Search

Spectrum map (SM) is an important tool to reflect the spectrum usage in the electromagnetic environment. To address the problems of low precision and poor efficiency in the SM construction, this paper develops a novel SM construction approach based on the artificial bee colony enabled sensor layout optimization and an adaptive Kriging model based on spatial autocorrelation. Considering the significant autocorrelation between sensor attributes caused by the exponentially decaying shadow fading of signal propagation, the sensor estimation groups are established, and the estimation results are obtained by the Kriging model. The simulation results show that the proposed SM construction scheme can not only effectively reduce the overhead of sensor resources but also obtain a high SM construction accuracy. Extensive simulation results show that the proposed method can reduce the RMSE of SM construction by 37.56%, 25.32% and 12.89% respectively compared with Random-OK when the standard deviation of shadow fading is 1 dB, 3 dB and 6 dB.

The process of selecting software requirements aims to identify the optimal set of requirements that enhances the value of a software release while keeping costs within the budget. It is referred to as the next release problem (NRP) and is classified as a non-deterministic polynomial (NP) hard problem. Additionally, the addressed requirements are complicated by interconnections and other constraints. In the current paper, the NRP is defined as a multi-objective optimization problem with two conflicting objectives, the satisfaction of customers and cost of development, and three constraints to address two real-world instances of the NRP. A hybrid algorithm combining the multi-objective artificial bee colony and differential evolution named (HABC-DE) is proposed in this work. The proposed approach involves management from the original artificial bee colony (ABC) with operators of the differential evolution (DE) algorithm to balance the optimization process's exploitation and exploration stages. The results demonstrated that the suggested algorithm was capable of efficiently generating high-quality non-dominated solutions with 163.48 ± 4.9295 for mean and standard deviation values which can help decision-makers choose the right set of requirements for a new software release production.

One of the most studied variant of portfolio optimization problems is with cardinality constraints that transform classical mean–variance model from a convex quadratic programming problem into a mixed integer quadratic programming problem which brings the problem to the class of NP-Complete problems. Therefore, the computational complexity is significantly increased since cardinality constraints have a direct influence on the portfolio size. In order to overcome arising computational difficulties, for solving this problem, researchers have focused on investigating efficient solution algorithms such as metaheuristic algorithms since exact techniques may be inadequate to find an optimal solution in a reasonable time and are computationally ineffective when applied to large-scale problems. In this paper, our purpose is to present an efficient solution approach based on an artificial bee colony algorithm with feasibility enforcement and infeasibility toleration procedures for solving cardinality constrained portfolio optimization problem. Computational results confirm the effectiveness of the solution methodology. [ABSTRACT FROM AUTHOR]

One of the most important issues of studies that have been done on the power systems is loss reduction. Flexible A. C. transmission systems (FACTS) give significant opportunities to realize this aim. Static Synchronous Compensators (STATCOMs) are the most flexible and sophisticated structure as compared to the other FACTS devices in terms of operation. Total losses of a power system change according to the location and reactive power output of STATCOM. In this work, it is aimed to find optimum location and output value of a STATCOM that provide minimum losses of power system by using four different heuristic algorithms and to compare these algorithms. Heuristic algorithms that are used in this paper are Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Ant Colony Algorithm (ACA) respectively. These methods were applied to IEEE-14 bus test system, and optimum locations and output values of STATCOMs that provide minimum losses have been found. Also, results of all methods are compared and discussed in terms of finding proper value and convergence speed. [ABSTRACT FROM AUTHOR]

Disassembly sequencing has significant effects on the performance of remanufacturing and recycling of used or discarded products. Studies on disassembly sequence optimization have largely focused on sequential disassembly. However, for large or complex products sequential disassembly takes long time to complete and is rather inefficient since it removes only one part or subassembly at a time with only one operator assigned to disassemble a product. This work studies selective cooperative disassembly sequence planning (SCDSP) problem which is essential to disassemble large or complex products in an efficient way. Similar to sequential disassembly planning, SCDSP aims at finding the optimal disassembly task sequence, but is more complicated. SCDSP is a nonlinear NP-complete combinatorial optimization problem, and evolutionary algorithms can be adopted to solve it. In this paper exclusive and cooperative relationships are introduced as additional constraints besides the common precedence relationship. A novel procedure to generate feasible cooperative disassembly sequences (GFCDS) is proposed. A mathematical programming model of SCDSP is developed based on the parallel disassembly characteristics with two optimization objectives i.e. disassembly time and profit, considered. A multi-objective evolutionary algorithm (MOEA), i.e., multi-objective discrete artificial bee colony optimization (MODABC), is adopted to solve the problem to create the Pareto frontier. This approach is applied to real-world disassembly processes of two products (a small product and a medium/large one) to verify its feasibility and effectiveness. Also, the proposed method is compared with the well-known NSGA-II. For our comparative study, the nondominated solutions of the two MOEAs are compared in both cases, and two quantitative metrics, i.e., inverted generational distance ( IGD ) and spacing ( SP ), are adopted to further measure the algorithm performance. Results indicate that the set of nondominated solutions from MODABC are better for each instance tested, and the Pareto front is overall superior to that from NSGA-II. For both cases, IGD and SP are decreased by up to 81.5% and 62.2%, respectively. [ABSTRACT FROM AUTHOR]

Imbalanced data appear in many real-world applications, from biomedical application to network intrusion or fraud detection, etc. Existing methods for Parkinson’s disease (PD) diagnosis are usually more concerned with overall accuracy (ACC), but ignore the classification performance of the minority class. To alleviate the bias against performance caused by imbalanced data, in this paper, an effective method named AABC-KWELM has been proposed for PD detection. First, based on a fast classifier extreme learning machine (ELM), weighted strategy is used for dealing with imbalanced data and non-linear mapping of kernel function is used for improving the extent of linear separation. Furthermore, both binary version and continuous version of an adaptive artificial bee colony (AABC) algorithm are used for performing feature selection and parameters optimization, respectively. Finally, PD data set is used for evaluating rigorously the effectiveness of the proposed method in accordance with specificity, sensitivity, ACC, G-mean and F-measure. Experimental results demonstrate that the proposed AABC-KWELM remarkably outperforms other approaches in the literature and obtains better classification performance via 5-fold cross-validation (CV), with specificity of 100%, sensitivity of 98.62%, ACC of 98.97%, G-mean of 99.30%, and F-measure of 99.30%. [ABSTRACT FROM AUTHOR]

Artificial Bee Colony (ABC) and Differential Evolution (DE) are two very popular and efficient meta-heuristic algorithms. However, both algorithms have been applied to various science and engineering optimization problems, extensively, the algorithms suffer from premature convergence, unbalanced exploration-exploitation, and sometimes slow convergence speed. Hybridization of ABC and DE may provide a platform for developing a meta-heuristic algorithm with better convergence speed and a better balance between exploration and exploitation capabilities. This paper proposes a hybridization of ABC and DE algorithms to develop a more efficient meta-heuristic algorithm than ABC and DE. In the proposed hybrid algorithm, Hybrid Artificial Bee Colony with Differential Evolution (HABCDE), the onlooker bee phase of ABC is inspired from DE. Employed bee phase is modified by employing the concept of the best individual while scout bee phase has also been modified for higher exploration. The proposed HABCDE has been tested over 20 test problems and 4 real-world optimization problems. The performance of HABCDE is compared with the basic version of ABC and DE. The results are also compared with state-of-the-art algorithms, namely Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Particle Swarm Optimization (PSO), Biogeography Based Optimization (BBO) and Spider Monkey Optimization (SMO) to establish the superiority of the proposed algorithm. For further validation of the proposed hybridization, the experimental results are also compared with other hybrid versions of ABC and DE, namely ABC-DE, DE-BCO and HDABCA and with modified ABC algorithms, namely Best-So-Far ABC (BSFABC), Gbest guided ABC (GABC) and modified ABC (MABC). Results indicate that HABCDE would be a competitive algorithm in the field of meta-heuristics. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) is widely used to minimize total power losses in distribution networks. However, one of the problems of DG in a grid system is reverse current ow (RCF), which is when the DG output becomes greater than the connected load. Therefore, this paper proposes a multiobjective artificial bee colony (MOABC) algorithm to determine the optimal network reconfiguration for reducing total RCF in DG. The proposed algorithm is tested on 33-bus radial distribution systems in two different scenarios, i.e. base case and with 50% load. The proposed technique can reduce reverse current by up to 93%; however, the total power loss in the system will increase by 7%. Therefore, a suitable weight value is needed in MOABC for balancing the effect of RCF and the power loss value. [ABSTRACT FROM AUTHOR]

In this paper, the multaiband cooperative spectrum sensing problem in cognitive radio network is investigated, which focuses on maximizing the total throughput under the constraint of the interference to primary users by jointly optimizing weight coefficients and decision threshold. Due to the non-convex characteristics of this optimization problem, a modified artificial bee colony (MABC) algorithm is proposed to deal with this problem, in which some improved mechanisms, such as mutation and crossover factors are introduced in ABC to enhance the diversity and improve the searching ability. Classical benchmark functions are employed to evaluate the searching ability of MABC when compared with PSO, GA, ABC and EA-ABC. Simulation results have been provided to validate the promising performance of MABC over the other intelligent evolutionary algorithms when applied to both the classical benchmark functions and multiband cooperative spectrum sensing scenario. [ABSTRACT FROM AUTHOR]

The search for new energy resources is a crucial task nowadays. Research on the use of solar energy is growing every year. The aim is the design of devices that can produce a considerable amount of energy using the Sun's radiation. The modeling of solar cells (SCs) is based on the estimation of the intrinsic parameters of electrical circuits that simulate their behavior based on the current vs. voltage characteristics. The problem of SC design is defined by highly nonlinear and multimodal objective functions. Most of the algorithms proposed to find the best solutions become trapped into local solutions. This paper introduces the Chaotic Improved Artificial Bee Colony (CIABC) algorithm for the estimation of SC parameters. It combines the use of chaotic maps instead random variables with the search capabilities of the Artificial Bee Colony approach. CIABC has also been modified to avoid the generation of new random solutions, preserving the information of previous iterations. In comparison with similar optimization methods, CIABC is able to find the global solution of complex and multimodal objective functions. Experimental results and comparisons prove that the proposed technique can design SCs, even with the presence of noise. [ABSTRACT FROM AUTHOR]

In order to obtain better generalization abilities and mitigate the impacts of the best and worst individuals during the process of optimization, this paper suggests Bee and Frog Co-Evolution Algorithm (abbreviation for BFCEA), which combines Mnemonic Shuffled Frog Leaping algorithm With Cooperation and Mutation (abbreviation for MSFLACM) with improved Artificial Bee Colony (abbreviation for ABC). The contrast experimental study about different iteratively updating strategies was acted in BFCEA, including strategy of integrating with ABC, regeneration of the worst frog and its leaping step. The key techniques focus on the first 10 and the last 10 frogs evolving ABC in BFCEA, namely, the synchronous renewal strategy for those winner and loser should be applied, after certain G times’ MSFLACM-running, so as to avoid trapping local optimum in later stage. The ABC evolution process will be called between all memes’ completing inner iteration and all frogs’ outer shuffling, the crossover operation is removed from MSFLACM for its little effect on time-consuming and convergence in this novel algorithm. Besides, in ABC, the scout bee is generated by Cauchy mutating instead at random. The performance of proposed approach is examined by well-known 16 numerical benchmark functions, and obtained results are compared with basic Shuffled Frog Leaping algorithm (abbreviation for SFLA), ABC and four other variants. The experimental results and related application in cloud resource scheduling show that the proposed algorithm is effective and outperforms other variants, in terms of solution quality and convergence, and the improved variants can obtain a lower degree of unbalanced load and relatively stable scheduling strategy of resources in complicated cloud computing environment. [ABSTRACT FROM AUTHOR]

Symbolic circuit analysis inherits the exponential growth of transfer function complexity with the circuit size. Therefore, symbolic simplification is an NP-hard problem. Although many simplification techniques have been presented, the simplified transfer functions are not written in a factorized form, and consequently, it is difficult to assess the contribution of poles and zeros on the circuit behavior. In this paper, a swarm intelligence based methodology is presented for the simplified factorized symbolic analysis of analog circuits. In this method, an extension of the root splitting technique is utilized to rewrite the expanded transfer function of the circuit into a factorized form comprising DC-gain, poles, and zeros. Then, the derived factorized transfer function is simplified using a hybrid Global and Local search algorithm based on Artificial Bee Colony and Simulated Annealing (named GLABCSA). The objective function is defined to minimize the complexity of the symbolic factorized transfer function while minimizing the DC-gain error and pole/zero displacements. The presented approach has been successfully developed in MATLAB. The program can derive the simplified factorized symbolic transfer function automatically from the input text netlist of the circuit. Symbolic and numerical results over two analog amplifiers are given to illustrate the efficiency of the presented methodology. [ABSTRACT FROM AUTHOR]

In this paper, a new hybrid optimization algorithm which combines the standard Butterfly Optimization Algorithm (BOA) with Artificial Bee Colony (ABC) algorithm is proposed. The proposed algorithm used the advantages of both the algorithms in order to balance the trade-off between exploration and exploitation. Experiments have been conducted on the proposed algorithm using ten benchmark problems having a broad range of dimensions and diverse complexities. The simulation results demonstrate that the convergence speed and accuracy of the proposed algorithm in finding optimal solutions is significantly better than BOA and ABC. [ABSTRACT FROM AUTHOR]

Achieving robustness with a limited distortion level is a challenging design problem for watermarking systems in multimedia applications with a guaranteed quality requirement. In this paper, we provide an intelligent system for watermarking through incorporating a meta-heuristic technique along with an embedding method to achieve an optimized performance. The optimization objective is to provide the maximum possible robustness without exceeding a predetermined distortion limit. Hence, the quality level of the watermarking method could be guaranteed through that constraint optimization. A new fitness function is defined to provide the required convergence toward the optimum solution for the defined optimization problem. The fitness function is based on dividing its applied solution population into two groups, where each group is ranked according to a different objective. Thus, the multi-objectives in the problem are decoupled and solved through two single-objective sub-problems. Unlike existing watermarking optimization techniques, the proposed work does not require weighting factors. To illustrate the effectiveness of the proposed approach, we employ a recent watermarking technique, and then use it as the embedding method to be optimized. The Artificial Bee Colony is selected as the meta-heuristic optimization method in which the proposed fitness function is used. Experimental results show that the imposed quality constraint is satisfied, and that the proposed method provides enhanced robustness under different attacks for various quality thresholds. The presented approach offers a robust solution that can be applied to numerous multimedia applications such as film industry, intelligent surveillance and security systems. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANETs) have drawn great attention in wireless communications. Prompt and reliable vehicular communication is a must to provide a good service. Routing is the key problem in information transmission of VANETs. This paper studies quality of service (QoS) constrained multicast routing problem. This problem has been proved to be NP-complete problem, and swarm intelligence algorithms are more suitable than classical algorithms. A micro artificial bee colony (MABC) algorithm is proposed to deal with the problem. The QoS constraints include maximize network lifetime and minimizing delay cost. Multicast routing is abstracted to a continuous optimization problem. Then, it is linked with MABC. Numerical simulation is implemented on a traffic scenario with three instances. Results show that the MABC algorithm successfully attains the optimal routes. Moreover, the routing framework can be applied in real time given the network structure does not change too frequently. [ABSTRACT FROM AUTHOR]

To accomplish reliable and efficient information routing, strong paths connecting all nodes are required in vehicular ad hoc networks (VANETs). Classical algorithms in graphic theory could find only one minimum spanning tree (MST) in VANETs. Swarm intelligence paradigms are able to obtain several alternatives to MST, which is useful for improving reliability of VANETs. This paper proposes a binary coded artificial bee colony (BABC) algorithm for tackling the spanning tree construction problem. A two-element variation technique is designed to keep the consistence of binary coded solutions. The proposed algorithm is applied to tackle a roadside-to-vehicle communication example. The success rate and average hitting time of the algorithm to find MST are also analyzed. It is found that the BABC algorithm could find MST with 92% probability. Though it is slower than Kruskal algorithm in terms of computational time, the BABC algorithm can attain several suboptimal spanning trees in one run. This suggests that the algorithm would be useful under the condition that tree paths are required to be rebuilt frequently while the network topology is unchanged in a short period. [ABSTRACT FROM AUTHOR]

There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC) for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell’s pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The brain signals usually generate certain electrical signals that can be recorded and analyzed for detection in several brain disorder diseases. These small signals are expressly called as Electroencephalogram (EEG) signals. This research work analyzes the epileptic disorder in human brain through EEG signal analysis by integrating the best attributes of Artificial Bee Colony (ABC) and radial basis function networks (RBFNNs). We have used Discrete Wavelet Transform (DWT) technique for extraction of potential features from the signal. In our study, for classification of these signals, in this paper, the RBFNNs have been trained by a modified version of ABC algorithm. In the modified ABC, the onlooker bees are selected based on binary tournament unlike roulette wheel selection of ABC. Additionally, kernels such as Gaussian, Multi-quadric, and Inverse-multi-quadric are used for measuring the effectiveness of the method in numerous mixtures of healthy segments, seizure-free segments, and seizure segments. Our experimental outcomes confirm that RBFNN with inverse-multi-quadric kernel trained with modified ABC is significantly better than RBFNNs with other kernels trained by ABC and modified ABC. [ABSTRACT FROM AUTHOR]

Magnetization curves are obtained with measurements and used for the description of magnetic material properties. In the case where the curve is rough problems can appear during the Finite Element Method (FEM) calculations. One of the solutions is the use of an analytically written curve, which fits the measured curve. In this paper different analytical expressions are tested on different measured magnetization curves and compared with each other. Different evolutionary methods are used and tested for the determination of the analytical expressions’ parameters: The Genetic Algorithm, Differential Evolution with three different strategies, Teaching-Learning Based Optimization and Artificial Bee Colony. To obtain credible and optimal results, we made a statistic evaluation of the results using Cross-validation, CRS4EAs (Chess rating system for evolutionary algorithms), and the Holm test. Based on the test’s results we improved the more appropriate evolutionary method, which was Artificial Bee Colony, using the Levenberg-Marquardt algorithm. As a result, two different methods: are presented and tested which combine Artificial Bee Colony and the Levenberg-Marquardt algorithm. An analytical expression is presented which can be used for a wide range of different materials’ curves and also a stable and efficient method for the determination of the analytical expression’s parameters. The presented solution is appropriate to be used together with, or as a part of, FEM calculation software. For preparation of magnetic material data the presented solution can be used as an independent programme for the transformation of the H - B table of values presenting not-smooth measured magnetic material curves (or measured with too few points) into the H - B table of values presenting smooth magnetic material curve which can be used as input data for any FEM software. [ABSTRACT FROM AUTHOR]

Artificial bee colony (ABC) is a novel swarm intelligence optimization algorithm that has been shown to be effective in solving high dimensional global optimization problem with good performance for its excellent exploration capability. It has received a great deal of attentions of researchers since it was proposed, and was employed to many application fields for its advantages of excellent global optimization ability and easy to implement. However, the basic ABC has some drawbacks like poor exploitation and slow convergence. In this paper, an improved artificial bee colony algorithm based on modified-neighborhood-based update operator and independent-inheriting-search strategy for global optimization called MNIIABC algorithm is proposed. In the proposed algorithm, a modified-neighborhood-based update operator, which contains a global-best term and a subset-best guided term, is applied in the employed bee stage to balance the exploration and exploitation. Aiming to improve the solution diversity, a subset partition method for producing perturbation term is considered. In order to enhance the exploitation of the algorithm, an independent-inheriting-search strategy is used in the onlooker stage. Experiment results tested on multiple benchmark functions show that the proposed method is effective, and has good performance. The comparison experimental results illustrate that the proposed algorithm has good solution quality and convergence characteristics. [ABSTRACT FROM AUTHOR]

Bayesian networks (BNs) are one of the most widely used class for machine learning and decision making tasks especially in uncertain domains. However, learning BN structure from data is a typical NP-hard problem. In this paper, we present a novel hybrid algorithm for BN structure learning, called MMABC. It's based on a recently introduced meta-heuristic, which has been successfully applied to solve a variety of optimization problems: Artificial Bee Colony (ABC). MMABC algorithm consists of three phases: (i) obtain an initial undirected graph by the subroutine MMPC. (ii) Generate the initial population of solutions based on the undirected graph and (iii) perform the ABC algorithm to orient the edges. We describe all the elements necessary to tackle our learning problem, and experimentally compare the performance of our algorithm with two state-of-the-art algorithms reported in the literature. Computational results demonstrate that our algorithm achieves better performance than other two related algorithms. [ABSTRACT FROM AUTHOR]

Cost of testing activities is a major portion of the total cost of a software. In testing, generating test data is very important because the efficiency of testing is highly dependent on the data used in this phase. In search-based software testing, soft computing algorithms explore test data in order to maximize a coverage metric which can be considered as an optimization problem. In this paper, we employed some meta-heuristics (Artificial Bee Colony, Particle Swarm Optimization, Differential Evolution and Firefly Algorithms) and Random Search algorithm to solve this optimization problem. First, the dependency of the algorithms on the values of the control parameters was analyzed and suitable values for the control parameters were recommended. Algorithms were compared based on various fitness functions (path-based, dissimilarity-based and approximation level + branch distance) because the fitness function affects the behaviour of the algorithms in the search space. Results showed that meta-heuristics can be effectively used for hard problems and when the search space is large. Besides, approximation level + branch distance based fitness function is generally a good fitness function that guides the algorithms accurately. [ABSTRACT FROM AUTHOR]

This article addresses the problem of converting a single-objective combinatorial problem into a multi-objective one using the Pareto front approach. Although existing algorithms can identify the optimal solution in a multi-objective space, they fail to satisfy constraints while achieving optimal performance. To address this issue, we propose a multi-objective artificial bee colony optimization algorithm with a classical multi-objective theme called fitness sharing. This approach helps the convergence of the Pareto solution set towards a single optimal solution that satisfies multiple objectives. This article introduces multi-objective optimization with an example of a non-dominated sequencing technique and fitness sharing approach. The experimentation is carried out in MATLAB 2018a. In addition, we applied the proposed algorithm to two different real-time datasets, namely the knapsack problem and the nurse scheduling problem (NSP). The outcome of the proposed MBABC-NM algorithm is evaluated using standard performance indicators such as average distance, number of reference solutions (NRS), overall count of attained solutions (TNS), and overall non-dominated generation volume (ONGV). The results show that it outperforms other algorithms. [ABSTRACT FROM AUTHOR]

The demand for the accurate and timely identification of melanoma as a major skin cancer type is increasing daily. Due to the advent of modern tools and computer vision techniques, it has become easier to perform analysis. Skin cancer classification and segmentation techniques require clear lesions segregated from the background for efficient results. Many studies resolve the matter partly. However, there exists plenty of room for new research in this field. Recently, many algorithms have been presented to preprocess skin lesions, aiding the segmentation algorithms to generate efficient outcomes. Nature-inspired algorithms and metaheuristics help to estimate the optimal parameter set in the search space. This research article proposes a hybrid metaheuristic preprocessor, BA-ABC, to improve the quality of images by enhancing their contrast and preserving the brightness. The statistical transformation function, which helps to improve the contrast, is based on a parameter set estimated through the proposed hybrid metaheuristic model for every image in the dataset. For experimentation purposes, we have utilised three publicly available datasets, ISIC-2016, 2017 and 2018. The efficacy of the presented model is validated through some state-of-the-art segmentation algorithms. The visual outcomes of the boundary estimation algorithms and performance matrix validate that the proposed model performs well. The proposed model improves the dice coefficient to 94.6% in the results. [ABSTRACT FROM AUTHOR]

Cities based on oil and gas energy resources are crucial to energy production and economic development, but they also face various disasters and security risks. To ensure the safety and well-being of urban residents during disaster events, the planning of urban shelters is crucial. In this paper, comprehensively considering multiple factors such as disaster risk, population distribution, and convenient transportation, the artificial bee colony algorithm is used to optimize the site selection and capacity planning of shelters. By comprehensively evaluating the disaster resistance capacity of urban refuges, the response speed of residents and other related indicators, the planning algorithm of refuges is continuously optimized to better meet the needs of oil and gas energy resource-based cities. The results of the study showed that the average overall disaster resilience of AI-based urban shelters reached 0.64. When the distance to the shelter was 4 km, the average response speed of residents reached 10.22 min, and other indicators also improved. The research shows that the oil and gas energy urban refuge planning algorithm based on the artificial intelligence elastic city model provides an innovative approach for urban planners and disaster managers. Further research and practice will help promote the application of this algorithm in real cities, improving the resilience and disaster resistance of cities and the safety and security level of residents.

The patient admission scheduling (PAS) problem is a well-known combinatorial optimization problem that entails the allocation of patients to some restricted bedspace for a certain period while adhering to a set of established constraints such as medical preferences and prerequisites. The existing algorithmic techniques utilized in solving the formulation of the PAS could not efficiently navigate the deep solution space due to the highly constrained nature of the formulation. Therefore, to address the drawbacks exhibited by the existing search methodologies when applied to the PAS problem, this paper proposed a solution template that hybridized the artificial bee colony (ABC) with hill climbing heuristic (HCH) named the Hill-ABC algorithm. Hill climbing is integrated within the operator of employed bees of the ABC algorithm to exploit the PAS’s rugged solution space and strike the right balance between diversification and intensification during search activities. The design of the proposed Hill-ABC approach is in two phases: The first phase is aimed at generating initial feasible solutions using a room selection-based method (RSM). Similarly, the second phase employed three neighbourhood structures to improve the quality of previously generated initial solutions. The proposed hybrid Hill-ABC algorithm is validated using the PAS standard datasets. The experiment analysis shows that the proposed method achieved better-quality solutions than most of the existing state-of-the-art algorithms.

The usage of renewable energy sources as an alternative energy source is faced with a major challenge introduced by the intermittency nature of the source. We have two options i) adapt the demand to the source, or ii) adapt the source to the demand. If the process demand is flexible and can be adapted to the energy source, it will enjoy cheap energy. Alternatively, non-flexible processes will require extra costs to be paid to adapt the source to the process energy requirements. This paper presents a demand response model to adapt the demand of industrial processes to the available energy source. The model is solved analytically using mixed integer linear programming. Then, three metaheuristic search algorithms were used. The results of the solving algorithms are compared with each other using the Wilcoxons' rank-sum test. Then the model is tested for two examples of flexible and rigid processes showing the differences.

The integro-differential equation with the Cauchy kernel is used in many different technical problems, such as in circuit analysis or gas infrared radiation studies. Therefore, it is important to be able to solve this type of equation, even in an approximate way. This article compares two approaches for solving this type of equation. One of the considered methods is based on the application of the differential Taylor series, while the second approach uses selected heuristic algorithms inspired by the behavior of animals. Due to the problem domain, which is symmetric, and taking into account the form of the function appearing in this equation, we can use this symmetry in some cases. The paper also presents numerical examples illustrating how each method works and comparing the discussed approaches.

Hyperspectral remote sensing images have characteristics such as high dimensionality and high redundancy. This paper proposes a pseudo-label guided artificial bee colony band selection algorithm with hypergraph clustering (HC-ABC) to remove redundant and noise bands. Firstly, replacing traditional pixel points by super-pixel centers, a hypergraph evolutionary clustering method with low computational cost is developed to generate high-quality pseudo-labels; Then, on the basis of these pseudo-labels, taking classification accuracy as the optimized objective, a supervised band selection algorithm based on artificial bee colony is proposed. Moreover, a noise filtering mechanism based on grid division is designed to ensure the accuracy of pseudo-labels. Finally, the proposed algorithm is applied in 3 real datasets and compared with 6 classical band selection algorithms. Experimental results show that the proposed algorithm can obtain a band subset with high classification accuracy for all the three classifiers, KNN, Random Forest, and SVM. [ABSTRACT FROM AUTHOR]

A radial basis function neural network-based 2-satisfiability reverse analysis (RBFNN-2SATRA) primarily depends on adequately obtaining the linear optimal output weights, alongside the lowest iteration error. This study aims to investigate the effectiveness, as well as the capability of the artificial immune system (AIS) algorithm in RBFNN-2SATRA. Moreover, it aims to improve the output linearity to obtain the optimal output weights. In this paper, the artificial immune system (AIS) algorithm will be introduced and implemented to enhance the effectiveness of the connection weights throughout the RBFNN-2SATRA training. To prove that the introduced method functions efficiently, five well-established datasets were solved. Moreover, the use of AIS for the RBFNN-2SATRA training is compared with the genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and artificial bee colony (ABC) algorithms. In terms of measurements and accuracy, the simulation results showed that the proposed method outperformed in the terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Schwarz Bayesian Criterion (SBC), and Central Process Unit time (CPU time). The introduced method outperformed the existing four algorithms in the aspect of robustness, accuracy, and sensitivity throughout the simulation process. Therefore, it has been proven that the proposed AIS algorithm effectively conformed to the RBFNN-2SATRA in relation to (or in terms of) the average value of training of RMSE rose up to 97.5%, SBC rose up to 99.9%, and CPU time by 99.8%. Moreover, the average value of testing in MAE was rose up to 78.5%, MAPE was rose up to 71.4%, and was capable of classifying a higher percentage (81.6%) of the test samples compared with the results for the GA, DE, PSO, and ABC algorithms. [ABSTRACT FROM AUTHOR]

Inefficient remaining useful life (RUL) estimation may cause unpredictable failures and unscheduled maintenance of machining tools. Multi-sensor data fusion will improve the RUL prediction reliability by fusing more sensor information related to the machining process of tools. In this paper, a multi-sensor data fusion system for online RUL prediction of machining tools is proposed. The system integrates multi-sensor signal collection, signal preprocess by a complementary ensemble empirical mode decomposition, feature extraction in time domain, frequency domain and time-frequency domain by such methods as statistical analysis, power spectrum density analysis and Hilbert-Huang transform, feature selection by a Light Gradient Boosting Machine method, feature fusion by a tool wear prediction model based on back propagation neural network optimized by improved artificial bee colony (IABC-BPNN) algorithm, and the online RUL prediction model by a polynomial curve fitting method. An example is used to verify whether if the prediction performance of the proposed system is stable and reliable, and the results show that it is superior to its rivals. [ABSTRACT FROM AUTHOR]

In this paper, an improved fuel consumption and emissions control strategy based on a mathematical and heuristic approach is presented to optimize Parallel Hybrid Electric Vehicles (HEVs). The well-known Sequential Quadratic Programming mathematical method (SQP-Hessian approach) presents some limitations to achieve fuel consumption and emissions control optimization, as it is not able to find the global minimum, and it generally shows efficient results in local exploitation searches. The usage of a combined Modified Artificial Bee Colony algorithm (MABC) with the SQP approach is proposed in this work to obtain better optimal solutions and overcome these limitations. The optimization is performed with boundary conditions, considering that the optimized vehicle performance has to satisfy Partnership for a New Generation of Vehicles (PNGV) constraints. The weighting factor of the vehicle's performance parameters in the objective function is varied, and optimization is carried out for two different driving cycles, namely Federal Test Procedure (FTP) and Economic commission Europe—Extra Urban Driving Cycle (ECE-EUDC), using the MABC and MABC with SQP approaches. The MABC with SQP approach shows better performance in terms of fuel consumption and emissions than the pure heuristic approach for the considered vehicle with similar boundary conditions. Moreover, it does not present significant penalties for final battery charging and it offers an optimized size of the key vehicle's components for different driving cycles. [ABSTRACT FROM AUTHOR]

In the generation of operating system planning, saving utility cost (SUC) is customarily implemented to attain the forecasted optimal economic benefits in a generating system associated with renewable energy integration. In this paper, an improved approach for the probabilistic peak-shaving technique (PPS) based on computational intelligence is proposed to increase the SUC value. Contrary to the dispatch processing of the PPS technique, which mainly relies on the dispatching of each limited energy unit in sequential order, a modified artificial bee colony with a new searching mechanism (MABC-NSM) is proposed. The SUC is originated from the summation of the Saving Energy Cost and Saving Expected Cycling Cost of the generating system. In addition, further investigation for obtaining the optimal value of the SUC is performed between the SUC determined directly and indirectly estimated by referring to the energy reduction of thermal units (ERTU). Comparisons were made using MABC-NSM and a standard artificial bee colony and verified on the modified IEEE RTS-79 with different peak load demands. A compendium of the results has shown that the proposed method is constituted with robustness to determine the global optimal values of the SUC either obtained directly or by referring to the ERTU. Furthermore, SUC increments of 7.26% and 5% are achieved for 2850 and 3000 MW, respectively. [ABSTRACT FROM AUTHOR]

The artificial bee colony (ABC) algorithm, a relatively new swarm intelligence optimization technique, has been shown to be a competitive alternative to other population-based algorithms. This paper fundamentally modifies the solution search equations of the ABC in a manner that sends bee agents in search of three types of search regions that improve convergence speeds and proposes an innovative artificial bee colony directive (ABCD) algorithm. Moreover, this paper validates the ABCD algorithm by showing better performance by improving two familiar ABC variants in experimental tests. In addition, 10 applicable search strategies that adopt the proposed three search-region types are presented. The proposed ABCD not only improves the original ABC and its subsequently improved versions but is also useful for setting the search regions of other swarm intelligence algorithms. • Three types of search regions are suggested to improve the basic ABC. • The proposed search regions improve two ABC variants as well. • In addition, 10 applicable search strategies are presented. • Four kinds of experiments are conducted. [ABSTRACT FROM AUTHOR]

Feature selection is a basic task for pattern recognition and classification. It enhances the performance of the classification algorithms with the help of removing the redundant features. Thanks to eliminating irrelevant features, the computational time is decreased. Thus, intensive works have been carried out in this area. This paper proposes a new standard error-based artificial bee colony (SEABC) algorithm for the feature selection problem, which is developed by integrating standard error-based new solution search mechanisms into the original artificial bee colony algorithm. The SEABC algorithm is used for feature selection. Shannon entropy function is used to serve as the objective function of the SEABC algorithm. Thirteen datasets are used from UCI machine learning datasets. Features are selected according to Shannon conditional entropy values and then a threshold process is implemented to find their best relevant subset. Support Vector Machines (SVMs) and k-Nearest Neighbor (KNN) are used as the optimal classifiers. The proposed SEABC algorithm is compared with genetic algorithm (GA), particle swarm optimization (PSO), ABC, improved ABC (I-ABC), Gbest-guided ABC (GABC), and PS-ABC algorithms. In general, it is observed that the SEABC algorithm achieves better classification results than other well-known algorithms.

• An improved ABC (GGABC) is proposed and validated on benchmark functions. • A novel wrapper FS technique (BGGABC) for EMG signal classification is presented. • Discrete Wavelet Transform (DWT) is used for feature extraction. • The performance of BGGABC is evaluated and compared with other competitors. Electromyography (EMG) measures muscle relaxes or contractions during muscular activity through EMG signal. It plays a vital role in identifying muscle-related problems for clinical diagnosis. This paper presents an efficient EMG feature selection technique for classifying 17 different prosthetic hand movements recorded from 11 subjects. Two variants of the Artificial Bee Colony (ABC) algorithm, namely: i) Global Best Guided ABC (GbestABC) and ii) Gaussian ABC (GABC), are employed to propose an Improved Artificial Bee Colony (ABC) algorithm called Global Best Guided Gaussian ABC (GGABC) for solving global optimization problems. GbestABC performs better in the exploitation phase, whereas GABC performs better in the exploration phase in searching for the optimal solutions. The proposed GGABC takes advantage of GbestABC and GABC to counterbalance basic ABC's exploitation and exploration capability. Further, a binary version of GGABC known as binary GGABC (BGGABC) is developed to solve binary optimization problems and select optimal EMG signal classification features. Extensive experiments are carried out in three phases: i) GGABC for global optimization problems ii) BGGABC for EMG feature selection problems with other meta-heuristic-based competitors iii) BGGABC for EMG feature selection problems with well-known filter based techniques. K-nearest neighbor (KNN) classifier is used in experiments to validate and investigate the effectiveness of the proposed algorithm. Experimental result shows that the BGGABC-based EMG feature selection achieved 94.13% average classification accuracy and 97.06% best classification accuracy. Obtained results confirm that the proposed algorithm outperforms or is competitive with state-of-the-art algorithms in EMG feature selection and classification. [ABSTRACT FROM AUTHOR]

Today, advances in photo editing and manipulation software and widespread access to social media have made it easier for unauthorized persons to manipulate and copy digital images. Therefore, studies on protecting the copyright of images have been increasing in recent years. This paper proposes a Contourlet, discrete cosine transform, and singular value decomposition based robust image watermarking method using Artificial Bee Colony (ABC) algorithm to obtain high robustness with a predetermined quality threshold. In the proposed method, the strength factor and embedding positions are optimally determined using the ABC algorithm for each image. The predetermined quality threshold defined in the optimization procedure guarantees a PSNR value higher than 40 dB to ensure high imperceptibility. The robustness of the proposed method was evaluated by applying a total of thirteen attacks from five different attack groups. Results confirmed that the presented method is robust to the compression, noise, enhancement, geometric, and filtering attacks. • A quality ensured blind and robust image watermarking method is proposed. • The embedding strategy is developed to achieve high imperceptibility. • ABC determines the optimal strength factor and embedding positions. • ABC is used to obtain high robustness while guaranteeing a pre-defined quality. [ABSTRACT FROM AUTHOR]

Efficient clustering and routing is a main challenge in a wireless sensor network (WSN). To achieve better quality-of-service (QoS) performance, this work introduces k-medoids with improved artificial-bee-colony (K-IABC)-based energy-efficient clustering and the cross-layer-based Harris-hawks-optimization-algorithm (CL-HHO) routing protocol for WSN. To overcome the power-asymmetry problem in wireless sensor networks, a cross-layer-based optimal-routing solution is proposed. The goal of cross-layer routing algorithms is to decrease network-transmission delay and power consumption. This algorithm which was used to evaluate and select the effective path route and data transfer was implemented using MATLAB, and the results were compared to some existing techniques. The proposed CL-HHO performs well in packet-loss ratio (PLR), throughput, end-to-end delay (E2E), jitter, network lifetime (NLT) and buffer occupancy. These results are then validated by comparing them to traditional routing strategies such as hierarchical energy-efficient data gathering (HEED), energy-efficient-clustering routing protocol (EECRP), Grey wolf optimization (GWO), and cross-layer-based Ant-Lion optimization (CL-ALO). Compared to the HEED, EECRP, GWO, and CL-ALO algorithms, the proposed CL-HHO outperforms them. [ABSTRACT FROM AUTHOR]

Engineering design optimization problems are difficult to solve because the objective function is often complex, with a mix of continuous and discrete design variables and various design constraints. Our research presents a novel hybrid algorithm that integrates the benefits of the sine cosine algorithm (SCA) and artificial bee colony (ABC) to address engineering design optimization problems. The SCA is a recently developed metaheuristic algorithm with many advantages, such as good search ability and reasonable execution time, but it may suffer from premature convergence. The enhanced SCA search equation is proposed to avoid this drawback and reach a preferable balance between exploitation and exploration abilities. In the proposed hybrid method, named HSCA, the SCA with improved search strategy and the ABC algorithm with two distinct search equations are run alternately during working on the same population. The ABC with multiple search equations can provide proper diversity in the population so that both algorithms complement each other to create beneficial cooperation from their merger. Certain feasibility rules are incorporated in the HSCA to steer the search towards feasible areas of the search space. The HSCA is applied to fifteen demanding engineering design problems to investigate its performance. The presented experimental results indicate that the developed method performs better than the basic SCA and ABC. The HSCA accomplishes pretty competitive results compared to other recent state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The optimal requirements selection set aims primarily at careful search for the best requirements set of the next release of software during development process. This procedure is widely defined as the next release problem (NRP), which is also classified as NP‐hard dilemma. Several techniques, in literature, have been proposed to tackle NRP. However, in real examples, the earlier studies still immature as NRP still suffers interactions and restrictions that makes the problem more complicated. Although few interesting works have been presented, yet NRP, based on our study, could be further investigated and effectively tackled. In this research, therefore, NRP is devised as a multi‐objective optimisation problem. Two clashing objectives (satisfaction and cost) and two constraints (interactions forms) are formulated. To tackle NRP effectively, a new hybrid genetic and artificial bee colony algorithm (HGABC) is introduced. HGABC combines features of genetic and artificial bee colony algorithms. Experimental study, using case studies and three criteria, have been conducted to show HGABC's power of generating non‐dominated effective Pareto solutions versus the state‐of‐the‐art algorithms. Results indicate that HGABC does not just outperform its rivals, yet also gives better Pareto solutions in terms of diversity and quality for almost all the instances of this problem. [ABSTRACT FROM AUTHOR]

Fast accurate and reliable identification of photovoltaic (PV) model parameters based on measured current-voltage (IV) characteristic curves is significant for the analysis, evaluation and diagnosis of the operating status of in-situ PV arrays to optimize solar energy conversion. Although many techniques have been proposed, it is still challenging to achieve both fast and accurate parameters identification with high reliability. In this paper, based on a new eagle strategy, an improved adaptive Nelder-Mead simplex (NMS) hybridized with the artificial bee colony (ABC) metaheuristic, EHA-NMS, is proposed to improve parameters identification of PV models. The proposed novel eagle strategy consists of three cascaded stages: coarse exploration, coarse exploitation and fine exploitation, through which the strong global exploration of ABC and the powerful local exploitation of NMS merits are combined and the high computation burden of ABC and the high probability of being trapped in local minima of NMS drawbacks are alleviated. The EHA-NMS is compared with some state-of-the-art algorithms on three benchmark problems of model parameters identification of a R.T.C France solar cell and Photowatt-PWP201 PV module which are commonly adopted in the literature. The intensive experiment result and analysis show that the EHA-NMS outperforms other state-of-the-art techniques especially in terms of convergence and reliability. Due to the high computation efficiency, the EHA-NMS can be easily ported to embedded systems to realize online real-time parameters identification of PV models. [ABSTRACT FROM AUTHOR]

Ear recognition is a new biometric technology that competes with well-known biometric modalities such as fingerprint, face and iris. However, this modality suffers from common image acquisition problems, such as change in illumination, poor contrast, noise and pose variation. Using a 3D ear models reduce rotation, scale variation and translation-related problems, but they are computationally expensive. This paper presents a new architecture of ear biometrics that aims at solving the acquisition problems of 2D ear images. The proposed system uses a new ear image contrast enhancement approach based on the gray-level mapping technique, and uses an artificial bee colony (ABC) algorithm as an optimizer. This technique permits getting better-contrasted 2D ear images. In the feature extraction stage, the scale invariant feature transform (SIFT) is used. For the matching phase, the Euclidean distance is adopted. The proposed approach was tested on three reference ear image databases: IIT Delhi, USTB 1 and USTB 2, and compared with traditional ear image contrast enhancement approaches, histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE). The obtained results show that the proposed approach outperforms traditional ear image contrast enhancement techniques, and increases the amount of detail in the ear image, and consequently improves the recognition rate. [ABSTRACT FROM AUTHOR]