Onlooker Bee Research Articles

A new artificial bee colony algorithm based on modified search strategy

Artificial bee colony (ABC) is an efficient global optimisation algorithm. It has attracted the attention of many researchers because of its simple concept and strong exploration. However, it exhibits weak exploitation capability. To improve this case, a novel ABC with modified search strategy (namely MSABC) is proposed in this work. In MSABC, some modified elite solutions are preserved and used to guide the search. In addition, MSABC uses the modified elite solutions to generate offspring to replace the probability selection in the onlooker bee phase. To evaluate the capability of MSABC, 22 classical problems are tested. Results demonstrate MSABC achieves superior performance than five other ABC variants.

Artificial bee colony based on adaptive search strategy and random grouping mechanism

As a popular global optimization algorithm, artificial bee colony (ABC) has strong search ability and simple concept. However, ABC has some deficiencies. The exploitation ability of ABC is not as strong as its exploration ability. The original roulette selection in the onlooker bee search will gradually lose its effect with increasing of iterations. In order to tackle the above problems, an efficient ABC based on adaptive search strategy and random grouping mechanism (called ASRGABC) is proposed in this paper. Firstly, an adaptive search strategy is designed by comparing the success rate of the current and previous iterations. According to the changes of the success rate, a suitable search strategy is adaptively selected. Then, a random grouping mechanism is proposed to replace the original roulette selection. The whole population is randomly divided into several groups. The onlooker bees are allowed to follow the best solution in each group. Based on the random grouping, the search strategy is modified. Thirdly, opposition-based learning is employed to enhance the scout bee phase. To verify the performance of ASRGABC, 22 classical benchmark problems and 28 CEC 2013 benchmark problems are tested. Experimental results show ASRGABC obtains better performance than thirteen other ABC variants.

Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework

There are generally many redundant and irrelevant features in high-dimensional datasets, which leads to the decline of classification performance and the extension of execution time. To tackle this problem, feature selection techniques are used to screen out redundant and irrelevant features. The artificial bee colony (ABC) algorithm is a popular meta-heuristic algorithm with high exploration and low exploitation capacities. To balance between both capacities of the ABC algorithm, a novel ABC framework is proposed in this paper. Specifically, the solutions are first updated by the process of employing bees to retain the original exploration ability, so that the algorithm can explore the solution space extensively. Then, the solutions are modified by the updating mechanism of an algorithm with strong exploitation ability in the onlooker bee phase. Finally, we remove the scout bee phase from the framework, which can not only reduce the exploration ability but also speed up the algorithm. In order to verify our idea, the operators of the grey wolf optimization (GWO) algorithm and whale optimization algorithm (WOA) are introduced into the framework to enhance the exploitation capability of onlooker bees, named BABCGWO and BABCWOA, respectively. It has been found that these two algorithms are superior to four state-of-the-art feature selection algorithms using 12 high-dimensional datasets, in terms of the classification error rate, size of feature subset and execution speed.

A dynamical artificial bee colony for vehicle routing problem with drones

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

An Intelligent Artificial Bee Colony and Adaptive Bacterial Foraging Optimization Scheme for Reliable Breast Cancer Diagnosis

: Breast cancer is essential to be detected in primitive localized stage for enhancing the possibility of survival since it is considered as the major malediction to the women society around the globe. Most of the intelligent approaches devised for breast cancer necessitate expertise that results in reliable identification of patterns that conclude the presence of oncology cells and determine the possible treatment to breast cancer patients in order to enhance their survival feasibility. Moreover, the majority of the existing schemes of the literature incur intensive labor and time, inducing a predominant impact over the diagnosis time utilized for detecting breast cancer cells. An Intelligent Artificial Bee Colony and Adaptive Bacterial Foraging Optimization (IABC-ABFO) scheme is proposed for facilitating a better rate of local and global searching ability in selecting the optimal features subsets and optimal parameters of ANN considered for breast cancer diagnosis. In the proposed IABC-ABFO approach, the traditional ABC algorithm used for cancer detection is improved by integrating an adaptive bacterial foraging process in the onlooker bee and the employee bee phase that results in optimal exploitation and exploration. The investigation of results of the proposed IABC-ABFO approach facilitating the use of the Wisconsin breast cancer dataset showed a mean classification accuracy of 99.52% which is higher than the existing breast cancer detection schemes.

A Tristage Adaptive Biased Learning for Artificial Bee Colony

In recent ten years, artificial bee colony (ABC) has attracted more and more attention, and many state-of-the-art ABC variants (ABCs) have been developed by introducing different biased information to the search equations. However, the same biased information is employed in employed bee and onlooker bee phases, which will cause over exploitation and lead to premature convergence. To overcome this limit, an effective framework with tristage adaptive biased learning is proposed for existing ABCs (TABL + ABCs). In TABL + ABCs, the search direction in the employed bee stage is guided by learning the ranking biased information of the parent food sources, while in the onlooker bee stage, the search direction is determined by extracting the biased information of population distribution. Moreover, a deletion-restart learning strategy is designed in scout bee stage to prevent the potential risk of population stagnation. Systematic experiment results conducted on CEC2014 competition benchmark suite show that proposed TABL + ABCs perform better than recently published AEL + ABCs and ACoS + ABCs.

Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem

In this era of unprecedented economic and social prosperity, problems such as energy shortages and environmental pollution are gradually coming to the fore, which seriously restrict economic and social development. In order to solve these problems, green shop scheduling, which is a key aspect of the manufacturing industry, has attracted the attention of researchers, and the widely used flow shop scheduling problem (HFSP) has become a hot topic of research. In this paper, we study the fuzzy hybrid green shop scheduling problem (FHFGSP) with fuzzy processing time, with the objective of minimizing makespan and total energy consumption. This is more in line with real-life situations. The non-linear integer programming model of FHFGSP is built by expressing job processing times as triangular fuzzy numbers (TFN) and considering the machine setup times when processing different jobs. To address the FHFGSP, a discrete artificial bee colony (DABC) algorithm based on similarity and non-dominated solution ordering is proposed, which allows individuals to explore their neighbors to different degrees in the employed bee phase according to a sequence of positions, increasing the diversity of the algorithm. During the onlooker bee phase, individuals at the front of the sequence have a higher chance of being tracked, increasing the convergence rate of the colony. In addition, a mutation strategy is proposed to prevent the population from falling into a local optimum. To verify the effectiveness of the algorithm, 400 test cases were generated, comparing the proposed strategy and the overall algorithm with each other and evaluating them using three different metrics. The experimental results show that the proposed algorithm outperforms other algorithms in terms of quantity, quality, convergence and diversity.

Minimizing the Total Cost of Inventory by Using Artificial Bee Colony Algorithm

Inventory denotes the assets and important resources utilised in an organisation or industry in which it offers various benefits for firm cash flow and persistent viability. If the production inventory of a company faces shortage or other problems such as greater operating cost due to interest payments on loan cash, the management will be in a critical situation, thereby leading to loss of customers and profits. This encourages organisations to implement inventory control, which aims for the lowest total cost of inventory, among others. Therefore, this study employed the Artificial Bee Colony (ABC) algorithm to ascertain the minimum total cost of inventory. The algorithm characterised a swarm-based meta-heuristic algorithm comprised of three divisions of bee troops in the ABC model, namely employed, onlooker, and scout bees. The resulting outcomes revealed a minimum total cost of inventory obtainable of RM45.38, with an optimal order quantity of 37 units.

Solving maximum quasi-clique problem by a hybrid artificial bee colony approach

Given a graph and a threshold (γ), the maximum quasi-clique (MQC) problem is to find a maximum cardinality subset of vertices in the graph such that the edge density of a corresponding induced subgraph is not less than γ. This problem istended version of the famous maximum clique problem, which arises in a various application domains and is known as NP-hard. In this study, we propose a heuristic method called the hybrid artificial bee colony (HABC) algorithm to address the MQC problem by incorporating several dedicated strategies into the artificial bee colony framework, which starts with an opposition-based initialization phase and then repeatedly alternates between an employed bees phase, an onlooker bees phase and a scout bees phase to perform the search. The employed bees phase and onlooker bees phase employ a decent local search and solution-based tabu search to locate the local optima and intensify the search,whereas the scout bees phase employs an opposition-based mechanism to reconstruct the best-found solutions to escape from the local optima and diversify the search. Experimental results indicate that our proposed HABC algorithm can improve the best-known solutions for 46 out of 112 problem instances and match the best-known solutions for 63 instances. Comparisons with the state-of-the-art heuristics and exact methods demonstrate the merits of the proposed algorithm.

Miner revenue optimization algorithm based on Pareto artificial bee colony in blockchain network

In order to improve the revenue of attacking mining pools and miners under block withholding attack, we propose the miner revenue optimization algorithm (MROA) based on Pareto artificial bee colony in blockchain network. MROA establishes the revenue optimization model of each attacking mining pool and revenue optimization model of entire attacking mining pools under block withholding attack with the mathematical formulas such as attacking mining pool selection, effective computing power, mining cost and revenue. Then, MROA solves the model by using the modified artificial bee colony algorithm based on the Pareto method. Namely, the employed bee operations include evaluation value calculation, selection probability calculation, crossover operation, mutation operation and Pareto dominance method, and can update each food source. The onlooker bee operations include confirmation probability calculation, crowding degree calculation, neighborhood crossover operation, neighborhood mutation operation and Pareto dominance method, and can find the optimal food source in multidimensional space with smaller distribution density. The scout bee operations delete the local optimal food source that cannot produce new food sources to ensure the diversity of solutions. The simulation results show that no matter how the number of attacking mining pools and the number of miners change, MROA can find a reasonable miner work plan for each attacking mining pool, which increases minimum revenue, average revenue and the evaluation value of optimal solution, and reduces the spacing value and variance of revenue solution set. MROA outperforms the state of the arts such as ABC, NSGA2 and MOPSO.

Rayleigh Wave Dispersion Curve Inversion with the Artificial Bee Colony Algorithm

Rayleigh wave dispersion curve inversion is a non-linear iterative optimization process with multi-parameter and multi-extrema. It is difficult to carry out inversion and reconstruction of stratigraphic parameters quickly and accurately with a single linear or non-linear inversion for the data processing of Rayleigh waves with complex seismic geological conditions. We proposed a new method that combines artificial bee colony algorithm (ABC) and damped least squares algorithm (DLS) to invert Rayleigh wave dispersion curve. First, food sources are initialized in a large scale of the model based on the prior geological information. Then, after three kinds of bee operators (employed bees, onlooker bees and scout bees) transform each other and perform search optimization with several iterations, the targets are converged near the optimal solution to obtain an initial S-wave velocity model. Finally, the final S-wave velocity model is obtained by local optimization of DLS inversion with fast convergence and strong stability. The correctness of the method has been verified by one high-velocity interlayer model, and it was further applied to a real Rayleigh wave dataset. The results show that our method not only absorbs the advantages of ABC global search optimization and strong adaptability, but also makes full use of the advantages of DLS inversion, such as high accuracy and fast convergence speed. The inversion strategy can effectively suppress the inversion falling into local extrema, get rid of the dependence on an initial model, enhance the inversion stability, further improve the convergence speed and inversion accuracy, while has good anti-noise ability.

An Improved Artificial Bee Colony for Feature Selection in QSAR

Quantitative Structure–Activity Relationship (QSAR) aims to correlate molecular structure properties with corresponding bioactivity. Chance correlations and multicollinearity are two major problems often encountered when generating QSAR models. Feature selection can significantly improve the accuracy and interpretability of QSAR by removing redundant or irrelevant molecular descriptors. An artificial bee colony algorithm (ABC) that mimics the foraging behaviors of honey bee colony was originally proposed for continuous optimization problems. It has been applied to feature selection for classification but seldom for regression analysis and prediction. In this paper, a binary ABC algorithm is used to select features (molecular descriptors) in QSAR. Furthermore, we propose an improved ABC-based algorithm for feature selection in QSAR, namely ABC-PLS-1. Crossover and mutation operators are introduced to employed bee and onlooker bee phase to modify several dimensions of each solution, which not only saves the process of converting continuous values into discrete values, but also reduces the computational resources. In addition, a novel greedy selection strategy which selects the feature subsets with higher accuracy and fewer features helps the algorithm to converge fast. Three QSAR datasets are used for the evaluation of the proposed algorithm. Experimental results show that ABC-PLS-1 outperforms PSO-PLS, WS-PSO-PLS, and BFDE-PLS in accuracy, root mean square error, and the number of selected features. Moreover, we also study whether to implement scout bee phase when tracking regression problems and drawing such an interesting conclusion that the scout bee phase is redundant when dealing with the feature selection in low-dimensional and medium-dimensional regression problems.

Improved artificial bee colony algorithm based on damping motion and artificial fish swarm algorithm

The basic artificial bee colony (ABC) algorithm is easy to fall into local optimum, and it has poor exploitation ability and slow convergence speed. According to the defects, the improved algorithm is proposed, which is based on damping motion and artificial fish swarm(AFS) algorithm. In the employed bees phase, the swarm behavior of AFS algorithm which can avoid sinking into local optimum is introduced. At the same time, considering the slow convergence speed, employed bees adopt the multidimensional updating process in the early stage of iteration. In the onlooker bees phase, an adaptive step size based on damping motion is designed to replace the random step size, which can balance the global exploration and the local exploitation. Through the simulation of six test functions, we get the average convergence algebra, the mean, the best and the variance of the optimal solution through 30 experiments. Simulation shows that the improved algorithm has a better performance than the basic one.

A binary artificial bee colony algorithm and its performance assessment

Artificial bee colony algorithm, ABC for short, is a swarm-based optimization algorithm proposed for solving continuous optimization problems. Due to its simple but effective structure, some binary versions of the algorithm have been developed. In this study, we focus on modification of its xor-based binary version, called as binABC. The solution update rule of basic ABC is replaced with a xor logic gate in binABC algorithm, and binABC works on discretely-structured solution space. The rest of components in binABC are the same as with the basic ABC algorithm. In order to improve local search capability and convergence characteristics of binABC, a stigmergic behavior-based update rule for onlooker bees of binABC and extended version of xor-based update rule are proposed in the present study. The developed version of binABC is applied to solve a modern benchmark problem set (CEC2015). To validate the performance of proposed algorithm, a series of comparisons are conducted on this problem set. The proposed algorithm is first compared with the basic ABC and binABC on CEC2015 set. After its performance validation, six binary versions of ABC algorithm are considered for comparison of the algorithms, and a comprehensive comparison among the state-of-art variants of swarm intelligence or evolutionary computation algorithms is conducted on this set of functions. Finally, an uncapacitated facility location problem set, a pure binary optimization problem, is considered for the comparison of the proposed algorithm and binary variants of ABC algorithm. The experimental results and comparisons show that the proposed algorithm is successful and effective in solving binary optimization problems as its basic version in solving continuous optimization problems.

A Discrete Dynamic Artificial Bee Colony with Hyper-Scout for RESTful web service API test suite generation

Microservices have become quite important because they decompose an application into a collection of highly maintainable services and reduce the complexity of monolithic architecture. Although the most common method of microservice development is the REST architecture, the number of studies on the automatic testing of RESTful APIs are very limited. For automatic testing of RESTful APIs, different algorithms have been proposed. However, their performance depends on the type of problem being addressed. This study implements the basic Artificial Bee Colony (ABC) algorithm for automatic testing of RESTful web services, and addresses some shortcomings for the RESTful test generation. To overcome these shortcomings, we propose a Discrete Dynamic Artificial Bee Colony with Hyper-Scout (DABC-HS) algorithm for the service-oriented problem. We enhanced the exploration ability of the algorithm by integrating a hyper-scout unit. The presence of solutions that correspond to covered targets but are not related to the remaining targets causes overburdening of the search. In the proposed approach, the onlooker bee phase generates a pool of all solutions and their mutants and applies a dominance-based selection operator to select high-quality solutions related to uncovered targets. The experimental results demonstrate that the DABC-HS recorded the highest performance in four of the seven problems while demonstrating similarly good performance in other problems. MOSA, which is the powerful algorithm in the literature, was the best in two of seven problems while ABC algorithm achieved the best performance in one problem. Although the method could not achieve the best result in all problems, it is more successful than the other published methods in the literature.

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.

Optimal feature selection for SAR image classification using biogeography-based optimization (BBO), artificial bee colony (ABC) and support vector machine (SVM): a combined approach of optimization and machine learning

Land cover classification is one of the most important applications of POLSAR images. In this paper, a hybrid biogeography-based optimization support vector machine (HBBOSVM) has been introduced to classify POLSAR images of RADARSAT 2 in band C acquired from San Francisco, USA. The main purpose of this classification is to minimize the number of features and maximize classification accuracy. The proposed method consists of three main steps: preprocessing, feature selection and classification. As preprocessing, radiometric calibration, speckle reduction and feature extraction have been performed. In the proposed HBBO, the combination of onlooker bee of artificial bee colony (ABC) and migration operator of biogeography-based optimization has been applied in order to optimal feature selection. Then, SVM has been used to classify the pixels into specific labels of land-covers. The ground truth samples have been generated by google earth image, Pauli RGB image, high resolution image and national land cover database (NLCD 2006). The performance of HBBOSVM has been compared with BBOSVM, ABCSVM, particle swarm optimization support vector machine (PSOSVM) and the results of previous studies. In addition, the performance of HBBO is evaluated upon 20 well-known benchmark problems. According to the obtained results, the overall accuracy and average accuracy of HBBOSVM are 96.01% and 93.37% respectively which is the best result in comparison with other results. The HBBOSVM has better performance than other algorithms in terms of overall accuracy, kappa coefficient, average accuracy, convergence trend, and stability. In addition, the HBBO can be considered as a successful meta-heuristic for benchmark problems. This paper displays that the combined approach of optimization and machine learning methods provides powerful results.

A Novel Genetic Artificial Bee Inspired Neural Network Model for Drug Name Recognition

Drug names recognition (DNR) that seeks to discriminate drug mentions within the unstructured biomedical texts and categorize them into drug classes, is an essential task in mining medical information. The neural network structure considerably influences their overall performance. If there were lots of neurons within a network, it might fit the training dataset better, and the network might achieve few training errors, however it could be prone to over-fitting and the poor network generalization. Therefore, this paper proposes a novel network model for DNR based on a hybrid genetic artificial bee colony (GABC) algorithm. The algorithm enhances exploitation process of ABC and avoids the premature convergence problem by combining genetic operators into the ABC. These operators are hybridized in two phases of ABC: (1) exploitation process of onlooker bees phase, to enhance information sharing in between employed and onlooker bees which can find better solutions, and (2) scout bees phase to improve the replacement of any exhausted solution. The proposed network model for DNR was compared to some ether established models using different bio-inspired algorithms. The results reveal that the proposed model outperforms the other models in terms of predictive ability and computational time.

An Adaptive Multi-Strategy Artificial Bee Colony Algorithm for Integrated Process Planning and Scheduling

Traditionally, process planning and scheduling were performed sequentially, where scheduling depended on the result of process planning. Considering their complementarity, the two functions are more tightly integrated to improve the performance of job shop flexible manufacturing environment. This study proposes an adaptive multi-strategy artificial bee colony (AMSABC) algorithm to solve integrated process planning and scheduling (IPPS) problem. In AMSABC, two search strategies with different characteristics are introduced into employed bees and onlooker bees to take on the responsibility of both exploration and exploitation. The selection probability of each search strategy is dynamically adjusted according to previous experiences. To further improve the exploitation performance of the approach, a problem-specific multi-objective local search has been embedded in the proposed algorithm. Furthermore, AMSABC algorithm presents a unique solution representation where a food source is represented by three discrete vectors, and a well-designed decoding scheme is developed. Next, the corresponding neighborhood structure is adopted that it can directly generate feasible solutions in the search space. The proposed algorithm is tested on the well-known benchmark instances and compared with the state-of-the-art algorithms. Through detail analysis of experimental results, AMSABC algorithm is more beneficial in the quality and efficiency of solution.

Application Research for Multiobjective Low-Carbon Flexible Job-Shop Scheduling Problem Based on Hybrid Artificial Bee Colony Algorithm

A hybrid artificial bee colony (HABC) solved the multiobjective low-carbon flexible job-shop scheduling problem (MLFJSP) was proposed in the paper. HABC algorithm uses a two-layer coding method to establish the initial population as the nectar source for the employed bees. In the optimization process, the employed bee phase and the onlooker bee phase adopt improved crossover mutation strategies and adaptive neighborhood search strategies to generate new nectar sources, and the greedy method is used to retain better solutions. The scout bee update mechanism prevents the algorithm from falling into a local optimum and enhances the convergence of the algorithm. In order to prevent the loss of the optimal solution, the optimization results of each phase are saved in the Pareto archive (PA). Finally, two sets of international standard instances are used to carry out simulation experiments. The simulation results demonstrated that HABC algorithm is an effective algorithm to solve the multiobjective low-carbon flexible job shop scheduling problem.