scholarly journals Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Tinggui Chen ◽  
Renbin Xiao
Keyword(s):  
Honey Bees ◽  
Artificial Bee Colony ◽  
Local Optimum ◽  
Immune Network ◽  
Artificial Immune ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Searching Strategy ◽  
Artificial Immune Network ◽  
Self Adaptive

Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments.

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 Handling Optimization Problem, and the Scope of Varied Artificial Bee Colony (ABC) Algorithms: A Contemporary

2019 ◽  
Vol 8 (6S4) ◽  
pp. 607-611
Keyword(s):  
Honey Bees ◽  
Optimization Problem ◽  
Artificial Bee Colony ◽  
Research Community ◽  
Food Sources ◽  
Search Process ◽  
Abc Algorithm ◽  
Success Stories ◽  
Bee Colony ◽  
Successful Search

One of the most successful search algorithms of the last decade is Artificial Bee Colony (ABC) algorithm. It was first coined by Dervis Karaboga, 2005. Since then a group of variants of the algorithm have been anticipated to find solutions for the problems of optimization. The motivation for the algorithm is the search process of honey bees for food sources. The present paper aimed to bring out the evolutionary developments of the algorithm that cover numerous versions of the algorithm with the strategic changes to meet the optimization needs of the adopted problem contexts. This survey clearly reviewed the basic types, advancements, application areas, and the relevance of the ABC algorithm addressing various problem contexts. The efforts made by the research community since the last two decades along with the success stories are discussed in detail. The attachment of the optimization process of ABC with data mining is dealt in particular. Finally the opportunities and the scope of the application of the algorithm in large areas of problem domains are highlighted.

scholarly journals A hybrid artificial bee colony algorithm for numerical function optimization

2015 ◽  
Vol 26 (10) ◽  
pp. 1550109 ◽  
Author(s):  
Zakaria N. Alqattan ◽  
Rosni Abdullah
Keyword(s):  
Artificial Bee Colony ◽  
Search Algorithm ◽  
Function Optimization ◽  
Search Process ◽  
Local Optimum ◽  
Particle Movement ◽  
Numerical Function ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Optimization Search

Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms; it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).

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.

scholarly journals A review on Artificial Bee Colony algorithm

2013 ◽  
Vol 2 (3) ◽  
pp. 175 ◽  
Author(s):  
Balwant Kumar Verma ◽  
Dharmender Kumar
Keyword(s):  
Honey Bees ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Travelling Salesman Problem ◽  
Control Parameters ◽  
Travelling Salesman ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Large Numbers ◽  
Short Span

In recent years large number of algorithms based on the swarm intelligence has been proposed by various researchers. The Artificial Bee Colony (ABC) algorithm is one of most popular stochastic, swarm based algorithm proposed by Karaboga in 2005 inspired from the foraging behavior of honey bees. In short span of time, ABC algorithm has gain wide popularity among researchers due to its simplicity, easy to implementation and fewer control parameters. Large numbers of problems have been solved using ABC algorithm such as travelling salesman problem, clustering, routing, scheduling etc. the aim of this paper is to provide up to date enlightenment in the field of ABC algorithm and its applications.

scholarly journals Hybridization of Artificial Bee Colony Algorithm and its Variants with Hyperbolic Spiral based Local Search

2019 ◽  
Vol 9 (2) ◽  
pp. 3547-3551
Keyword(s):  
Local Search ◽  
Honey Bees ◽  
Efficient Algorithm ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Search Strategies ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Swarming Behavior

Artificial bee colony (ABC) algorithm is grounded on intelligent swarming behavior of honey bees. It is one of the efficient algorithm for optimization. The ABC algorithm is good in exploration and sometimes fails to exploit properly. Local search strategies in addition to existing steps play important role to improve exploitation. In order to improve exploitation here a local search inspired by the nature of hyperbolic spiral introduced in ABC. The purposed variant used with ABC, Best-so-far ABC and Gbest ABC. Outcomes proved that hybrid of these algorithms with hyperbolic search gives good results with higher accuracy and reliability.

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 Artificial Bee Colony Algorithm Combined with Grenade Explosion Method and Cauchy Operator for Global Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Jian-Guo Zheng ◽  
Chao-Qun Zhang ◽  
Yong-Quan Zhou
Keyword(s):  
Global Optimization ◽  
Honey Bees ◽  
Artificial Bee Colony ◽  
Global Optimum ◽  
Local Optimum ◽  
Bee Colony ◽  
Explosion Method ◽  
Accelerate Convergence ◽  
Cauchy Operator ◽  
Intelligence Technique

Artificial bee colony (ABC) algorithm is a popular swarm intelligence technique inspired by the intelligent foraging behavior of honey bees. However, ABC is good at exploration but poor at exploitation and its convergence speed is also an issue in some cases. To improve the performance of ABC, a novel ABC combined with grenade explosion method (GEM) and Cauchy operator, namely, ABCGC, is proposed. GEM is embedded in the onlooker bees’ phase to enhance the exploitation ability and accelerate convergence of ABCGC; meanwhile, Cauchy operator is introduced into the scout bees’ phase to help ABCGC escape from local optimum and further enhance its exploration ability. Two sets of well-known benchmark functions are used to validate the better performance of ABCGC. The experiments confirm that ABCGC is significantly superior to ABC and other competitors; particularly it converges to the global optimum faster in most cases. These results suggest that ABCGC usually achieves a good balance between exploitation and exploration and can effectively serve as an alternative for global optimization.

scholarly journals Self-Adaptive Artificial Bee Colony for Function Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Mingzhu Tang ◽  
Wen Long ◽  
Huawei Wu ◽  
Kang Zhang ◽  
Yuri A. W. Shardt
Keyword(s):  
Local Search ◽  
Artificial Bee Colony ◽  
Optimization Method ◽  
Population Based ◽  
Function Optimization ◽  
Initial Population ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Global And Local ◽  
Self Adaptive

Artificial bee colony (ABC) is a novel population-based optimization method, having the advantage of less control parameters, being easy to implement, and having strong global optimization ability. However, ABC algorithm has some shortcomings concerning its position-updated equation, which is skilled in global search and bad at local search. In order to coordinate the ability of global and local search, we first propose a self-adaptive ABC algorithm (denoted as SABC) in which an improved position-updated equation is used to guide the search of new candidate individuals. In addition, good-point-set approach is introduced to produce the initial population and scout bees. The proposed SABC is tested on 12 well-known problems. The simulation results demonstrate that the proposed SABC algorithm has better search ability with other several ABC variants.

Sign in / Sign up

Export Citation Format

Share Document