Standard Artificial Bee Colony Algorithm Research Articles

Simulation of Multimedia Visual Image Motion Track Marking Based on Artificial Bee Colony Algorithm

With the rapid development of electronic technology, network technology, and multimedia processing technology, people pay more and more attention to the surrounding environment. Paying attention to and handling emergencies have also become one of the main points of attention. Through the combination of multimedia and monitoring system, the collected image data are processed and integratedly controlled by the computer platform, and an intelligent monitoring system for traffic road safety can be obtained. Due to the advantages of multimedia visual image motion trajectory identification technology in road traffic safety, it can effectively prevent the chaos and influence caused by humans in chaotic situations, making it widely used. By identifying the motion trajectories of different objects, road emergencies can be effectively prevented and countermeasures can be established. Based on this, this study proposes the artificial bee colony (ABC) algorithm to identify the motion trajectory in the visual image, but the standard ABC algorithm is too slow to converge in the later stage of trajectory recognition. Therefore, this study is based on the standard ABC algorithm. The trajectory recognition simulation shows that the optimized algorithm accelerates the convergence speed and the ability of the global optimal solution in the trajectory recognition process. Then, the optimized ABC algorithm is used to identify the multimedia visual image trajectory. The experiment shows that the average correct rate of feature extraction is also about 98%. The data identification results also show that the optimized ABC algorithm fits the real trajectory better in the trajectory identification. The running time of the algorithm is shorter than the running time of other comparison algorithms, which fully shows the accuracy and superiority of the algorithm in this study.

A new search scheme using multi‐bee‐colony elite learning method for unmanned aerial vehicles in unknown environments

AbstractIn this research, a cooperative search for multiple dynamic targets in an unknown marine environment by multiple unmanned aerial vehicles is studied based on a novel multi‐bee‐colony (MBC) elite learning algorithm. First, a specialized searching model is established which includes the UAV dynamics, the sensor model, the target probability and environmental certainty at different flight altitudes. Then, a new search strategy, which consists of rough search and accurate search, is proposed by maximizing the multiobjective utility function with the dynamic changing of the flight altitude. In order to solve the optimization problem, an improved MBC algorithm based on elite learning is designed, which can improve the adaptability and computation speed of the standard artificial bee colony (ABC) algorithm under different search missions. Finally, extensive simulations are conducted to show the effectiveness and superiority of the proposed search strategy.

Artificial bee colony algorithm based on a new local search approach for parameter estimation of photovoltaic systems

In this study, an ABC-Local Search (ABC-Ls) method was proposed by including a new local search procedure into the standard artificial bee colony (ABC) algorithm to perform the parameter estimation of photovoltaic systems (PV). The aim of the proposed ABC-Ls method was to improve the exploration capability of the standard ABC with a new local search procedure in addition to the exploitation and exploration balance of the standard ABC algorithm. The proposed ABC-Ls method was first tested on 15 well-known benchmark functions in the literature. In the results of the Friedman Mean Rank test used for statistical analysis, ABC-Ls method successfully ranked first with a value of 1.300 in benchmark functions. After obtaining successful results on the benchmark tests, the proposed ABC-Ls method was applied to the single diode, double diode and Photowatt-PWP-201 PV modules of PV systems for parameter estimations. In addition, the proposed ABC-Ls method has been applied to the KC200GT PV module for parameter estimation under different temperature and irradiance conditions of the PV modules. The success of ABC-Ls method was compared with genetic algorithm (GA), particle swarm optimization (PSO) algorithm, ABC algorithm, tree seed algorithm (TSA), Jaya, Atom search optimization (ASO). The comparison results were presented in tables and graphics in detail. The RMSE values for the parameter estimation of single diode, double diode and Photowatt-PWP-201 PV module of the proposed ABC-LS method were found as 9.8602E−04, 9.8257E−04 and 2.4251E−03, respectively. In this context, the proposed ABC-LS method has been compared with the literature for parameter estimation of single diode, double diode and Photowatt-PWP-201 PV module and it has been found that it provides a parameter estimation similar or better than other studies. The proposed ABC-Ls method for parameter estimation of the KC200GT PV module under different conditions is shown in convergence graphs and box plots, where it achieves more successful, effective and stable results than GA, PSO, ABC, TSA, Jaya and ASO algorithms.

Adaptive operator selection with reinforcement learning

Operator selection plays a crucial role in the efficiency of heuristic-based problem solving algorithms, especially, when a pool of operators is used to let algorithms dynamically select operators to produce new candidate solutions. A sequence of selected operators forms up throughout the search which impacts the success of the algorithms. Successive operators in a bespoke sequence can be complementary and therefore diversify the search while randomly selected operators are not expected to behave in this way. State of art adaptive selection schemes have been proposed to select the best next operator without considering the problem state in the process. In this study, a reinforcement learning algorithm is proposed to embed in a standard artificial bee colony algorithm for taking the problem state on board in operator selection process. The proposed approach implies mapping the problem states to the best fitting operators in the pool so as to achieve higher diversity and shape up an optimum operator sequence throughout the search process. The experimental study successfully demonstrates that the proposed idea works towards higher efficiency. The state of art approaches are outperformed with respect to the quality of solution in solving Set Union Knapsack problem over 30 benchmarking instances.

A Novel Breast Cancer Diagnosis Scheme With Intelligent Feature and Parameter Selections

Background and ObjectiveBreast cancer is the most commonly occurring cancer among women, which contributes to the global death rate. The key to increasing the survival rate of affected patients is early diagnosis along with appropriate treatments. Manual methods for breast cancer diagnosis fail due to human errors, inaccurate diagnoses, and are time-consuming when demands are high. Intelligent systems based on Artificial Neural Network (ANN) for automated breast cancer diagnosis are powerful due to their strong decision-making capabilities in complicated cases. Artificial Bee Colony, Artificial Immune System, and Bacterial Foraging Optimization are swarm intelligence algorithms that solve combinatorial optimization problems. This paper proposes two novel hybrid Artificial Bee Colony (ABC) optimization algorithms that overcome the demerits of standard ABC algorithms. First, this paper proposes a hybrid ABC approach called HABC, in which the standard ABC optimization is hybridized with a modified clonal selection algorithm of the Artificial Immune System that eliminates the poor exploration capabilities of standard ABC optimization. Further, this paper proposes a novel hybrid Artificial Bee Colony (Hybrid ABC) optimization where the strong explorative capabilities of the chemotaxis phase of the bacterial foraging optimization are integrated with a spiral model-based exploitative phase of the ABC by which the proposed Hybrid ABC overcomes the demerits of poor exploration and exploitation of the standard ABC algorithm. MethodsIn this work, the two proposed hybrid approaches were used in concurrent feature selection and parameter optimization of an ANN model. The proposed algorithm is implemented using various back-propagation algorithms, including resilient back-propagation (HABC-RP and Hybrid ABC-RP), Levenberg Marquart (HABC-LM and Hybrid ABC-LM), and momentum-based gradient descent (HABC-MGD and Hybrid ABC-GD) for parameter tuning of ANN. The Wisconsin breast cancer dataset was used to evaluate the performance of the proposed algorithms in terms of accuracy, complexity, and computational time. ResultsThe mean accuracy of the proposed HABC-RP was 99.14% and 99.54% for Hybrid ABC which is better than the results found in the existing literature. HABC-RP attained a sensitivity of 98.32%, a specificity of 99.63%, and a precision of 99.38% whereas Hybrid ABC attained sensitivity of 99.08% and Specificity of 99.81%. ConclusionsHABC-RP and Hybrid ABC-RP yielded high accuracy with a low complexity ANN structure compared to other variants. After evaluation, interestingly it is found that the Hybrid ABC-RP has achieved the highest mean accuracy of 99.54% with low complexity of 10.25 mean connections when compared to other variants proposed in this paper. It can be concluded that the concurrent selection of input features and tuning of parameters of ANN plays a vital role in increasing the accuracy of a breast cancer diagnosis. The proposed HABC-RP and Hybrid ABC-RP showed better results when compared to the existing breast cancer diagnosis systems taken for comparison. In the future, the proposed two-hybrid approaches can be used to generate optimal thresholds for the segmentation of tumors in abnormal images. HABC and Hybrid ABC can be used for tuning the parameters of various classifiers.

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.

Performance evaluation of artificial bee colony algorithm and its variants in the optimum design of steel skeletal structures

Artificial bee colony algorithm (ABC) has been efficiently used to obtain the optimum design of structures under design code limitations. There are several enhancements suggested to improve the performance of ABC even further in the literature. In this chapter, the performance of enhanced ABC variants in the optimum design of steel skeletal frames is studied. The mathematical model of such design problem when based on the provisions of design code requirements results in a discrete nonlinear programming problem. Seven variants of ABC is selected from the literature. These are standard artificial bee colony algorithm (sABC), artificial bee colony algorithm with orthogonal learning (OCABC), quick artificial bee colony algorithm (qABC), noel chaotic artificial bee colony algorithm (STOC-BC), directed artificial bee colony algorithm (dABC), improved artificial bee colony algorithm (NSABC) and artificial bee colony algorithm with Levy flight distribution (ABC_Levy). Seven optimum design algorithms are developed to obtain the solution of the design problem formulated considering design code provisions. Two steel space frames, one ten story and the other fifteen story are optimized using each of the seven optimum design techniques. The performance of each variant is observed and the optimum designs are compared. It is noticed after carrying out the statistical analysis that ABC_Levy and qABC algorithms outperform the other algorithms.

A new artificial bee colony algorithm employing intelligent forager forwarding strategies

Artificial Bee Colony (ABC) algorithm that mimics the intelligent foraging behaviors of real honey bees has been successfully applied to different types of optimization problems in recent years. Actually, the main reason lying behind the high preference of the ABC algorithm is related with its good performance on solving difficult optimization problems due to the effective search mechanisms existing in a single cycle and easily-implementable bee phases. However, with the purpose of increasing the implementation simplicity and generalizing the principal concept of the algorithm, some significant behaviors of the real foraging bees are not closely simulated and integrated into the workflow of the ABC or tried to be managed by employing simple randomized or conditional operations. In this study, in order to increase the performance of ABC algorithm, the complex behavior of the foraging employed bees related with how they decide to pass through to the dance area and how long they stay on there for informing onlookers is modeled in detail and then a new variant of ABC called intelligent forager forwarding ABC for short iff-ABC is proposed. For analyzing the possible contributions of the intelligent forager forwarding strategy on the performance of the ABC algorithm, thirteen classical benchmark problems and fifteen computationally expensive benchmark problems presented at the CEC 2015 were tested. The results obtained from the experimental studies were compared with the results of the different meta-heuristics in addition to the well-known variants of the standard ABC algorithm. From the experimental studies, it was concluded that the intelligent forager forwarding strategy significantly improves the quality of the final solutions and the convergence speed of the ABC algorithm.

A modified Artificial Bee Colony algorithm for structural damage identification under varying temperature based on a novel objective function

This paper presents a modified Artificial Bee Colony algorithm for structural damage identification. Meanwhile, the effect of temperature variation is considered and the change of temperature will lead to the alteration of Young's modulus of material. A novel objective function is proposed as the combinations of the partial mode shape curvature data, alterations of natural frequencies, and a sparse penalty term. Such an objective is found to be sensitive to structural damage while not sensitive to environmental effects. On the other hand, To render the standard Artificial Bee Colony algorithm more powerful and robustness, two local search strategies are introduced into the employed and onlooker bee phase of the Artificial Bee Colony algorithm, respectively. Two numerical examples and a laboratory verification are employed to verify the efficiency and advantage of the proposed algorithm. The final results show that the present algorithm could yield more satisfactory identification results compared with other state-of-the-art evolutionary algorithms, even high-level noise and temperature variation are considered; and the proposed novel objective function is more sensitive to structural damages, compared with the traditional mode-shape-based objective function.

Mission planning of multi base UAV Based on improved artificial bee colony algorithm

Aiming at the constraint that the take-off and landing position of Unmanned Aerial Vehicle (UAV) varies from time in maritime operations, a cooperative attack model of multi-base UAV is established. An improved artificial bee colony algorithm based on coding operation pool is proposed, which retains the basic idea of standard artificial bee colony algorithm, It draws lessons from RNA coding operation and introduces coding operation pool to ensure that the algorithm has better global search ability. The simulation results show that the cooperative attack model of multi-base and multi-UAV complies with the characteristics of modern maritime unmanned combat missions, and the proposed algorithm has strong optimization efficiency and optimization ability.

Inverse design of photonic structures using an artificial bee colony algorithm.

We adapted the standard artificial bee colony algorithm for a binary search space in order to optimize photonic structures. The artificial bee colony algorithm in conjunction with the finite element method is applied for maximizing photonic bandgaps of photonic crystals. The proposed approach is assessed by two case studies assuming 2D photonic crystals comprised of silicon and air in a triangular lattice. The crystals were optimized to present large photonic bandgaps. The artificial bee colony algorithm presented superior performance when compared with that of a standard genetic algorithm, demonstrating an interesting approach to be used on the inverse design or optimization of photonic structures.

A comparative study between artificial bee colony (ABC) algorithm and its variants on big data optimization

The big data term and its formal definition have changed the properties of some of the computational problems. One of the problems for which the fundamental properties change with the existence of the big data is the optimization problems. Artificial bee colony (ABC) algorithm inspired by the intelligent source search, consumption and communication characteristics of the real honey bees has proven its efficiency on solving different numerical and combinatorial optimization problems. In this study, the standard ABC algorithm and its well-known variants including the gbest-guided ABC algorithm, the differential evolution based ABC/best/1 and ABC/best/2 algorithms, crossover ABC algorithm, converge-onlookers ABC algorithm and quick ABC algorithm were assessed using the electroencephalographic signal decomposition based optimization problems introduced at the 2015 Congress on Evolutionary Computing Big Data Competition. The experimental studies on solving big data optimization problems showed that the phase-divided structure of the standard ABC algorithm still protects its advantageous sides when the candidate food sources or solutions are generated by referencing the global best solution in the onlooker bee phase.

A genetic Artificial Bee Colony algorithm for signal reconstruction based big data optimization

In recent years, the researchers have witnessed the changes or transformations driven by the existence of the big data on the definitions, complexities and future directions of the real world optimization problems. Analyzing the capabilities of the previously introduced techniques, determining possible drawbacks of them and developing new methods by taking into consideration of the unique properties related with the big data are nowadays in urgent demands. Artificial Bee Colony (ABC) algorithm inspired by the clever foraging behaviors of the real honey bees is one of the most successful swarm intelligence based optimization algorithms. In this study, a novel ABC algorithm based big data optimization technique was proposed. For exploring the solving abilities of the proposed technique, a set of experimental studies has been carried out by using different signal decomposition based big data optimization problems presented at the Congress on Evolutionary Computation (CEC) 2015 Big Data Optimization Competition. The results obtained from the experimental studies first were compared with the well-known variants of the standard ABC algorithm named gbest-guided ABC (GABC), ABC/best/1, ABC/best/2, crossover ABC (CABC), converge-onlookers ABC (COABC) and quick ABC (qABC). The results of the proposed ABC algorithm were also compared with the Differential Evolution (DE) algorithm, Genetic algorithm (GA), Firefly algorithm (FA), Fireworks algorithm (FW), Phase Base Optimization (PBO) algorithm, Particle Swarm Optimization (PSO) algorithm and Dragonfly algorithm (DA) based big data optimization techniques. From the experimental studies, it was understood that the newly introduced ABC algorithm based technique is capable of producing better or at least promising results compared to the mentioned big data optimization techniques for all of the benchmark instances.

An Energy Efficient Routing Protocol Based on Improved Artificial Bee Colony Algorithm for Wireless Sensor Networks

Clustering sensor nodes is an effective method in designing routing algorithms for Wireless Sensor Networks (WSNs), which improves network lifetime and energy efficiency. In clustered WSNs, cluster heads are the key nodes, they need to perform more tasks, so they consume more energy. Therefore, it is an important problem to select the optimal cluster heads. In this paper, we propose a clustering algorithm that selects cluster heads using an improved artificial bee colony (ABC) algorithm. Based on the standard ABC algorithm, an efficient improved ABC algorithm is proposed, and then the network cluster head energy, cluster head density, cluster head location and other similar factors are introduced into the improved ABC algorithm theory to solve the clustering problem in WSNs. In the network initialization period, all nodes have the same energy level, the improved ABC algorithm is used to optimize fuzzy C-means clustering to find the optimal clustering method. We also propose an energy-efficient routing algorithm based on an improved ant colony optimization for routing between the cluster heads and the base station. In order to improve energy efficiency and further improve network throughput, in the stable transmission phase, we introduce a polling control mechanism based on busy/idle nodes into intra-cluster communication. The performance of the proposed protocol is evaluated in several different scenarios. The simulation results show that the proposed protocol has a better performance compared to a number of recent similar protocols.

Parameter optimisation research for hydraulic turbine regulating system based on CGABC algorithm

Artificial bee colony (ABC) algorithm is regularly taken in the parameter optimisation of the hydraulic turbine regulating system (HTRS). The chaotic and global artificial bee colony (CGABC) algorithm is proposed to overcome the slow convergence speed and low convergence precision that existed in standard ABC algorithm. Among them, global optimal is used to enhance the global exploration ability, and chaos optimisation is adopted to increase the diversity of bee colony. The simulation results show that compared with fuzzy strategy, ZN, DE and ABC algorithm, the proposed CGABC algorithm significantly improves the dynamic transition process of HTRS. Simultaneously, the simulation experiment is carried out on HTRS with different governor parameters and controlled parameters when it is under the frequency and load disturbance condition. The results show that the dynamic characteristics and robustness is determined by the parameter selection, and the appropriate combination of parameters allows HTRS to achieve optimal dynamic performance and robustness.

Improved Artificial Bee Colony Algorithm and Its Application to Fundus Retinal Blood Vessel Image Binarization

The content of this work is based on the characteristics of standard artificial bee colony(ABC) algorithm with weak local search ability and slow convergence speed. Then, an improved algorithm named KD-ABC is proposed. For improving the diversity and quality of the solution, it changes the generation method of honey source. In the initialization phase, it uses the cluster center generated by the K-MEANS method as the initial honey source instead of the initialization in the standard method. For improving the local optimization ability and the convergence speed without reducing the global search, we proposed a dynamic neighborhood search mechanism based on the number of iterations in terms of ABC search strategy and neighborhood selection stage. In order to find a suitable threshold to divide the grayscale image into blood vessels and background parts, we applied the characteristics of the KD-ABC algorithm to the binary processing stage of the fundus retinal blood vessel image, which lays the foundation for future image recognition.

A Novel Artificial Bee Colony Algorithm for Structural Damage Detection

A novel artificial bee colony (ABC) algorithm to detect structural damage via modal and frequency analyses is proposed (named as TCABC algorithm). Compared to the standard ABC algorithm, tabu search method and chaotic search method are adopted in the proposed algorithm to enhance the exploration and exploitation ability. The tabu search method uses a memory function to avoid the solution being trapped in a local minimum, which increases the exploitation ability. Chaotic search method generates more searching points for finding the global minimum, which increases the exploration ability. Additionally, the first roulette wheel selection is replaced by the tournament selection to enhance the global searching ability of the TCABC algorithm. Several explicit test functions and an implicit damage detection function are employed to check the numerical results obtained from ABC and TCABC algorithms. Afterward, the damage detection accuracy of the TCABC algorithm is verified under different circumstances, and several recommendations are given for using the TCABC algorithm to detect structural damages under actual conditions. Finally, an experimental study is applied to examine the performance of TCABC algorithm for damage detection. The results show the following: (1) compared to traditional ABC algorithm, TCABC algorithm performs better; (2) fewer groups lead to faster convergence as demonstrated by both algorithms used in the same damage situation; (3) TCABC algorithm can infer the locations and extents of the damage when the groupings are inaccurate; (4) the accuracy of the field test data profoundly affects the precision of the damage detection results. In other words, stronger noises result in worse identification results; (5) whether or not the noises exist, the more data are measured, the more accurate the results can be achieved; (6) the TCABC algorithm can efficiently detect structural damage in the experimental study.

Time-based information sharing approach for employed foragers of artificial bee colony algorithm

Collective foraging and information sharing behaviors of honey bees have lead to emerge different swarm intelligence-based optimization techniques. Within these swarm intelligence-based optimization techniques, Artificial Bee Colony (ABC) algorithm has a special position due to its less control parameters, robust, phase-divided and easily implementable structures. Although standard workflow of ABC algorithm is capable of producing optimal or near optimal solutions for numerous problems, there are still some intelligent operations that are not directly modeled for the ABC algorithm in order to maintain the reduced complexity of the implementation and small number of control parameters. In this study, ABC algorithm is tried to be powered with a more realistic dancing approach called time-based information sharing, for short tb, model. The proposed model is integrated into the workflow of the standard ABC algorithm and its well-known variants. Experimental studies carried out on both classical and bound constrained single-objective CEC2015 benchmark functions showed that the proposed model in which the dancing durations of the employed bees are determined by the fitness values of the memorized sources significantly improved the performance of the standard and other variants of the ABC algorithm.

A hybrid algorithm of ABC variant and enhanced EGS local search technique for enhanced optimization performance

A hybrid algorithm for optimizing a complex power system-based problem, economic environmental dispatch (EED) is proposed. The algorithm hybridizes a recently proposed artificial bee colony (ABC) variant referred to as JA-ABC3 and a local search technique, evolutionary gradient search (EGS) which has been enhanced i.e., augmented. The enhanced EGS has been inserted into JA-ABC3’s framework and the resulting hybrid algorithm, known as EGSJAABC3 is expected to exhibit robust optimization performance by showing the capability to reach the global optimum in less number of generations. JA-ABC3 which is generated through few modifications towards the standard ABC algorithm is the best candidate as it has exhibited better performance than the standard ABC and other ABC variants. Since JA-ABC3 is a global search algorithm, a local search technique, EGS that has been augmented is selected to be its hybrid partner as it also exhibits better or same performance than its kind. The task of the augmented EGS is to enhance the exploitation capability of the algorithm and thus, guides the solution faster towards the global optimum. In other word, the enhanced EGS is taking part in the exploitation process while JA-ABC3 takes role in exploration and some parts of the exploitation processes. Then, a number of benchmark functions are used to evaluate the robustness of EGSJAABC3 in terms of convergence speed and global optimum achievement. Next, the main significant output of this research is a robust optimization algorithm (i.e., EGSJAABC3) later applied to solve complex real-world problems that is known for their uncertainty. In this paper, EGSJAABC3 is tested to minimize EED on three test generator systems; 6, 10 and 40 units. The acquired outcome on both benchmark functions and EED application demonstrate the robustness of EGSJAABC3 as an optimization algorithm and therefore, provide other researchers and engineers a tool for solving optimization problems.

Cooperative approach to artificial bee colony algorithm for optimal power flow

Recently, the artificial bee colony (ABC) algorithm has been developed to efficiently and effectively solve a wide range of optimization problems. In this work, the standard ABC algorithm is extended by incorporating a cooperation approach, and an algorithm called cooperative ABC (CABC) is proposed to solve the optimal power flow (OPF) problem. CABC aims at improving the performance of the standard ABC algorithm by using multiple artificial bee colonies to optimized different components of the solution vector cooperatively. With six well known benchmarks, CABC is proved to have significant better performance improvement on the standard ABC. CABC is then applied to the real-world OPF problem on an IEEE 30-bus test system. The simulation results showed that the proposed CABC outperforms other algorithms investigated in this paper in terms of optimization accuracy and computation robustness.