES, loads and energy bid. In this paper, an IMG is simulated to maximise the revenue considering a dynamic optimisation objective subjected to various constraints of system components for two different cases, i.e. considering the stationary and the dynamic energy bid. Artificial bee colony (ABC) and its variants based on global best (GABC), and global and personal best with asynchronous scaling factors (GPSABC) are implemented and the obtained results are also compared with the existing optimisation techniques. Results of GPSABC improve the revenues and offers better accuracy and stability parameters. Further, the effect on revenue results with different ramp rates of diesel generators is described through GPSABC.

Artificial bee colony algorithm is an effective algorithm for parameter optimization, but the traditional artificial bee colony algorithm is liable to fall into local extreme points at a later stage. In this paper, we propose an improved artificial bee colony optimization algorithm, which solves the problems of premature convergence and falling into the local extreme value in the classification of hyperspectral images. First we use an improved chaotic sequence with higher randomness to initialize and update nectar sources to expand the distribution of nectar sources. Secondly, the optimized adaptive step size is introduced into the neighborhood search to speed up the algorithm convergence and improve the search efficiency. Then we build an improved artificial bee colony algorithm support vector machine optimization model to mine the optimal values of penalty factor C and kernel function parameter σ. Next, the model was used to perform classification experiments on two hyperspectral images (University of Pavia, Indian Pine) with different attributes, and compared with the traditional bee colony algorithm, genetic algorithm, and particle swarm algorithm. Experimental results on HSI datasets demonstrate the superiority of the proposed method over several well-known methods in both classification accuracy and convergence speed.

In recent years, system maintenance has become a hot topic that has attracted significant research interests from both academia and industry, and has found applications in many areas. A modern system usually consists of many components, among which there may be different dependencies. The maintenance strategy that ignores the dependencies among components cannot meet the actual engineering requirements and may even affect the usability of the entire system. Therefore, in this paper, we study the economic dependence of equipment cost and duration sharing when components are replaced at the same time. By characterizing the relationship between the effective service age and reliability of components using the Weibull distribution, we establish a selective maintenance model of multi-state complex system with economic dependence. The discrete artificial bee algorithm is used as the global optimization method to solve the dual- objective optimization model and obtain the Pareto solution set of the maintenance level of each component. A case study of the Shooman network system is performed to demonstrate the usefulness of the model and the importance of considering the economic dependencies between components in maintenance decisions.

Lifetime is one of the most critical indexes of the Wireless Sensor Network (WSN). In this paper, we propose a clustering protocol based on the meta-heuristic approach (CPMA). CPMA takes the network lifetime as the primary consideration and consists of two parts. The first part focuses on the online cluster head selection and network communication coordination. The selection is based on the Harmony Search (HS) Algorithm, which aims to reduce the total energy dissipation and smooth the energy distribution throughout the network. Currently, most clustering protocols cannot automatically tune the corresponding protocol parameters according to the diversity of different WSNs. To solve such issue, the second part of CPMA uses the Artificial Bee Colony (ABC) algorithm to optimize its crucial parameters. The optimization is offline and will be executed only once before the network is working. We make a detailed comparison of CPMA with classical clustering protocols. The results show that CPMA can better prolong the network lifetime and improve network throughput under almost all the network conditions. Furthermore, our simulation also exhibits that CPMA has good adaptability and performs well under different network lifetime definitions. All the results prove that CPMA has the advantages of being suitable and efficient for a wide number of WSN applications.

Cloud computing is a modern technology for dealing with large-scale data. The Cloud has been used to process the selection and placement of replications on a large scale. Most previous studies concerning replication used mathematical models, and few studies focused on artificial intelligence (AI). The Artificial Bee Colony (ABC) is a member of the family of swarm intelligence based algorithms. It simulates bee direction to the final route and has been proven to be effective for optimization. In this paper, we present the different costs and shortest route sides in the Cloud with regard to replication and its placement between data centers (DCs) through Multi-Objective Optimization (MOO) and evaluate the cost distance by using the knapsack problem. ABC has been used to solve shortest route and lower cost problems to identify the best selection for replication placement, according to the distance or shortest routes and lower costs that the knapsack approach has used to solve these problems. Multi-objective optimization with the artificial bee colony (MOABC) algorithm can be used to achieve highest efficiency and lowest costs in the proposed system. MOABC can find an optimal solution for the best placement of data replicas according to the minimum distance and the number of data transmissions, affording low cost with the knapsack approach and availability of data replication.Low cost and fast access are characteristics that guide the shortest route in the CloudSim implementation as well. The experimental results show that the proposed MOABC is more efficient and effective for the best placement of replications than compared algorithms.

Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) are traditionally optimised independently. However recently, integrated ASP and ALB optimisation has become more relevant to obtain better quality solution and to reduce time to market. Despite many optimisation algorithms that were proposed to optimise this problem, the existing researches on this problem were limited to Evolutionary Algorithm (EA), Ant Colony Optimisation (ACO), and Particle Swarm Optimisation (PSO). This paper proposed a modified Artificial Bee Colony algorithm (MABC) to optimise the integrated ASP and ALB problem. The proposed algorithm adopts beewolves predatory concept from Grey Wolf Optimiser to improve the exploitation ability in Artificial Bee Colony (ABC) algorithm. The proposed MABC was tested with a set of benchmark problems. The results indicated that the MABC outperformed the comparison algorithms in 91% of the benchmark problems. Furthermore, a statistical test reported that the MABC had significant performances in 80% of the cases.

Abstract Unmanned aerial vehicles (UAVs) in wireless communication have received significant interests recently due to its low cost and flexibility in providing wireless connectivity in areas without infrastructure coverage. In this paper, we optimize the location of UAVs to maximize their coverage areas. We propose a modified artificial bee colony (MABC) incorporating bee number reallocation and a new search equation. In the proposed algorithm, a greater number of bees in the population are assigned to execute local searches near food sources to enhance solution accuracy, and the bees are guided by elite vectors to enable the algorithm to rapidly converge to a potentially globally optimal position. The deployment of UAVs is thus obtained. The experimental results show that the proposed algorithm achieves great improvements over the traditional algorithm.

• A fault feature extraction method for early fault detection is proposed for rotating machinery in NPPs. • VMD is applied to decompose the vibration signals to effectively suppress mode mixing and end effect. • To get the optimal signal decomposition effect, ABC algorithm is used to optimize the VMD parameters. • The weighted kurtosis index is introduced as the basis for selecting sensitive mode. • TEO is applied to strengthen the weak fault characteristics in the sensitive mode and identify the early fault. Extracting features for early failure detection in rotating machinery of nuclear power plants (NPPs) is difficult because in the early stages of failure the impact on the vibration signals is weak. To improve early fault detection in rotating machinery, a fault feature extraction method based on the combination of parameter-adaptive Variational Mode Decomposition (VMD) and Teager energy operator (TEO) is proposed in this paper. Firstly, we introduce the maximum weighted kurtosis index (WKI) as the objective function, and the Artificial Bee Colony (ABC) is used to optimize the VMD parameters. Then, the optimized VMD is used to decompose the vibration signal into multiple intrinsic mode functions (IMFs). Finally, TEO is used to demodulate the sensitive mode with the maximum WKI and identify the fault frequencies. Simulation and experiment show that the early fault features in vibration signals can be effectively extracted by the proposed method, and the comparisons with other three methods highlight the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

Among all the conventional maximum power point tracking (MPPT) techniques for a photovoltaic (PV) system that have been proposed, incremental conductance (INC) and perturb and observe (P&O) are the most popular because of their simplicity and ease of implementation. However, under partial shading conditions (PSCs), these MPPT algorithms fail to track the global maximum power point (GMPP) and instead converge into local maximum power points (LMPPs), resulting in considerable PV power loss. This paper presents a new hybrid MPPT technique combining the artificial bee colony (ABC) and P&O algorithms named ABC-P&O. The P&O technique is used to track the MPP under uniform irradiance, and only during irradiance variations is the ABC algorithm employed. The effectiveness of the proposed hybrid algorithm at tracking the GMPP, under both uniform and nonuniform irradiance conditions, was assessed by hardware-in-the-loop (HIL) tests employed by a dc–dc boost converter. Then, the ABC-P&O strategy was applied to obtain the voltage reference for the outer PI control loop, which provided the current reference to the discrete-time sliding-mode current control. The ABC-P&O algorithm has a reasonable computational cost, allowing the use of a commercial, low-priced digital signal controller (DSC) with outer voltage and inner current control loops. Many challenging tests validated that the proposed ABC-P&O technique converges fast to the GMPP with high efficiency and superior performance under different PSCs. [ABSTRACT FROM AUTHOR]

The optimization is essential in the engineering area and, in conjunction with use of meta-heuristics, has had a great impact in recent years; this is because of its great precision in search of optimal parameters for the solution of problems. In this work, the use of the Artificial Bee Colony Algorithm (ABC) is presented to optimize the values for the variables of a proportional integral controller (PI) to observe the behavior of the controller with the optimized Ti and Kp values. It is proposed using a robot built using the MINDSTORMS version EV3 kit. The objective of this work is to demonstrate the improvement and efficiency of the controllers in conjunction with optimization meta-heuristics. In the results section, we observe that the results improve considerably compared to traditional methods. In this work, the main contribution is the implementation of an optimization algorithm (ABC) applied to a controller (PI), and the results are tested to control the movement of a robot. There are many papers where the kit is used in various domains such as education as well as research for science and technology tasks and some real-world problems by engineering scholars, showing the acceptable result. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) includes various technologies, including sensing devices, Radio-Frequency Identification (RFID), and Microelectromechanical Systems (MEMS). Despite numerous advantages of IoT, security and privacy are important challenges. IoT infrastructures are frequently attacked by different invaders, including white hat hackers whose mission is to test the system's penetrability. Other attacks are orchestrated by adversaries that misuse system vulnerabilities to seize information for personal benefits. Hence, security is a key factor and fundamental requirement of IoT design. Thus, increased cyberattacks call for an appropriate strategic plan to ensure IoT security. Enhancing data security in IoT has proved to be a major concern, and one solution to mitigate this is to apply suitable encryption techniques when storing data in the IoT. An intruder will be able to control IoT devices without physical access if the network is not secure enough. To overcome this challenge, this paper proposes a security design based on Elliptic-Curve Cryptography (ECC), the SHA-256 (Secure Hash Algorithm 256) algorithm, and the Artificial Bee Colony (ABC) algorithm to boost the security of IoT-based smart irrigation systems. The proposed model applies the ABC algorithm to generate the private key for ECC. The results show that the optimal encoding and decoding times were 100 and 150 iterations, respectively. Moreover, compared to 3DES&ECC&SHA-256 and RC4&ECC&SHA-256, the total throughput of the proposed model was about 50.04% and 55.29% higher in encryption and 51.36% and 58.41% higher in decryption. The evaluation indicates a significant improvement (>50%) in the throughput rate. The performance results obtained indicate the efficiency and effectiveness of the proposed scheme in terms of performance and security. [ABSTRACT FROM AUTHOR]

Voltage collapse tends to occur due to the voltage instability created during large faults. As a last resort, under-voltage load shedding (UVLS) is performed after all the available power operation and control mechanisms have been exhausted. Load shedding techniques have advanced from the conventional and adaptive methods that are less optimal compared to computational intelligence-based techniques. Recent works have identified hybrid algorithms to give more optimal solutions for UVLS problems with multi-objective functions. In this paper, a novel hybrid ABC-PSO algorithm, adapted from a software estimation project, is used to perform UVLS on a modified IEEE 14-bus system. Eight overload conditions are imposed on the system ranging from 105% to 140% loading, where FVSI ranking is used in identifying weak buses. The load shedding is then performed following decentralized relay settings of 3.5 seconds, 5 seconds and 8 seconds, which gives an overall 99.32% recovery of voltage profiles. The proposed hybrid ABC-PSO algorithm is able to shed optimal amounts of load, giving an 89.56% post-contingency load, compared to GA's 77.04%, ABC-ANN at 84.03% and PSO-ANN at 80.96%. This study has been simulated on MATLAB software, using the Power System Analysis Toolbox (PSAT) graphical user and command-line interfaces.

This paper presents an adaptive Fractional Order PID (FOPID) controller for improving the performance of a Brushless DC (BLDC) motor using Artificial Bee Colony (ABC) algorithm. BLDC motor is desired to operate at various speed and load conditions with enhanced performance and robust speed control. In practice, the effect of longer settling time, fluctuation of steady-state error, power fluctuation and nonlinearity characteristics of the BLDC motor drive result in poor controllability. To overcome the problems, an optimized FOPID controller using the ABC algorithm in a self-tuned regulator structure is proposed to minimize the given objective function to satisfy the inequality constraints. It is also interesting to note that the usage of Hall Effect sensors has many limitations due to the failure of its components, poor reliability, need special mechanical arrangements for mounting and electrical noise aspects. In order to avoid such issues, a Kalman Filter is designed for estimating the speed of the motor. The simulation is carried out for the proposed ABC tuned FOPID controller and the results are compared with conventional genetic algorithm and modified genetic algorithm tuned FOPID controllers. The results indicate that the proposed ABC tuned controller is superior in terms of time-domain characteristics, control effort, and specified performance indices. Further to show the usefulness of the proposed method, an experimental model is developed and validated for the selected operating conditions with the required comparison.