混合田口─混沌方法、免疫演算法及人工蜂群演算法之研究與其應用

The artificial bee colony (ABC) algorithm is an important branch of evolutionary algorithms and has been shown to be competitive with other algorithms for solving optimisation problems. However, there are always some insufficiencies in ABC algorithm such as lower convergence speed and easily get trapped in the local optima. To increase depth search capabilities of onlooker bees and ensure scout bees do not discard the current optimal solution, we proposed a modified ABC algorithm (denoted as ORABC) based on the optimisation strategy and retained strategy of the best individual in this paper. Compared with ABC algorithm, ABC* (added retained strategy to ABC) and ORABC algorithm, experiments are conducted on a set of numerical benchmark functions. The numerical simulation results demonstrate that ORABC algorithm improves the convergence characteristics of ABC algorithm and provides very remarkable performance in solving complex numerical optimisation problems compared to original algorithm.

The artificial bee colony (ABC) algorithm is a biological‐inspired optimisation algorithm proposed by Karaboga. Since its solution search equation is good at exploration but poor at exploitation, the ABC algorithm converges slowly and is easy to fall into local optimum. Inspired by opposition‐based learning (OBL), the authors propose an improved ABC algorithm called opposition‐based learning ABC (OLABC). In OLABC, firstly, the population would be initialised using OBL. Secondly, to ensure the diversity of the population during the iterative process, the solution search equation is employed to bee phase would be improved. Generate the opposite solution when the fitness value of the newly generated solution is smaller than the current solution, and then apply the greedy selection strategy to update the solution. Thirdly, the adaptive weight strategy is used to dynamically adjust the weight, balancing the global exploration and local exploitation capabilities of the algorithm. Experiments on a set of benchmark functions show that OLABC has better convergence speed and optimisation precision than the compared algorithms.

Artificial Bee Colony (ABC) algorithm inspired by the complex search and foraging behaviors of real honey bees is one of the most promising implementations of the Swarm Intelligence- (SI-) based optimization algorithms. Due to its robust and phase-divided structure, the ABC algorithm has been successfully applied to different types of optimization problems. However, some assumptions that are made with the purpose of reducing implementation difficulties about the sophisticated behaviours of employed, onlooker, and scout bees still require changes with the more literal procedures. In this study, the ABC algorithm and its well-known variants are powered by adding a new control mechanism in which the decision-making process of the employed bees managing transitions to the dance area is modeled. Experimental studies with different types of problems and analysis about the parallelization showed that the newly proposed approach significantly improved the qualities of the final solutions and convergence characteristics compared to the standard implementations of the ABC algorithms.

Swarm intelligence is briefly defined as the collective behavior of decentralized and self-organized swarms. Self-organization and labor division are the two key components of swarm intelligence. Artificial Bee Colony (ABC) algorithm is one of the most recent swarm intelligence-based algorithms. The behavior of bees in ABC algorithm satisfies the self-organization features, but there is no specific labor division mechanism in ABC algorithm. In this work, we propose an improved ABC algorithm called labor division artificial bee colony (LDABC) algorithm by incorporating the labor division mechanism into ABC algorithm, which is achieved by individual specialization and role plasticity. We specify three different search methods for employed bees, onlooker bees and scout bees to realize individual specialization, these search methods are related to food source quality, enable bees to maximize exploitation of food source. Role plasticity is achieved by combining with cellular automata, where the roles of bees are not static but vary with their surrounding environment, enable bees not to limit to one search method. The different search modes and the flexibility of the search behaviors make our algorithm achieve a better balance between exploration and exploitation. The experimental results tested on 13 benchmark functions and CEC-2013 test functions demonstrate a competitive performance.

Archive-based multi-criteria Artificial Bee Colony algorithm for whole test suite generation

An induction motor is the most commonly used motor in industry today. Motor circuit parameters are essential for designing, evaluating performance, and controlling the applications of the motor. However, it is difficult to measure the electric parameters, e.g., resistance and reactance, of induction motors accurately. Therefore, researchers have noted the parameter estimation of induction motors as an essential optimization problem. The artificial bee colony (ABC) algorithm is an efficient element of bioinspired optimization algorithms and has been successfully applied in numerous engineering applications. However, the ABC algorithm suffers from slow convergence and poor exploitation. Additionally, there are bleak chances of getting a fitter food source for scout bees using the the standard ABC algorithm scheme. Therefore, different solutions have already been proposed to avoid the flaws of the ABC algorithm. Nevertheless, the proposed solutions are either computationally intensive or prone to local optima traps or they require additional control variables to tune. Moreover, there is no systematic way to tune the additional control variables for yielding the optimal performance of the algorithms. Therefore, this research work proposes a novel variant of the ABC algorithm, which capitalizes on multiple global-best food sources rather than a single global-best food source. In addition, this research work proposes a novel scheme for enhancing the performance of the ABC algorithm's scout bee. Two modifications for the performance enhancement of the ABC algorithm are proposed in this research work. The proposed algorithm is compared with various recently proposed variants of the ABC algorithm and various other available methods for estimating induction motor parameters. The performance of the proposed algorithm is also assessed using the chaotic map initialization technique. The results prove that the proposed algorithm is able to achieve the best convergence among all of the compared algorithms.

Artificial Bee Colony (ABC) algorithm is a novel bio-inspired swarm intelligence approach which is competitive with other population-based algorithms and has the advantage of using fewer control parameters. However, basic ABC is easy to be prematurely convergent and be trapped into local optimum. In the later iteration, algorithm has low convergent speed and population diversity seriously decreases. In this paper, Gaussian mutation and chaos disturbance are introduced into ABC to overcome the shortcomings above. Applications of improved ABC algorithm on four benchmark optimization functions show marked improvement in performance over the basic ABC.

As a relatively new paradigm of evolutionary algorithms, artificial bee colony (ABC) algorithm has shown attractive performance in solving optimization problems. However, for some complex optimization problems, its performance is still not satisfactory. To address this concerning issue, in this paper, we designed an improved ABC variant, called DLABC, based on the division of labor theory. In the DLABC, both of the employed bee phase and the onlooker bee phase are modified by introducing new solution search equations. For the modified employed bee phase, two new control parameters are introduced in the new solution search equation, which aims to control the frequency of perturbation and the magnitude of perturbation, respectively. For the modified onlooker bee phase, a novel depth-first search framework is used which tends to allocate more computing resources to elite solutions for accelerating convergence rate. By combing these two modified phases, we attempt to balance the exploration and exploitation capabilities of ABC. Numerical experiments are conducted on the CEC 2019 100-digit challenge benchmark suite, and the DLABC is compared with the basic ABC and a recently well-established ABC variant (DFSABC_elite). The total scores of these included algorithms have shown that our proposed DLABC algorithm performed best.

ABSTRACTOne of the complex and time‐consuming stages of land consolidation, where both parcel geometry in rural lands and the ownership structure are changed, is land reallocation. Land reallocation consists of land redistribution and land partitioning stages. Land redistribution aims at careful location of the parcels whose ownership statuses have been changed into land blocks constructed through taking many parameters into account. The focus of this study is the optimization of the land redistribution stage. The performance of many optimization algorithms such as genetic algorithms (GA), Artificial Bee Colony (ABC) Algorithm, and Migrating Birds Optimization (MBO) Algorithm has been investigated for the solution of the land redistribution problem. In this study, we apply the Clonal Selection Algorithm (CSA) to the land redistribution problem for the first time, offering a novel approach to improving allocation efficiency. CSA has been applied to the land redistribution problem, which considers various redistribution parameters. The study area covers 1551.1 ha; there are 351 cadastral parcels and 143 blocks where the parcel areas will be redistributed. So as to test the validity of the application results, the outcomes of CSA were compared with those results of Genetic Algorithms (GA), a biological‐based optimization method previously used to solve the problem. According to the results obtained for GA, the best fitness value was achieved (523.4672), and the placement rate based on the most successful preferences was (67.41294). According to the results obtained for CSA, the best fitness value was achieved (296.4551), and the placement rate based on the most successful preferences was (66.54229). The primary goal of this process was to find the lowest fitness values. In addition, the best suitability solutions found with GA and CSA were compared according to the block fullness rates. The deviations in block occupancy rates obtained by GA are larger than the deviations obtained by CSA.

An improved feature selection approach using global best guided Gaussian artificial bee colony for EMG classification

INTRODUCTION: The artificial bee colony (ABC) algorithm is a nature-inspired technique used for solving different optimization problems. This paper presents a multi-hive ABC algorithm for solving constrained benchmark functions of CEC2006. The CEC2006 data set contains the global benchmark functions with different design variables, number and type of constraints. OBJECTIVES: The objective of the proposed work is to design and apply the GPGPU based multi-hive ABC algorithm to solve constrained optimization problems. METHODS: The proposed approach is a multi-population coarse-grained system implemented using General Purpose Graphics Processing Unit (GPGPU). The performance of the proposed approach is compared with the serial ABC algorithm for eleven benchmark functions and results in the literature. The multi-hive ABC algorithm has multiple hives, each running separate ABC algorithm on different cores of GPGPU. RESULTS: The proposed approach provides global best solutions in significantly reduced time for all benchmark functions. The speed-up obtained is approximately 7X to 9X. The GPGPU device utilization is approximately 57% to 91%. CONCLUSION: The GPGPU based multi-hive ABC algorithm is a found good with respect to best results, speed up factor and GPU utilization to solve constrained optimization problems.

Based on the coupled vertical-torsional free vibration records of the spring-suspended section model of bridge decks, an improved Artificial Bee Colony (ABC) algorithm is proposed to identify the flutter derivatives. The ABC algorithm is a novel biological-inspired optimization algorithm, comparing with other iteration methods, the ABC algorithm can facilitate the identification process with no need for initial value. The standard ABC algorithm is good at exploration but poor at exploitation. To enhance the exploitation of the ABC algorithm, Powell’s method is used as a local search tool and a nonlinear factor is introduced for convergence control. Search equations applied to generate candidate solutions in the onlookers and scout bees phase are modified to improve the search ability of this method. The objective function in the optimization is weighted residual sum of squares between measured records and predicted values, where the weighting factors are optimized by an iteration process. The modified ABC algorithm is tested on several benchmark functions compared with the standard ABC algorithm. Simulation results show that the proposed algorithm has a higher convergence speed, better solution quality and stronger robustness. In order to investigate the effectiveness of the modified ABC algorithm in the flutter derivatives identification, numerical simulations of an ideal thin-plate model are carried out. The identification results show that the modified ABC algorithm for flutter derivatives identification of bridge decks is robust and reliable.

Artificial Bee Colony (ABC) is one of the latest and emerging swarm intelligence algorithms. Though, there are some areas where ABC works better than other optimization techniques but, the drawbacks like stucking at local optima and preferring exploration at the cost of exploitation, are also associated with it. This paper uses position update equation in ABC as in Gbest-guided ABC (GABC) and attempts to improve ABC algorithm by balancing its exploration and exploitation capabilities. The proposed algorithm is named as Expedited Artificial Bee Colony (EABC). We altered the onlooker bee phase of ABC by forcing the individual bee to take positive direction towards the random bee if this selected random bee has better fitness than the current bee and if it is not the case then the current bee will move in reverse direction. In this way, ABC colony members will not follow only global best bee but also a random bee which has better fitness than the current bee which is going to be modified. So the mentioned drawbacks of the ABC may be resolved. To analyze the performance of the proposed modification, 14 unbiased benchmark optimization functions have been considered and experimental results reflect its superiority over the Basic ABC and GABC.

PurposeThe purpose of this paper is to use an ABC algorithm to improve the thrust–torque ratio of a rotating-wing unmanned aerial vehicle (UAV) model.Design/methodology/approachThe design of UAVs, such as aircraft, drones, helicopters, has become one of the popular engineering areas with the development of technology. This study aims to improve the value of thrust–torque ratio of an unmanned helicopter. For this purpose, an unmanned helicopter was built at the Faculty of Aeronautics and Astronautics, Erciyes University. The maximum thrust–torque ratio was calculated considering the blade length, blade chord width, blade mass density and blade twist angle. For calculation, artificial bee colony (ABC) algorithm was used. By using ABC algorithm, the maximum thrust–torque ratio was obtained against the optimum input values. For this purpose, a model with four inputs and a single output is formed. In the generated system model, optimum thrust–torque ratio was calculated by changing the input values used in the ±5% range. As a result of this study, approximately 31% improvement was achieved. According to these results, the proposed approach will provide convenience to the designers in the design of the rotating-wing UAV.FindingsAccording to these results, approximately 31% improvement was achieved, and the proposed approach will provide convenience to the designers in the design of the rotating-wing UAV.Research limitations/implicationsIt takes a long time to obtain the optimum thrust–torque ratio value through the ABC algorithm method.Practical implicationsUsing ABC algorithm provides to improve the value of thrust–torque ratio of an unmanned helicopter. With this algorithm, unmanned helicopter flies more than ever. Thus, the presented method based on the ABC algorithm is more efficient.Social implicationsThe application of the ABC algorithm method can be used effectively to calculate the thrust–torque ratio in UAV.Originality/valueProviding an original and penetrating a method that saves time and reduces the cost to improve the value of thrust–torque ratio of an unmanned helicopter.

Gbest-guided artificial bee colony algorithm for numerical function optimization