Enhancing Artificial Bee Colony Research Articles

A Bee Colony-Based Optimized Searching Mechanism in the Internet of Things

The Internet of Things (IoT) consists of complex and dynamically aggregated elements or smart entities that need decentralized supervision for data exchanging throughout different networks. The artificial bee colony (ABC) is utilized in optimization problems for the big data in IoT, cloud and central repositories. The main limitation during the searching mechanism is that every single food site is compared with every other food site to find the best solution in the neighboring regions. In this way, an extensive number of redundant comparisons are required, which results in a slower convergence rate, greater time consumption and increased delays. This paper presents a solution to optimize search operations with an enhanced ABC (E-ABC) approach. The proposed algorithm compares the best food sites with neighboring sites to exclude poor sources. It achieves an efficient mechanism, where the number of redundant comparisons is decreased during the searching mechanism of the employed bee phase and the onlooker bee phase. The proposed algorithm is implemented in a replication scenario to validate its performance in terms of the mean objective function values for different functions, as well as the probability of availability and the response time. The results prove the superiority of the E-ABC in contrast to its counterparts.

Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks

The integration of non-orthogonal multiple access (NOMA) technology and cognitive radio networks (CRNs) promises to enhance the spectrum utilization efficiency of 5G and beyond-5G (B5G) mobile communication systems. In this article, a NOMA-based spectrum-sharing scheme is proposed for dual-hop CRNs in which a primary transmitter separated by a long distance from the primary receiver communicates via NOMA-based CRN. In this scenario, we mathematically formulate a constrained optimization problem to maximize the sum rate of all secondary users (SUs) while maintaining the total transmit power of the system. Inspired by the effectiveness of computational intelligence (CI) tools in solving non-linear optimization problems, this article proposes three CI-based solutions to the given problem aiming to guarantee quality of service (QoS) for all users. In addition, an enhanced version of the classic artificial bee colony (ABC) algorithm, referred to here as the enhanced-artificial-bee-colony (EABC)-based power allocation scheme, is proposed to overcome the limitations of classic ABC. The comparison of different CI approaches illustrates that the minimum power required by the secondary NOMA relay to satisfy the primary rate threshold of 5 bit/s/Hz is 20 mW for EABC, while ABC, PSO and GA achieve the same target at 23 mW, 27 mW and 32 mW, respectively. Thus, EABC reduces power consumption by 13.95% compared to ABC, while 29.78% and 46.15% power-saving is achieved compared to PSO and GA, respectively.

Performance analysis of vehicle-to-everything communication using internet of LoRa computing for intelligent transportation system

Intelligent Transportation System (ITS) is an emerging field nowadays that is widely utilized to improve safety measures, avoid abnormalities, and traffic flow control, and also develops the environment without hassle. So far, the deployment of sensors into vehicles and the analyzing the vehicular parameters towards the smart city applications have been achieved by the integration of LoRa-based vehicular communication. However, trust in previous design architecture should need efficient transmission, robustness, and energy efficiency. To overcome the challenges, the proposed system designed the Internet of LoRa computing enabled vehicular communication with high reliability by offering the optimization technique namely an Enhanced Artificial Bee Colony (EABC) algorithm for the localization scheme. The proposed framework consists of two sections. First, observe the objects nearby vehicles using an ultrasonic sensor that is equipped in the Arduino module with a LoRa shield. The second work contributes to the evaluation of performance metrics of vehicular communication in the sensing region with a minimum delay of two seconds using MathWorks simulation. The article designed the VANET, which utilized the LoRa architecture for Vehicle to Everything communication, and pointed out the position of the sensor nodes using a localization scheme (EABC algorithm), comparing the proposed EABC algorithm with the other optimization techniques viz Particle Swarm Optimization and Genetic algorithms in the dense nodes and it achieves 25% variation in minimizing the position error at a certain speed. Further, find the system performance by calculating the BER (Bit Error Rate) in both coherent and non-coherent with varying speeds of the vehicle and router connections and it achieves 40% variation in efficiency and realizes the network coverage in terms of the position of the vehicle in the way the proposed framework achieves the high accuracy in overall system throughput.

Query-based image tagging model using ensemble learning with enhanced artificial bee colony optimization

Digital images make up most multimedia data and are analysed in computer vision applications. Daily uploads of millions of pictures to Internet archives such as satellite image repositories complicate multimedia content and image graphs. As feature vectors, content based image retrieval (CBIR) and image classification models represent high-level image viewpoints. Observing photos recognizes objects and evaluates their significance for image enhancement. To access the visual information of big datasets, efficiently retrieve and query picture graphs. The artificial bee colony (ABC) algorithm is inspired by the foraging behaviour of honeybee swarms. ABC is susceptible to laziness in convergence and local optimums, just like other optimization methods. This study created an enhanced ABC (EABC) model to enhance precision. This study presents query-based image tagging model using ensemble learning with EABC (QbITM-ELEABC) for CBIR for appropriately tagging images based on the query image. We examine a number of convolutional neural network (CNNs) with varying topologies that can be trained on the dataset with varying degrees of similarity. As representations, each network extracts class probability vectors from images. The final image representation is created by combining the ensemble's class probability vectors with image.

Improved bat optimization algorithm and enhanced artificial bee colony‐based cluster routing scheme for extending network lifetime in wireless sensor networks

SummaryIn this paper, improved bat and enhanced artificial bee colony optimization algorithm‐based cluster routing (IBEABCCR) scheme is proposed for optimal cluster head (CH) selection with the merits of global diversity and improved convergence rate. It is proposed for achieving optimal CH selection by balancing the tradeoff between the phases of exploration and exploitation. It specifically targeted on the formulation of an ideal CH selection scheme using improved bat optimization algorithm (IBOA) for minimizing the energy depletion rate. It also focuses on the design of an enhanced artificial bee colony (EABC)‐based sink node mobility scheme for determining the optimal points of deployment over which sink nodes can be moved to achieve better delivery of packets from CH to sink node. This CH selection and sink node mobility schemes are contributed for extending the network lifespan using the fitness function, which adopted the factors of node centrality, node degree, distance amid CH and base station (BS), distance among sensor nodes, and residual energy during CH selection process. The simulation experiments were performed using MATLAB version 2018, which confirmed that the number of alive nodes realized in the network is enhanced by 39.21% with the location of BS positioned at (100, 100). The number of rounds (network lifetime) is enhanced by 23.84% with different BS locations in the network. Furthermore, the packets received at the BS are also realized to be enhanced by 26.32% on an average in contrast to the baseline CH schemes used for investigation.

Optimal Trading in Local Electricity Market Based on an Enhanced Evolutionary Algorithm with Distribution Estimation

The evolution from passive to active distribution network, the ability to actively control distributed energy resources and to perform bilateral energy exchange has significantly complicated the market transactions in recent years due to activities among consumers and prosumers. Local electricity market (LEM) is challenged to optimize welfare as well as reduction of emissions by involving participants in energy transactions. In this paper, the trading strategy is modeled as a single-objective optimization problem to determine each agent's optimal energy trading. Modern heuristic optimization techniques have proven their efficiency and robustness in large optimization problems and thus, an enhanced artificial bee colony (EABC) incorporating a distribution estimation algorithm is proposed, which guides the search for optimum by building and sampling explicit probabilistic models of promising candidate solutions. With the help of a distribution estimation algorithm, the EABC eliminates many tuning parameters in the original ABC and increases its exploitation to obtain more robust and competitive performance. A comparative study is conducted with the basic ABC, differential evolution, and particle swarm optimization, and the EABC demonstrates its efficiency on the case study where nine agents are involved in trading energy in the day-ahead LEM.

An artificial bee colony algorithm for scheduling call centres with weekend-off fairness

The workforce scheduling problem in call centres is defined as assigning a number of employees to various overlapping shifts during a specified planning horizon, considering some regulations and preferences, so as to minimize the labour cost. This is often difficult to solve to optimality due to its combinatorial structure. In this paper, we propose an enhanced artificial bee colony (EABC) algorithm to solve the problem. After introducing a good-quality solution generated by a heuristic algorithm to the initial population, the solutions are further exploited by four elaborate neighbourhood structures, which are designed based on the decomposed structure of the problem and deeply embedded in the solution evolution mode. Furthermore, a modified abandoning mode is employed in the onlooker stage. Using real instances from Chengxi Call Centre in Suqian, China, the experimental results show that the proposed algorithm can achieve (sub-)optimal solutions for large-scale problems. Furthermore, a comparative evaluation of EABC is carried out against a hybrid artificial bee colony (HABC) and simulated annealing (SA). The results show that EABC outperforms the other two algorithms for all problem instances. In addition, we analyse the influence of the weekend-off fairness on the labour cost.

Research on a new optimization algorithm simulating multi- states of matter inspired by finite element analysis approach

A new optimization algorithm is proposed, since a huge problem that many algorithms faced was not being able to effectively balance the global and local search ability. Matter exists in three states: solid, liquid, and gas, which presents different motion characteristics. Inspired by multi- states of matter, individuals of optimization algorithm have different motion characteristics of matter, which could present different search ability. The Finite Element Analysis (FEA) approach can simulate multi- states of matter, which can be adopted to effectively balance the global search ability and local search ability in new optimization algorithm. The new algorithm is creative application of Finite Element Analysis at optimization algorithm field. Artificial Physics Optimization (APO) and Gravitational Search Algorithm (GSA) belongs to the algorithm types defined by force and mass. According to FEA approach, node displacement caused by force and stiffness could be equivalent to motion caused by force and mass of APO and GSA. In the new algorithm framework, stiffness replaces mass of APO and GSA algorithm. This paper performs research on two different algorithms based on APO and GSA respectively. The individuals of new optimization algorithm are divided into solid state, liquid state, and gas state. The effects of main parameters on the performance were studied through experiments of 6 static test functions. The performance is compared with PSO, basic APO, or GSA for four complex models which made up of solid individual, liquid individual, and gas individual in iterative process. The reasonable complex model can be confirmed experimentally. Based on the reasonable complex model, the article conducted complete experiments against Enhancing artificial bee colony algorithm with multi-elite guidance (MGABC), Artificial bee colony algorithm with an adaptive greedy position update strategy (AABC), Multi-strategy ensemble artificial bee colony (MEABC), Self-adaptive heterogeneous PSO (fk-PSO), and APO with 28 CEC2013 test problem. Experimental results show that the proposed method achieves a good performance in comparison to its counterparts as a consequence of its better exploration– exploitation balance. The algorithm supplies a new method to improve physics optimization algorithm.

An enhanced artificial bee colony algorithm for the green bike repositioning problem with broken bikes

The Bike Repositioning Problem (BRP) has raised many researchers’ attention in recent years to improve the service quality of Bike Sharing Systems (BSSs). It is mainly about designing the routes and loading instructions for the vehicles to transfer bikes among stations in order to achieve a desirable state. This study tackles a static green BRP that aims to minimize the CO2 emissions of the repositioning vehicle besides achieving the target inventory level at stations as much as possible within the time budget. Two types of bikes are considered, including usable and broken bikes. The Enhanced Artificial Bee Colony (EABC) algorithm is adopted to generate the vehicle route. Two methods, namely heuristic and exact methods, are proposed and incorporated into the EABC algorithm to compute the loading/unloading quantities at each stop. Computational experiments were conducted on the real-world instances having 10–300 stations. The results indicate that the proposed solution methodology that relies on the heuristic loading method can provide optimal solutions for small instances. For large-scale instances, it can produce better feasible solutions than two benchmark methodologies in the literature.

Enhanced Bee Colony Approach for reducing the energy consumption during VM migration in cloud computing environment

To achieve virtualization in a cloud environment, resource utilization and energy need to be handled carefully. For this one should have to manage the workload, by distributing the load equally among the node. So that, the resources should be distributed equally among the cloud user and access data anytime from anywhere with minimum energy. In this paper, an enhanced Artificial Bee Colony (E-ABC) approach is presented to minimize overall energy consumption with minimum number of migrations. E-ABC approach migrates the VM from the overloaded host to underloaded hosts and hence save energy. The enhancement of the proposed work is exhibited by showing comparison with the Enhanced Cuckoo Search (E-CS) approach and Ant Colony Optimization technique using MATLAB simulator. Enhancement in the reduction of energy consumption of about 15.45 %, and 17.03 % is observed against E-CS, and existing work.

Enhancing artificial bee colony algorithm with multi-elite guidance

Artificial bee colony (ABC) algorithm is a relatively new paradigm of swarm intelligence based optimization technique, which has attracted a lot of attention for its simple structure and good performance. For some complex optimization problems, however, the performance of ABC is challenged due to its solution search equation that has strong explorative ability but poor exploitative ability. To solve this defect, in this work, we propose an improved ABC algorithm by using multi-elite guidance, which has the benefits of utilizing valuable information from elite individuals to guide search while without losing population diversity. First, we construct an elite group by selecting some elite individuals, and then introduce two improved solution search equations into the employed bee phase and onlooker bee phase based on the elite group, respectively. Last, we develop a modified neighborhood search operator by utilizing the elite group as well, which aims to achieve a better tradeoff between explorative and exploitative abilities. To verify our approach, 50 well-known test functions and one real-world optimization problem are used in the experiments, including 22 scalable basic test functions and 28 complex CEC2013 test functions. Seven different well-established ABC variants are involved in the comparison and the results show that our approach can achieve better or at least comparable performance on most of the test functions.

Enhancing artificial bee colony algorithm using refraction principle

The artificial bee colony algorithm (ABC), as one of the excellent intelligent optimization technologies, has presented very good optimization performance for many complex problems due to its simplicity and easiness of implementation. However, ABC has a very good performance at exploration relatively, but for some complex problems it still results in slower convergent speed and lower convergent accuracy in the later stage of algorithms. Meanwhile, ABC has relatively poor performance at exploitation. To overcome these drawbacks further, the enhancing ABC algorithm using refraction principle is proposed (EABC-RP) in this paper. In EABC-RP, on the one hand, in order to enhance its exploration further, the unified opposition-based learning (UOBL) based on refraction principle is employed to generate refraction solutions (new food sources) for employed bees, which helps to increase population diversity and guide search direction close to the global optimal solution. On the other hand, for exploitation, when ABC has fallen into the local optimal solution, the UOBL based on refraction principle is employed for mutation to increase the probability of jumping out of the local optimal solution for scout bees. A lot of experiments are conducted on 23 benchmark functions to verify the effectiveness of EABC-RP. The experimental results show that EABC-RP achieves higher solution accuracy and faster convergent speed in most cases and outperforms other ABC variants. In addition, EABC-RP is used to optimize finite impulse response (FIR) low-pass digital filter which obtains the better filtering performance, which validates the effectiveness of the EABC-RP algorithm further.

Parameter Space Exploration for Analog Circuit Design Using Enhanced Bee Colony Algorithm

In this work, novel swarm optimization algorithm based on the Artificial Bee Colony (ABC) algorithm called Enhanced Artificial Bee Colony (EABC) algorithm is proposed for the design and optimization of the analog CMOS circuits. The new search strategies adopted improve overall performance of the proposed algorithm. The performance of EABC algorithm is compared with other competitive algorithms such as ABC, GABC (G-best Artificial Bee Colony Algorithm) and MABC (Modified Artificial Bee Colony Algorithm) by designing three CMOS circuits; Two-stage operational amplifier, low-voltage bulk driven OTA and second generation low-voltage current conveyor in 0.13 [Formula: see text]m and 0.09[Formula: see text][Formula: see text]m CMOS technologies. The obtained results clearly indicate that the performance of EABC algorithm is better than other mentioned algorithms and it can be an effective approach for the automatic design of the analog CMOS circuits.

REAL POWER LOSS REDUCTION ENHANCED ARTIFICIAL BEE COLONY ALGORITHM

In this paper, Enhanced Artificial Bee Colony (EABC) algorithm is proposed for solving optimal reactive power problem. The projected method assimilates crossover operation from Genetic Algorithm (GA) with artificial bee colony (ABC) algorithm. The EABC strengthens the exploitation phase of ABC as crossover enhances exploration of search space. Projected EABC algorithm has been tested on has been tested on standard IEEE 118 & practical 191 bus test systems and simulation results show clearly about the premium performance of the proposed algorithm in reducing the real power loss.

Enhanced Artificial Bee Colony Algorithm for Liver Cancer Analysis

Abstract During the development of computer technology, computer-aided diagnosis (CAD) technology, used in quantitative analysis of medical imaging, arose at a historic moment and became a research hotspot in medical imaging. Discrimination of hepatocellular carcinoma (HCC) in the liver is a challenge in the histopathologic diagnostics. For this reason, there is an urgent need for new detection methods to improve the accuracy of the diagnosis of liver cancer. Traditional machine-learning approaches are neural network (NN)-based. Cost-sensitive learning and a support vector machine (SVM) is observed to provide a good result in the case of balanced data sets; however, it is not capable of dealing with the classification of imbalanced data sets. These machine-learning approaches may be biased toward the majority class, thus producing a poor predictive accuracy over the minority class. In this paper, a novel technique for the purpose of liver cancer cell classification and root liver cancer cell recognition is proposed. The objective is to automatically categorize several classes of liver cancer cells and to discover the root cancer cell. To solve this problem, initially, preprocessing on noisy imbalanced data sets is carried out by means of improved weighted synthetic minority oversampling technique (IWSMOTE)-based oversampling and evolutionary undersampling. An ensemble-based learning algorithm (DataBoost.IM) with SVM is employed for final classification to classify the cancer cells and non-cancer cells. Finally, the enhanced artificial bee colony (EABC) clustering is applied to discover the root cancer cell. The proposed EABC clustering approach is tested using the liver cancer cell data set, providing an accuracy level of 96.15 %, which is 95.61 % and 92.80 % higher than the ant colony optimization (ACO) and artificial bee colony (ABC) algorithms, respectively.

Dynamic green bike repositioning problem – A hybrid rolling horizon artificial bee colony algorithm approach

This paper introduces a new dynamic green bike repositioning problem (DGBRP) that simultaneously minimizes the total unmet demand of the bike-sharing system and the fuel and CO2 emission cost of the repositioning vehicle over an operational period. The problem determines the route and the number of bikes loaded and unloaded at each visited node over a multi-period operational horizon during which the cycling demand at each node varies from time to time. To handle the dynamic nature of the problem, this study adopts a rolling horizon approach to break down the proposed problem into a set of stages, in which a static bike repositioning sub-problem is solved in each stage. An enhanced artificial bee colony (EABC) algorithm and a route truncation heuristic are jointly used to optimize the route design in each stage, and the loading and unloading heuristic is used to tackle the loading and unloading sub-problem along the route in a given stage. Numerical results show that the EABC algorithm outperforms Genetic Algorithm in solving the routing sub-problem. Computation experiments are performed to illustrate the effect of the stage duration on the two objective values, and the results show that longer stage duration leads to higher total unmet demand and total fuel and CO2 emission cost. Numerical studies are also performed to illustrate the effects of the weight and the loading and unloading times on the two objective values and the tradeoff between the two objectives.

Wind Energy Power System Stabilizer Design using H∞ Robust Technique based on Enhance ABC Optimal Power System

system stabilizers (PSS) are now regularly used in the industry to damp out power system oscillations. High performance excitation system have become very important as limited generation capacity and consumer desires for power continue to increase. In this work, enhanced Artificial Bee Colony (ABC) technique is applied to design a robust power system stabilizer (PSS) in order to improve transient and dynamic stabilities of a turbo-alternator connected to an infinite bus system. The design problem of the proposed controller is formulated as an optimization problem and enhanced ABC is employed to search for optimal controller parameters. The robust power system stabilizer (RPSS) is designed using enhanced ABC for designing the controllers for dynamical systems in electrical engineering. Comparisons are also made between the Conventional power system stabilizer with a strong action (CPSS) and PSS with H∞ optimization. The simulation results show the effectiveness of proposed method for a stabilizer by enhancing the performance and robustness.

ENHANCED BIO-INSPIRED ALGORITHM FOR CONSTRUCTING PHYLOGENETIC TREE

This paper illustrates an enhanced algorithm based on one of the swarm intelligence techniques for constructing the Phylogenetic tree (PT), which is used to study the relationship between species. The main scheme is to formulate a PT, an NP- complete problem through an evolutionary algorithm called Artificial Bee Colony (ABC). The tradeoff between the accuracy and the computational time taken for constructing the tree makes way for new variants of algorithms. A new variant of ABC algorithm is proposed to promote the convergence rate of general ABC algorithm through recommending a new formula for searching solution. In addition, a searching step has been included so that it constructs the tree faster with a nearly optimal solution. Experimental results are compared with the ABC algorithm, Genetic Algorithm and the state-of-the-art techniques like unweighted pair group method using arithmetic mean, Neighbour-joining and Relaxed Neighbor Joining. For results discussion, we used one of the standard dataset Treesilla. The results show that the Enhanced ABC (EABC) algorithm converges faster than others. The claim is supported by a distance metric called the Robinson-Foulds distance that finds the dissimilarity of the PT, constructed by different algorithms.

Security constrained multi-period optimal power flow by a new enhanced artificial bee colony

Security constrained optimal power flow (SCOPF) is an important operation function for dispatching centers of current power systems. It optimizes operating conditions of the system, while saves its security. However, SCOPF in its present form focuses on a single time interval, which is known as static SCOPF. A multi-period SCOPF model, referred to as dynamic SCOPF (DSCOPF) in this paper, is presented. It extends static SCOPF to multi-period frameworks considering the inter-temporal constraints. The proposed DSCOPF is a more practical operation function and can optimize operating conditions of the system more realistically compared to traditional SCOPF. Moreover, to solve this DSCOPF model, considering its nonlinear and non-convex behavior, a new stochastic search method is presented, which is an enhanced version of artificial bee colony (ABC) algorithm. The proposed enhanced ABC (EABC) has high exploration capability and can discover different areas of the solution space. Also, it is a robust algorithm and has low sensitivity with respect to the initial population. Effectiveness of the proposed EABC is extensively illustrated on various test cases.

Enhanced ABC Based PID Controller for Nonlinear Control Systems

A Nonlinear PID (NPID) controller tuning based on the Enhanced Artificial Bee Colony (E-ABC) algorithm is presented. The ABC algorithm uses the foraging behavior of honey bee swarm to find the optimal PID parameters kp, ki and kd. In this proposed E-ABC, the Particle Swarm Optimization (PSO) swarm intelligence behavior is inherited to ABC scout bee to get proper selection of food source. The convergence characteristics of the E-ABC based optimization shows that the proposed method provides better controller settings with minimum iteration. To show the effectiveness of the proposed method, it is presented to the nonlinear Continuous Stirred Tank Reactor (CSTR) process and the results are compared with the conventional Internal Model Control (IMC) tuning method, and heuristic approaches viz., Genetic Algorithm (GA), Simulated Annealing (SA), PSO and other hybrid methods based PID performances. From the results of integral performance criterions viz., ISE, IAE and ITAE, it is evident that the proposed E-ABC provides better tracking and improves closed loop accuracy.