scholarly journals A Transition Control Mechanism for Artificial Bee Colony (ABC) Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-24 ◽  
Author(s):  
Selcuk Aslan
Keyword(s):  
Honey Bees ◽  
Artificial Bee Colony ◽  
Control Mechanism ◽  
Optimization Problems ◽  
Experimental Studies ◽  
Decision Making Process ◽  
Abc Algorithm ◽  
Transition Control ◽  
Bee Colony ◽  
Different Types

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.

An Artificial Bee Colony-Guided Approach for Electro-Encephalography Signal Decomposition-Based Big Data Optimization

2020 ◽  
Vol 19 (02) ◽  
pp. 561-600
Author(s):  
Selcuk Aslan
Keyword(s):  
Big Data ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Experimental Studies ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Abc Algorithm ◽  
Bee Colony ◽  
De Algorithm ◽  
Data Optimization

The digital age has added a new term to the literature of information and computer sciences called as the big data in recent years. Because of the individual properties of the newly introduced term, the definitions of the data-intensive problems including optimization problems have been substantially changed and investigations about the solving capabilities of the existing techniques and then developing their specialized variants for big data optimizations have become important research topic. Artificial Bee Colony (ABC) algorithm inspired by the clever foraging characteristics of the real honey bees is one of the most successful swarm intelligence-based metaheuristics. In this study, a new ABC algorithm-based technique that is named source-linked ABC (slinkABC) was proposed by considering the properties of the optimization problems related with the big data. The slinkABC algorithm was tested on the big data optimization problems presented at the Congress on Evolutionary Computation (CEC) 2015 Big Data Optimization Competition. The results obtained from the experimental studies were compared with the different variants of the ABC algorithm including gbest-guided ABC (GABC), ABC/best/1, ABC/best/2, crossover ABC (CABC), converge-onlookers ABC (COABC), quick ABC (qABC) and modified gbest-guided ABC (MGABC) algorithms. In addition to these, the results of the proposed ABC algorithm were also compared with the results of the Differential Evolution (DE) algorithm, Genetic algorithm (GA), Firefly algorithm (FA), Phase-Based Optimization (PBO) algorithm and Particle Swarm Optimization (PSO) algorithm-based approaches. From the experimental studies, it was understood that the ABC algorithm modified by considering the unique properties of the big data optimization problems as in the slinkABC produces better solutions for most of the tested instances compared to the mentioned optimization techniques.

Characteristic Analysis of Artificial Bee Colony Algorithm with Network-Structure

2017 ◽  
Vol 21 (3) ◽  
pp. 496-506
Author(s):  
Shunta Imamura ◽  
◽  
Toshiya Kaihara ◽  
Nobutada Fujii ◽  
Daisuke Kokuryo ◽  
...  
Keyword(s):  
Network Structure ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Computer Experiments ◽  
High Dimensional ◽  
Characteristic Analysis ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Different Types ◽  
Efficient Exploration

The artificial bee colony (ABC) algorithm, which is inspired by the foraging behavior of honey bees, is one of the swarm intelligence systems. This algorithm can provide an efficient exploration of the optimal solutions using three different types of agents for optimization problems with multimodal functions. However, the performance of the conventional ABC algorithm decreases for high-dimensional problems. In this study, we propose an improved algorithm using the network structure of agents to enhance the ability for global search. The efficacy of the proposed algorithm is evaluated by performing computer experiments with high-dimensional benchmark functions.

A State-of-the-Art Review of Artificial Bee Colony in the Optimization of Single and Multiple Criteria

2013 ◽  
Vol 4 (4) ◽  
pp. 23-45 ◽  
Author(s):  
B. S. P. Mishra ◽  
S. Dehuri ◽  
G.-N. Wang
Keyword(s):  
Honey Bees ◽  
Artificial Bee Colony ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
State Of The Art ◽  
Exact Algorithm ◽  
Multi Objective Optimization ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Time And Space Complexity

Nowadays computers are used to solve a variety and multitude of complex problems facing in every sphere of peoples’ life. However, many of the problems are intractable in nature exact algorithm might need centuries to manage with formidable challenges. In such cases heuristic or in a broader sense meta-heuristic algorithms that find an approximate solution but have acceptable time and space complexity play indispensable role. In this article, the authors present a state-of-the-art review on meta-heuristic algorithm popularly known as artificial bee colony (ABC) inspired by honey bees. Moreover, the ABC algorithm for solving single and multi-objective optimization problems have been studied. A few potential application areas of ABC are highlighted as an end note of this article.

scholarly journals A Shuffle-Based Artificial Bee Colony Algorithm for Solving Integer Programming and Minimax Problems

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1211
Author(s):  
Ivona Brajević
Keyword(s):  
Integer Programming ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Minimax Problems ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Effective Performance ◽  
Complex Optimization ◽  
Overall Performance ◽  
Integer Programming Problems

The artificial bee colony (ABC) algorithm is a prominent swarm intelligence technique due to its simple structure and effective performance. However, the ABC algorithm has a slow convergence rate when it is used to solve complex optimization problems since its solution search equation is more of an exploration than exploitation operator. This paper presents an improved ABC algorithm for solving integer programming and minimax problems. The proposed approach employs a modified ABC search operator, which exploits the useful information of the current best solution in the onlooker phase with the intention of improving its exploitation tendency. Furthermore, the shuffle mutation operator is applied to the created solutions in both bee phases to help the search achieve a better balance between the global exploration and local exploitation abilities and to provide a valuable convergence speed. The experimental results, obtained by testing on seven integer programming problems and ten minimax problems, show that the overall performance of the proposed approach is superior to the ABC. Additionally, it obtains competitive results compared with other state-of-the-art algorithms.

ARTIFICIAL BEE COLONY ALGORITHM WITH IMPROVED EXPLORATIONS: A NOVEL APPROACH FOR NUMERICAL OPTIMIZATION

2014 ◽  
Vol 13 (02) ◽  
pp. 1450010
Author(s):  
MD. SHAFIUL ALAM ◽  
MD. MONIRUL ISLAM ◽  
KAZUYUKI MURASE
Keyword(s):  
Swarm Intelligence ◽  
Numerical Optimization ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Adaptive Strategy ◽  
Local Optima ◽  
Selection Scheme ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Novel Approach

The Artificial Bee Colony (ABC) algorithm is a recently introduced swarm intelligence algorithm that has been successfully applied on numerous and diverse optimization problems. However, one major problem with ABC is its premature convergence to local optima, which often originates from its insufficient degree of explorative search capability. This paper introduces ABC with Improved Explorations (ABC-IX), a novel algorithm that modifies both the selection and perturbation operations of the basic ABC algorithm in an explorative way. First, an explorative selection scheme based on simulated annealing allows ABC-IX to probabilistically accept both better and worse candidate solutions, whereas the basic ABC can accept better solutions only. Second, a self-adaptive strategy enables ABC-IX to automatically adapt the perturbation rate, separately for each candidate solution, to customize the degree of explorations and exploitations around it. ABC-IX is evaluated on several benchmark numerical optimization problems and results are compared with a number of state-of-the-art evolutionary and swarm intelligence algorithms. Results show that ABC-IX often performs better optimization than most other algorithms in comparison on most of the problems.

scholarly journals Artificial Bee Colony Algorithm with Time-Varying Strategy

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Quande Qin ◽  
Shi Cheng ◽  
Qingyu Zhang ◽  
Li Li ◽  
Yuhui Shi
Keyword(s):  
Swarm Intelligence ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Experimental Studies ◽  
Population Based ◽  
Time Varying ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Linear And Nonlinear ◽  
Proper Balance

Artificial bee colony (ABC) is one of the newest additions to the class of swarm intelligence. ABC algorithm has been shown to be competitive with some other population-based algorithms. However, there is still an insufficiency that ABC is good at exploration but poor at exploitation. To make a proper balance between these two conflictive factors, this paper proposed a novel ABC variant with a time-varying strategy where the ratio between the number of employed bees and the number of onlooker bees varies with time. The linear and nonlinear time-varying strategies can be incorporated into the basic ABC algorithm, yielding ABC-LTVS and ABC-NTVS algorithms, respectively. The effects of the added parameters in the two new ABC algorithms are also studied through solving some representative benchmark functions. The proposed ABC algorithm is a simple and easy modification to the structure of the basic ABC algorithm. Moreover, the proposed approach is general and can be incorporated in other ABC variants. A set of 21 benchmark functions in 30 and 50 dimensions are utilized in the experimental studies. The experimental results show the effectiveness of the proposed time-varying strategy.

scholarly journals A Novel Optimization Algorithm Combing Gbest-Guided Artificial Bee Colony Algorithm with Variable Gradients

2020 ◽  
Vol 10 (10) ◽  
pp. 3352
Author(s):  
Xiaodong Ruan ◽  
Jiaming Wang ◽  
Xu Zhang ◽  
Weiting Liu ◽  
Xin Fu
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Numerical Test ◽  
Stochastic Algorithm ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Novel Algorithm

The artificial bee colony (ABC) algorithm, which has been widely studied for years, is a stochastic algorithm for solving global optimization problems. Taking advantage of the information of a global best solution, the Gbest-guided artificial bee colony (GABC) algorithm goes further by modifying the solution search equation. However, the coefficient in its equation is based only on a numerical test and is not suitable for all problems. Therefore, we propose a novel algorithm named the Gbest-guided ABC algorithm with gradient information (GABCG) to make up for its weakness. Without coefficient factors, a new solution search equation based on variable gradients is established. Besides, the gradients are also applied to differentiate the priority of different variables and enhance the judgment of abandoned solutions. Extensive experiments are conducted on a set of benchmark functions with the GABCG algorithm. The results demonstrate that the GABCG algorithm is more effective than the traditional ABC algorithm and the GABC algorithm, especially in the latter stages of the evolution.

Differential Operators Embedded Artificial Bee Colony Algorithm

2013 ◽  
pp. 149-163
Author(s):  
Tarun Kumar Sharma ◽  
Millie Pant
Keyword(s):  
Global Optimization ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Differential Operators ◽  
Optimization Problems ◽  
Numerical Results ◽  
Benchmark Problems ◽  
Continuous Space ◽  
Abc Algorithm ◽  
Bee Colony

Artificial Bee Colony (ABC) is one of the most recent nature inspired (NIA) algorithms based on swarming metaphor. Proposed by Karaboga in 2005, ABC has proven to be a robust and efficient algorithm for solving global optimization problems over continuous space. However, it has been observed that the structure of ABC is such that it supports exploration more in comparison to exploitation. In order to maintain a balance between these two antagonist factors, this paper suggests incorporation of differential evolution (DE) operators in the structure of basic ABC algorithm. The proposed algorithm called DE-ABC is validated on a set of 10 benchmark problems and the numerical results are compared with basic DE and basic ABC algorithm. The numerical results indicate that the presence of DE operators help in a significant improvement in the performance of ABC algorithm.

scholarly journals The Artificial Bee Colony (ABC) Algorithm for Estimating Parameter of Epidemic Influenza Model

2020 ◽  
Vol 10 (1) ◽  
pp. 29-36
Author(s):  
Ririn Nirmalasari ◽  
◽  
Agus Suryanto ◽  
Syaiful Anam
Keyword(s):  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Error Function ◽  
Dynamical Behavior ◽  
Graphical Analysis ◽  
Unknown Parameters ◽  
Abc Algorithm ◽  
Bee Colony ◽  
The Mean ◽  
Best Parameter

The Artificial Bee Colony (ABC) is one of the stochastic algorithms that can be applied to solve many real-world optimization problems. In this paper, The ABC algorithm was used to estimate the parameter of the epidemic influenza model. This model consists of a differential system represented by variations of Susceptible (S), Exposed (E), Recovered (R), and Infected (I). The ABC processes explore the minimum value of the mean square error function in the current iteration to estimate the unknown parameters of the model. Estimating parameters were made using participation data containing influenza disease in Australia, 2017. The best parameter chosen from the ABC process matched the dynamical behavior of the influenza epidemic field data used. Graphical analysis was used to validate the model. The result shows that the ABC algorithm is efficient for estimating the parameter of the epidemic influenza model.

Logistic step size based artificial bee colony algorithm for optimum induction motor speed control

2019 ◽  
Vol 12 ◽  
Author(s):  
Fani Bhushan Sharma ◽  
Shashi Raj Kapoor
Keyword(s):  
Induction Motor ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Pi Controller ◽  
Motor Speed ◽  
Step Size ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Pi Controllers ◽  
Selection Of

: The disruption in the parameters of induction motor (IM) are quite frequent. In case of substantial parameter disruption the IM becomes unreliable. To deal with this complication of parameter disruption the proportional integral (PI) controllers are utilized. The selection of PI controller parameters is process dependent and any inappropriate selection of controller setting may diverge to instability. In recent years swarm intelligence (SI) based algorithms are executing well to solve real-world optimization problems. Now a days, the tuning of PI controller parameters for IM is executed using a significant SI algorithm namely, artificial bee colony (ABC) algorithm. Further, the ABC algorithm is amended based on inspiration from mathematical logistic formula. The propound algorithm is titled as logistic ABC algorithm and is applied for PI controller tuning of IM. The outcomes reveals that the propound algorithm performs better as compared to other state- of-art strategies available in the literature.

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