scholarly journals A Modified Artificial Bee Colony Algorithm Based on Search Space Division and Disruptive Selection Strategy

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Zhen-an He ◽  
Caiwen Ma ◽  
Xianhong Wang ◽  
Lei Li ◽  
Ying Wang ◽  
...  
Keyword(s):  
Artificial Bee Colony ◽  
Search Space ◽  
Poor Quality ◽  
Population Diversity ◽  
Disruptive Selection ◽  
Selection Strategy ◽  
Abc Algorithm ◽  
Space Division ◽  
Bee Colony

Artificial bee colony (ABC) algorithm has attracted much attention and has been applied to many scientific and engineering applications in recent years. However, there are still some insufficiencies in ABC algorithm such as poor quality of initial solution, slow convergence, premature, and low precision, which hamper the further development and application of ABC. In order to further improve the performance of ABC, we first proposed a novel initialization method called search space division (SSD), which provided high quality of initial solutions. And then, a disruptive selection strategy was used to improve population diversity. Moreover, in order to accelerate convergence rate, we changed the definition of the scout bee phase. In addition, we designed two types of experiments to testify our proposed algorithm. On the one hand, we conducted experiments to make sure how much each modification makes contribution to improving the performance of ABC. On the other hand, comprehensive experiments were performed to prove the superiority of our proposed algorithm. The experimental results indicate that SDABC significantly outperforms other ABCs, contributing to higher solution accuracy, faster convergence speed, and stronger algorithm stability.

scholarly journals An Artificial Bee Colony Algorithm with Random Location Updating

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Lijun Sun ◽  
Tianfei Chen ◽  
Qiuwen Zhang
Keyword(s):  
Information Exchange ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Population Diversity ◽  
Selection Strategy ◽  
Initial Population ◽  
Search Range ◽  
Bee Colony ◽  
Random Location ◽  
Location Updating

As a novel swarm intelligence algorithm, artificial bee colony (ABC) algorithm inspired by individual division of labor and information exchange during the process of honey collection has advantage of simple structure, less control parameters, and excellent performance characteristics and can be applied to neural network, parameter optimization, and so on. In order to further improve the exploration ability of ABC, an artificial bee colony algorithm with random location updating (RABC) is proposed in this paper, and the modified search equation takes a random location in swarm as a search center, which can expand the search range of new solution. In addition, the chaos is used to initialize the swarm population, and diversity of initial population is improved. Then, the tournament selection strategy is adopted to maintain the population diversity in the evolutionary process. Through the simulation experiment on a suite of unconstrained benchmark functions, the results show that the proposed algorithm not only has stronger exploration ability but also has better effect on convergence speed and optimization precision, and it can keep good robustness and validity with the increase of dimension.

scholarly journals Research on WNN Modeling for Gold Price Forecasting Based on Improved Artificial Bee Colony Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Bai Li
Keyword(s):  
Neural Network ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Gold Price ◽  
Selection Strategy ◽  
Price Forecasting ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Previous Cycle ◽  
Subsequent Cycle

Gold price forecasting has been a hot issue in economics recently. In this work, wavelet neural network (WNN) combined with a novel artificial bee colony (ABC) algorithm is proposed for this gold price forecasting issue. In this improved algorithm, the conventional roulette selection strategy is discarded. Besides, the convergence statuses in a previous cycle of iteration are fully utilized as feedback messages to manipulate the searching intensity in a subsequent cycle. Experimental results confirm that this new algorithm converges faster than the conventional ABC when tested on some classical benchmark functions and is effective to improve modeling capacity of WNN regarding the gold price forecasting scheme.

Diversity Conserved Chaotic Artificial Bee Colony Algorithm based Brightness Preserved Histogram Equalization and Contrast Stretching Method

2015 ◽  
Vol 5 (4) ◽  
pp. 45-73 ◽  
Author(s):  
Krishna Gopal Dhal ◽  
Sanjoy Das
Keyword(s):  
Artificial Bee Colony ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Population Diversity ◽  
Objective Functions ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Optimal Values ◽  
Occurrence Matrix ◽  
Contrast Stretching

This study is organized into two parts. The first part introduces two image enhancement methods with the ability to preserve the original brightness of the image. These two methods are: optimal ranged brightness preserved contrast stretching (ORBPCS) method and weighted thresholded histogram equalization (WTHE) method. The efficiency of these two methods crucially depends on the method's associated parameters. To find the optimal values of the parameters Artificial Bee Colony (ABC) algorithm and a novel objective function have been employed in this study. The second part of this study mainly concentrates on the efficiency increment of ABC algorithm and to develop the proper objective functions to preserve the original brightness of the image. Some new mechanisms like population diversity measurement technique, use of chaotic sequence etc. are also introduced to enhance the efficiency of traditional ABC algorithm. The objective functions have been developed by using co-occurrence matrix and peak-signal to noise ratio (PSNR).

A Memetic Artificial Bee Colony Algorithm for High Dimensional Problems

2020 ◽  
Vol 19 (01) ◽  
pp. 2050008
Author(s):  
Dongli Jia ◽  
Teng Li ◽  
Yufei Zhang ◽  
Haijiang Wang
Keyword(s):  
Local Search ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Search Space ◽  
Small Region ◽  
High Dimensional ◽  
Large Space ◽  
Bee Colony ◽  
Local Search Strategy

This work proposed a memetic version of Artificial Bee Colony algorithm, or called LSABC, which employed a “shrinking” local search strategy. By gradually shrinking the local search space along with the optimization process, the proposed LSABC algorithm randomly explores a large space in the early run time. This helps to avoid premature convergence. Then in the later evolution process, the LSABC finely exploits a small region around the current best solution to achieve a more accurate output value. The optimization behavior of the LSABC algorithm was studied and analyzed in the work. Compared with the classic ABC and several other state-of-the-art optimization algorithms, the LSABC shows a better performance in terms of convergence rate and quality of results for high-dimensional problems.

Hybrid Artificial Bee Colony Algorithm with Differential Evolution and Free Search for Numerical Function Optimization

2016 ◽  
Vol 25 (04) ◽  
pp. 1650020 ◽  
Author(s):  
Lian Lian ◽  
Fu Zaifeng ◽  
Yang Guangfei ◽  
Huang Yi
Keyword(s):  
Differential Evolution ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Robustness Analysis ◽  
Selection Strategy ◽  
Local Optimum ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Free Search

Artificial bee colony (ABC) algorithm invented by Karaboga has been proved to be an efficient technique compared with other biological-inspired algorithms for solving numerical optimization problems. Unfortunately, convergence speed of ABC is slow when working with certain optimization problems and some complex multimodal problems. Aiming at the shortcomings, a hybrid artificial bee colony algorithm is proposed in this paper. In the hybrid ABC, an improved search operator learned from Differential Evolution (DE) is applied to enhance search process, and a not-so-good solutions selection strategy inspired by free search algorithm (FS) is introduced to avoid local optimum. Especially, a reverse selection strategy is also employed to do improvement in onlooker bee phase. In addition, chaotic systems based on the tent map are executed in population initialization and scout bee's phase. The proposed algorithm is conducted on a set of 40 optimization test functions with different mathematical characteristics. The numerical results of the data analysis, statistical analysis, robustness analysis and the comparisons with other state-of-the-art-algorithms demonstrate that the proposed hybrid ABC algorithm provides excellent convergence and global search ability.

scholarly journals An ABC Algorithm with Recombination

2018 ◽  
Vol 13 (4) ◽  
pp. 590-601 ◽  
Author(s):  
Xuemei You ◽  
Yinghong Ma ◽  
Zhiyuan Liu ◽  
Mingzhao Xie
Keyword(s):  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Search Model ◽  
Abc Model ◽  
Hybrid Search ◽  
Swarm Optimization ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Swarm Intelligence Algorithm

Artificial bee colony (ABC) is an efficient swarm intelligence algorithm, which has shown good exploration ability. However, its exploitation capacity needs to be improved. In this paper, a novel ABC variant with recombination (called RABC) is proposed to enhance the exploitation. RABC firstly employs a new search model inspired by the updating equation of particle swarm optimization (PSO). Then, both the new search model and the original ABC model are recombined to build a hybrid search model. The effectiveness of the proposed RABC is validated on ten famous benchmark optimization problems. Experimental results show RABC can significantly improve the quality of solutions and accelerate the convergence speed.

scholarly journals A New Emigrant Creation Strategy with Randomized Sources for Parallel Artificial Bee Colony Algorithm

2018 ◽  
pp. 81-86
Author(s):  
Selcuk Aslan ◽  
Dervis Karaboga ◽  
Alperen Aksoy
Keyword(s):  
Artificial Bee Colony ◽  
Parallel Implementation ◽  
Experimental Studies ◽  
Population Diversity ◽  
Computational Power ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Convergence Performance ◽  
Neighborhood Topology ◽  
Current Systems

Dividing the whole population into subpopulations or subcolonies then evaluating them simultaneously is one of the most commonly used parallelization approaches to utilize the computational power of the current systems. However, this type of parallelization strategy decreases the population diversity because of the division of the entire population and needs migrations between subpopulations to maintain the solution diversity until the end of the iterations. In this study, we proposed a new emigrant creation strategy in which the parameters of the best food source being migrated to the neighbor subpopulation is modified with the more appropriate parameters of the randomly determined solution or solutions and investigated its effect on the performance of the parallel Artificial Bee Colony (ABC) algorithm. Experimental studies showed that newly proposed emigrant creation strategy based on randomized solutions significantly improved the convergence performance and solution qualities of parallel ABC algorithm compared to the its standard serial and ring neighborhood topology based parallel implementation for which the best solutions are directly used as emigrants.

scholarly journals A novel hybrid approach based artificial bee colony and salp swarm algorithms for solving ORPD problem

2021 ◽  
Vol 23 (3) ◽  
pp. 1825
Author(s):  
Souheil Salhi ◽  
Djemai Naimi ◽  
Ahmed Salhi ◽  
Saleh Abujarad ◽  
Abdelouahab Necira
Keyword(s):  
Artificial Bee Colony ◽  
Reactive Power ◽  
Electrical Power ◽  
Hybrid Approach ◽  
Stable State ◽  
Search Space ◽  
Electrical Power System ◽  
Solution Quality ◽  
Abc Algorithm ◽  
Bee Colony

Optimal reactive power dispatch (ORPD) is an important task for achieving more economical, secure and stable state of the electrical power system. It is expressed as a complex optimization problem where many meta-heuristic techniques have been proposed to overcome various complexities in solving ORPD problem. A meta-heuristic search mechanism is characterized by exploration and exploitation of the search space. The balance between these two characteristics is a challenging problem to attain the best solution quality. The artificial bee colony (ABC) algorithm as a reputed meta-heuristic has proved its goodness at exploration and weakness at exploitation where the enhancement of the basic ABC version becomes necessary. Salp swarm algorithm (SSA) is a newly developed swarm-based meta-heuristic, which has the best local search capability by using the best global solution in each iteration to discover promising solutions. In this paper, a novel hybrid approach-based ABC and SSA algorithms (ABC-SSA) is that developed to enhance the exploitation capability of the ABC algorithm using SSA and applied for solving ORPD problem. The efficiency of ABC-SSA is investigated using two standard test systems IEEE-30 and IEEE-300 buses, and that by considering the famous objective functions in ORPD problem.

Artificial bee colony algorithm with dynamic multi-population

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740087 ◽  
Author(s):  
Ming Zhang ◽  
Zhicheng Ji ◽  
Yan Wang
Keyword(s):  
Convergence Rate ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Search Space ◽  
Experimental Results ◽  
Search Process ◽  
Competitive Performance ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Fcm Clustering

To improve the convergence rate and make a balance between the global search and local turning abilities, this paper proposes a decentralized form of artificial bee colony (ABC) algorithm with dynamic multi-populations by means of fuzzy C-means (FCM) clustering. Each subpopulation periodically enlarges with the same size during the search process, and the overlapping individuals among different subareas work for delivering information acting as exploring the search space with diffusion of solutions. Moreover, a Gaussian-based search equation with redefined local attractor is proposed to further accelerate the diffusion of the best solution and guide the search towards potential areas. Experimental results on a set of benchmarks demonstrate the competitive performance of our proposed approach.

A quasi-affine transformation artificial bee colony algorithm for global optimization

2021 ◽  
pp. 1-18
Author(s):  
Baohua Zhao ◽  
Tien-Wen Sung ◽  
Xin Zhang
Keyword(s):  
Affine Transformation ◽  
Artificial Bee Colony ◽  
Triangular Matrix ◽  
Convergence Speed ◽  
Test Set ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Search Mechanism ◽  
Multiple Dimensions ◽  
Collaborative Search

The artificial bee colony (ABC) algorithm is one of the classical bioinspired swarm-based intelligence algorithms that has strong search ability, because of its special search mechanism, but its development ability is slightly insufficient and its convergence speed is slow. In view of its weak development ability and slow convergence speed, this paper proposes the QABC algorithm in which a new search equation is based on the idea of quasi-affine transformation, which greatly improves the cooperative ability between particles and enhances its exploitability. During the process of location updating, the convergence speed is accelerated by updating multiple dimensions instead of one dimension. Finally, in the overall search framework, a collaborative search matrix is introduced to update the position of particles. The collaborative search matrix is transformed from the lower triangular matrix, which not only ensures the randomness of the search, but also ensures its balance and integrity. To evaluate the performance of the QABC algorithm, CEC2013 test set and CEC2014 test set are used in the experiment. After comparing with the conventional ABC algorithm and some famous ABC variants, QABC algorithm is proved to be superior in efficiency, development ability, and robustness.

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