Improved Artificial Bee Colony Research Articles

Research on reverse order impoundment mode of cascade reservoir flood control system: Case study on upper reaches of Yangtze River

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.

AccurateSparameter prediction of L‐shaped probe‐fed patch antenna with an improved artificial bee colony algorithm based on artificial neural network

The determination of antenna geometric parameters is a major difficulty in the process of antenna design. With the progress of communication technology, the requirements of antenna performance are gradually improved, the design process of antenna becomes more complex to meet the performance requirements of engineering. As a mathematical model with strong information and data processing ability, artificial neural network (ANN) has been gradually applied to antenna parameters prediction and other related fields. In order to quickly and accurately obtain the model of high-performance L-shaped probe-fed patch antenna, an improved artificial bee colony (IABC) algorithm was proposed. The algorithm was improved to overcome the weak local optimization ability of standard artificial bee colony (ABC) algorithm. It can be seen from the experimental results that the IABC algorithm based on ANN is an effective optimization algorithm for microstrip antenna design optimization.

A new standard error based artificial bee colony algorithm and its applications in feature selection

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.

Locating abrupt disaster emergency logistics centres using improved artificial bee colony (IABC) algorithm.

Emergency management is conceptualized as a complex, multi-objective optimization problem related to facility location. However, little research has been performed on the horizontal transportation of emergency logistics centres. This study makes contributions to the multi-objective locating abrupt disaster emergency logistics centres model with the smallest total cost and the largest customer satisfaction. The IABC algorithm is proposed in this paper to solve the multi-objective emergency logistics centres locating problem. IABC algorithm can effectively calculate the optimal location of abrupt disaster emergency logistics centres and the demand for relief materials, and it can solve the rescue time satisfaction for different rescue sites. (1) IABC has better global search capabilities to avoid premature convergence and provide a faster convergence speed, and it has optimal solution accuracy, solution diversity and robustness. (2) From the three optimal objective function values obtained, the optimal objective function values obtained by IABC algorithm are obviously better than ABC and GABC algorithms. (3) From the convergence curves of three objective functions the global search ability and the stability of IABC algorithm are better than those of ABC and GABC algorithm. The improved ABC algorithm has proven to be effective and feasible. However, emergency relief logistics systems are very complex and involve many factors, the proposed model needs to be refined further in the future.

A Multi-objective optimized node deployment algorithm for Wireless Sensor Networks Based on the Improved ABC

In the Internet of Things(IoT) for smart fire applications, sensors are deployed at different places and collect multiple types of data during their work time. Since the coverage of a sensor is limited, how to deploy sensors is important to reduce the number of sensors and improve the quality of wireless sensor networks(WSNs) service. The service quality and network lifetime of wireless sensor networks also are dramatically varied across different coverage and node deployment strategies. There are two crucial problems in WSNs, i.e. node deployment with lowest cost, and coverage optimization with maximum coverage rate, need to be solved. In this research, we focus on the problem of coverage optimization and node deployment. Based on the binary coverage probability model, we develop the joint binary coverage probability model and Improved Artificial Bee Colony(IABC) algorithm. The result of simulation shows that our proposed deployment algorithm have a greater coverage ratio in the case of fewer iterations when the volume of deployed sensors is the same compared with the standard ABC algorithm.

Path Planning of Unmanned Autonomous Helicopter Based on Human-Computer Hybrid Augmented Intelligence.

Unmanned autonomous helicopter (UAH) path planning problem is an important component of the UAH mission planning system. The performance of the automatic path planner determines the quality of the UAH flight path. Aiming to produce a high-quality flight path, a path planning system is designed based on human-computer hybrid augmented intelligence framework for the UAH in this paper. Firstly, an improved artificial bee colony (I-ABC) algorithm is proposed based on the dynamic evaluation selection strategy and the complex optimization method. In the I-ABC algorithm, the following way of on-looker bees and the update strategy of nectar source are optimized to accelerate the convergence rate and retain the exploration ability of the population. In addition, a space clipping operation is proposed based on the attention mechanism for constructing a new spatial search area. The search time can be further reduced by the space clipping operation under the path planning result within acceptable changes. Moreover, the entire optimization process and results can be feeded back to the knowledge database by the human-computer hybrid augmented intelligence framework to guide subsequent path planning issues. Finally, the simulation results confirm that a feasible and effective flight path can be quickly generated by the UAH path planning system based on human-computer hybrid augmented intelligence.

Anomaly Detection Collaborating Adaptive CEEMDAN Feature Exploitation with Intelligent Optimizing Classification for IIoT Sparse Data

IIoT (Industrial Internet of Things) has gained considerable attention and has been increasingly applied due to its ubiquitous sensing and communication. However, the sparse characteristic of sensing data in distributed IIoT networks may bring out tremendous challenges to implement the security protection measures. Based on the design of centralized data gathering and forwarding, this paper proposes a novel anomaly detection approach for IIoT sparse data, which can successfully collaborate the adaptive CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) feature exploitation with one intelligent optimizing classification. Furthermore, in the adaptive CEEMDAN feature exploitation, the CEEMDAN energy entropy based on adaptive IMF (Intrinsic Mode Function) selection is designed to extract the sensing features from IIoT sparse data; in the intelligent optimizing classification, one effective OCSVM (One‐Class Support Vector Machine) classifier optimized by the IABC (Improved Artificial Bee Colony) swarm intelligence algorithm is introduced to detect various abnormal sensing features. The experimental results show that, not only does the CEEMDAN energy entropy based on adaptive IMF selection accurately describe the change of industrial production by analyzing the probability distribution and energy distribution of sparse sensing data, but also the proposed IABC‐OCSVM classifier has higher detection efficiency compared with the OCSVM classifiers optimized by other swarm intelligence algorithms.

An Improved Artificial Bee Colony Algorithm and Its Application in Machine Learning

In order to effectively overcome the problems and shortcomings of the basic improved artificial bee colony algorithm in solving the optimization accuracy of complex standard test functions, the low accuracy of the solution function and the large blindness of the local search strategy, a more basic improved artificial bee colony is proposed. Parameter optimization algorithm. The above artificial bee colony algorithm introduces the conjugate gradient method with strong local optimization search strategy performance in the follow bee stage of the basic improved artificial bee colony algorithm to change the optimization search strategy, and replaces the blind search strategy with a local deterministic optimization search strategy. The sexual optimization search reduces the randomness, enhances the local certainty of the food source of the follower bee and the optimization search ability, and ensures that each update of the follower bee’s food source will quickly be substantially improved. This improved artificial bee colony algorithm is widely used in parameter optimization of traditional dengue virus propagation model. The simulation results of the improved standard test function on the problem show that the improved artificial bee colony parameter optimization algorithm has higher optimization and solution function accuracy during simulation than the basic improved artificial bee colony parameter optimization algorithm. The obtained standard test the parameter output corresponding to the dengue virus model parameter output is in good agreement with the actual situation when the data is simulated.

Optimal Non-Convex Combined Heat and Power Economic Dispatch via Improved Artificial Bee Colony Algorithm

It is well accepted that combined heat and power (CHP) generation can increase the efficiency of power and heat generation at the same time. With the increasing penetration of CHPs, determination of economic dispatch of power and heat becomes more complex and challenging. The CHP economic dispatch (CHPED) problem is a challenging optimization problem due to non-linearity and non-convexity in both objective function and constraints. Hence, in this paper a novel meta-heuristic algorithm, namely improved artificial bee colony (IABC) algorithm is proposed to solve the CHPED problem. The valve-point effects, power losses as well as the feasible operation region of CHP units are taken into account in the proposed CHPED problem model and the optimal dispatch of power/heat outputs of CHP units is determined via the proposed IABC algorithm. The proposed algorithm is applied on three test systems, in which two of them are large-scale CHPED benchmarks. The obtained results and comprehensive comparison with available methods, demonstrate the superiority of the proposed algorithm for dealing with non-convex and constrained CHPED problem.

An improved artificial bee colony algorithm for distributed heterogeneous hybrid flowshop scheduling problem with sequence-dependent setup times

The distributed manufacturing mode which is widely used in the modern manufacturing system often contains the different status of different workshops, called as the heterogeneous workshops. However, the existing work of distributed shop scheduling assume that there are identical workshops, which lacks the consideration of practical constraints about heterogeneous workshops. Therefore, this paper firstly focuses on the distributed heterogeneous hybrid flowshop scheduling problem (DHHFSP) with unrelated parallel machines (UPM) and the sequence-dependent setup time (SDST). This is a typical NP-hard problem which is quite hard to be solved. This paper designs a machine position-based mathematical model and proposes an improved artificial bee colony (IABC) algorithm for this problem. The proposed IABC employs a two-level encoding and a decoding method of the machine selection to ensure feasible schedules. The IABC adopts the factory assignment rule and greedy iterative strategy to generate high quality initial solutions. And the IABC adopts solutions update techniques: the local exploitation around critical factories, a hybrid search strategy combines the advantages of simulated annealing (SA) and a retention mechanism. These techniques can keep the diversity of solution space and enhance the computational efficiency. There are 320 instances randomly generated and used to verify the performance of the IABC. Through the comparison with the reported state-of-the-art algorithms, the effectiveness of proposed IABC is shown clearly.

An improved artificial bee colony for multi-objective distributed unrelated parallel machine scheduling

Distributed scheduling has been frequently investigated with the increasing applications of multi-factory production; however, distributed unrelated parallel machine scheduling problem (DUPMSP) is seldom considered. In this study, multi-objective DUPMSP is considered and an improved artificial bee colony (IABC) is presented to minimise makespan and total tardiness simultaneously. Problem-related knowledge is proved and knowledge-based neighbourhood search is proposed. Employed bees and onlooker bees are decided dynamically and not given fixed numbers in the search process. Different combinations of global search and neighbourhood search are used in employed bee phase and onlooker bee phase. A new way is applied to execute scout phase. Extensive experiments are conducted on the effect of new strategies and performances of IABC. Computational results demonstrate that IABC has reasonable and effective strategies and very competitive performances on solving the considered DUPMSP.

Power Capacity Optimization in a Photovoltaics-Based Microgrid Using the Improved Artificial Bee Colony Algorithm

Although the combined cooling, heating and power (CCHP) microgrid is feasible for achieving a high energy utilization efficiency, the fluctuation of energy sources, such as a photovoltaic system and multiple loads, may affect the safety, economics and stability in CCHP microgrid operation. For this reason, this paper establishes a mathematical model using a multi-objective optimization mechanism for resolving the influence of economy and energy allocation in the mixed photovoltaic type CCHP microgrid. It is based on analytic hierarchy process (AHP) to determine the individual weight of objective function optimization for the multi-objective power capacity allocation. The improved artificial bee colony (IABC) based on the whale search and dynamic selection probability can achieve an optimization solution, reaching a stable operation state and reasonable capacity configuration in the microgrid system. The performance results confirm that the proposed algorithm is superior to others in both convergence speed and accuracyfor the capacity allocation of the CCHP microgrid.

Dynamic XFEM-based detection of multiple flaws using an improved artificial bee colony algorithm

This paper proposes a novel strategy for detecting multiple flaws based on the dynamic extended finite element method (XFEM) and an improved artificial bee colony (IABC) algorithm. To improve the qualities of the initial iterative candidates, a chaotic sequence is adopted to replace the random sequence in the conventional artificial colony algorithm. Meanwhile, an adaptive parameter dimension search is proposed to prevent the iterative solution from falling into a local optimum trap, and a set of subcandidates is utilized to describe the shape of irregular flaws. The detection process of multiple flaws in the structure is divided into two stages. First, the improved ABC algorithm with preset discrete points is combined with topology variables to obtain the number of and approximate locations of the flaws and output the corresponding narrow subdomains. Second, a set of circular candidates is regenerated in the new subdomains derived from the first stage to capture the target flaws. In addition, XFEM is employed for the forward analysis, and the discontinuities of the flaws are characterized by level set functions. Several numerical experiments are performed to illustrate the performance of the proposed strategy. The effects of sensor layout and measurement noise with different levels are considered to assess the robustness of this strategy. The results show that this strategy is able to quantify the number of and capture the shape of unknown flaws. The narrow distributions of the Monte Carlo simulations indicate that the proposed strategy is robust against noise.

Construction cost minimisation of the stepped spillway using improved particle swarm optimisation and artificial bee colony algorithms

AbstractIn this paper, the improved artificial bee colony (IABC) and improved particle swarm optimisation (IPSO) have been used for the optimal design of stepped spillway with the minimisation of construction cost as the energy dissipater structure for a dam project. Generally, the construction cost of this structure is the most expensive part of the project. However, traditional designing methods of stepped spillway such as the Vittal and Porey (VP) approach cannot find optimal dimensions of the stepped spillway with minimum cost. Therefore, the optimisation methods such as meta‐heuristic algorithms have been used. As a case study, Tehri dam’s stepped spillway in India has been considered here and the optimal dimensions of this spillway have been obtained using IPSO, IABC algorithms and the results have been compared with VP approach and other available results. Comparison of the results shows that when the three‐stepped spillway is considered, the results of IABC and IPSO algorithms are improved by 17.72% in comparison with VP results. In addition, when the four‐stepped spillway is considered, the results of IABC, IPSO algorithms are, respectively, improved by 16.47% and 16.53% in comparison with the VP results.

Hearing loss detection by discrete wavelet transform and multi-layer perceptron trained by nature-inspired algorithms

(Aim) For detecting the hearing loss (HL) more accurately and efficiently, the new computer-aid diagnosis (CAD) based on a nature-inspired algorithm (NIAs) is proposed in this study. (Method) First, the discrete wavelet transform (DWT) is used for extracting texture features from the brain images, and then the principle component analysis (PCA) is employed to decrease the dimension of features. Second, the Multi-Layer Perceptron (MLP) is used as a classifier. Traditional gradient-based descent algorithms are vulnerable to get struck at local minima; thus, the NIAs are introduced. The differential evolution algorithm (DE), particle swarm optimization (PSO), artificial bee colony algorithm (ABC), and improved ABC (IABC) are employed to train MLP. Because the ordinary ABC is good at exploration but gives a poor performance at exploitation, therefore a new model of ABC, called IABC is proposed. The K-fold validation method is utilized to measure the performance of the CAD. (Result) To verify the performance of our method, The CAD based on IABC is compared with state-of-the-art-approaches. (Conclusion) The experiment results show that the overall accuracy of our method has the highest overall accuracy among five approaches. Therefore, the proposed CAD is effective method for detecting hearing loss.

Optimized SVM-Driven Multi-Class Approach by Improved ABC to Estimating Ship Systems State

In the intelligent ship field, with the upgrading of ship maintenance mode, the human-centered system maintenance will be gradually replaced by the artificial intelligence decision methods. To improve the training speed and testing accuracy of the state estimation model, an optimized Support Vector Machine (SVM) driven approach by Improved Artificial Bee Colony (IABC) was proposed to solve the global parameters optimization problem. First, the IABC method was achieved from three aspects: nectar source initializing, employed bee global neighborhood searching, and scouts mutation neighborhood searching. Second, the multi-class SVM with one-against-one classifiers was selected, and the best global parameters were achieved by the IABC. Third, the optimized SVM model was adopted in the testing to verify the effectiveness of state estimation. Finally, the elaborated methodology was applied to two actual ship systems to get the analysis results. The effectiveness was verified by using two examples. The results show the following: the IABC optimized SVM can obtain the global optimal parameters at a faster speed than the traditional ABC optimized method; the IABC optimized method can help the training start with better initial parameters, and get a higher classification accuracy rate than the traditional ABC optimized method. Based on the comparative analysis results, the IABC optimized SVM shows an obvious advantage of parameter optimization in the training process, and it can also significantly improve the model training efficiency and achieve a higher state estimation accuracy. The optimized SVM by IABC is an effective state estimation method in ship systems.

Optimal ECC-based signcryption algorithm for secured video compression process in H.264 encoder

Combination of cryptography method and video technology is a video encryption. Video encryption process is a total and demonstrable security of video data. For the purpose of protecting the video sequence, we have intended to recommend a video compression procedure along with its encryption for providing secured video compression framework. In this document we have suggested a method for ECC-based signcryption algorithm for secured video compression process. At first, encryption process will be used on the motion vector by applying elliptic curve cryptography (ECC)-based signcryption algorithm. The suggested method uses ECC method for the generation of public and private key. At the point when contrasted with the other encryption algorithms like small key, more security, increased velocity, little storage space and low data transfer capacity ECC has particular preferences. The suggested method employs the improved artificial bee colony (IABC) algorithm in order to optimise the private key. Next, the optimal selection of private key applied to encrypt the motion vector. By means of different security attacks such as man in middle (MiM) attack, brute force and denial of service (DoS) attacks, the security of the suggested method will be examined.

An Improved Artificial Bees Colony Algorithm to Solve Minimal Exposure Problem in Wireless Sensor Networks

The problem of exposure, which is associated with the quality of coverage, is a basic issue faced in wireless sensor networks. Search for the Minimum Exposure Path (MEP) is one among the crucial problems in Wireless Sensor Networks (WSNs). The exposure with respect to the MEP is one of the indicators used for measuring the coverage quality of a WSN, depicting the efficiency with which the monitoring of a mobile target is done all along the sensing field. The available hybrid technique does not have sufficient accuracy to get the MEP, which is too complicated, and is not suitable to networks having heterogeneous sensor nodes, or in large number, or an all-sensor field intensity function. In order to get over these problems, an Improved Artificial Bee Colony (IABC) model is introduced for the MEP problem associated with getting the optimal shortest path in this research work. The IABC model exhibits an extensive use of energy and data congestion will happen. In order to have an efficient resolution to the data congestion problem, Prim’s algorithm has introduced the link level congestion scenario. Also for effective solving of this issue, in accordance with the features of the node, a semi Hidden Markov Model is developed to solve the problem of energy efficiency in WSN model. A number of experiments were carried out, and the results show that the proposed model and the designed IABC can help in increasing the solution accuracy and can be useful to not just the heterogeneous sensors’ case but also the scenario in where there are a big number of sensor nodes and also an all-sensor field intensity function.

Meta-heuristic algorithm for solving vehicle routing problems with time windows and synchronized visit constraints in prefabricated systems

Prefabricated construction has attracted research interest as it can significantly improve the energy, cost, and time efficiency of construction. However, dispatching the required prefabricated components to construction sites in a prefabricated system is challenging. To address this issue, we modeled the dispatching problem as a special type of vehicle routing problem with time windows (VRPTW) and solved it by using an improved artificial bee colony (IABC) algorithm. First, to efficiently solve the cross-synchronization problem that occurs in prefabricated systems, two problem-specific lemmas were derived. Then, a hybrid initialization strategy was developed to generate feasible and efficient solutions and a well-designed encoding repair strategy was utilized to make solutions feasible. Finally, a variable length local search strategy was embedded to enhance the exploitation ability. To verify the performance of the proposed IABC algorithm, 55 instances were generated and used for simulation tests. Three efficient algorithms, including the two-phase genetic algorithm (TPGA), improved tabu search algorithm (ITSA), and adaptive large neighborhood search (ALNS) heuristic, were selected for detailed comparisons. Our simulation results show that, considering the energy consumption metric, the proposed algorithm yields average deviations of about 0.099, 0.096, and 0.143 times the values obtained with the TPGA, ITSA, and ALNS heuristic, respectively. The simulation results confirmed that the proposed algorithm can solve the VRPTW in prefabricated systems with high efficiency.

Semi-Supervised Adaptive Neuro Fuzzy Inference System (SSANFIS) for Effective Classification of Alzheimer's Disease

Alzheimer's disease (AD) is the most popular dementia in elderly people worldwide. It affects memory of the patient. The early detection of Alzheimer’s disease still a challenge because of the estimation of the scans depends on manual directing and visual reading. To overcome this issue, Semi-Supervised Adaptive Neuro Fuzzy Inference System (SSANFIS) is introduced for effective classification of alzheimer's disease. In this work, an Improved Artificial Bee Colony (IABC) algorithm is used for preprocessing step which provides higher classification performance. It removes noises from the images based on the employed bees, onlooker bees and scout bees calculation. Then using the Adaptive Median Filter (AMF) enhances the quality of the images by comparing each pixel in the image to its surrounding neighbor pixels. The Hybrid Wavelet Transform (HWT) is enhanced using Haar with Walsh wavelet transform; it is used to extract the features from the MRI images. Unsupervised Fuzzy C Means (USFCM) is applied for selecting the important features from the extracted features. The model is learned by using Semi-Supervised Adaptive Neuro Fuzzy Inference System (SSANFIS) approach. It selects optimal fuzzy rules based on the higher frequency rules which are used to increase the accurate classification results which provide higher results than other methods