Research on Cellular Artificial Bee Colony Algorithm and its Computational Experiments

2013 ◽  
Vol 284-287 ◽  
pp. 3168-3172
Author(s):  
Ren Bin Xiao ◽  
Ying Cong Wang
Keyword(s):  
Population Structure ◽  
Cellular Automata ◽  
Selection Pressure ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Exploration And Exploitation ◽  
Fixed Position ◽  
Bee Colony ◽  
Search Mechanism ◽  
The Relationship

It is the research hotspot for evolutionary algorithms to solve the contradiction between exploration and exploitation. Cellular artificial bee colony (CABC) algorithm is proposed by combining cellular automata with artificial bee colony algorithm from the perspective of the neighborhood in this paper. Each bee in the population structure defined in CABC has a fixed position and can only interact with bees in its neighborhood. The overlap between neighborhoods of different bees may make a bee an employed bee in one neighborhood and an onlooker bee in another neighborhood and vice versa, which increases the diversity of the population. The neighborhood and evolutionary rule help to control the selection pressure effectively, and the improved search mechanism in artificial bee colony algorithm is proposed to enhance the local search ability. The experimental results tested on four benchmark functions show that CABC can further balance the relationship between exploration and exploitation when compared with three ABC-based algorithms.

Improved Artificial Bee Colony Algorithm and its Application in Classification

2018 ◽  
Vol 30 (6) ◽  
pp. 921-926
Author(s):  
Haiquan Wang ◽  
Jianhua Wei ◽  
Shengjun Wen ◽  
Hongnian Yu ◽  
Xiguang Zhang ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Initial Solution ◽  
Solution Set ◽  
Exploration And Exploitation ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Parameters Selection ◽  
Optimal Feature

For improving the classification accuracy of the classifier, a novel classification methodology based on artificial bee colony algorithm is proposed for optimal feature and SVM parameters selection. In order to balance the ability of exploration and exploitation of traditional ABC algorithm, improvements are introduced for the generation of initial solution set and onlooker bee stage. The proposed algorithm is applied to four datasets with different attribute characteristics from UCI and efficiency of the algorithm is proved from the results.

scholarly journals Hybrid Artificial Bee Colony Algorithm and Particle Swarm Search for Global Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Wang Chun-Feng ◽  
Liu Kui ◽  
Shen Pei-Ping
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Particle Swarm ◽  
Population Based ◽  
Initial Population ◽  
Bee Colony ◽  
Search Mechanism ◽  
Point Set ◽  
Searching Ability ◽  
Good Point Set

Artificial bee colony (ABC) algorithm is one of the most recent swarm intelligence based algorithms, which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this problem, we propose a novel artificial bee colony algorithm based on particle swarm search mechanism. In this algorithm, for improving the convergence speed, the initial population is generated by using good point set theory rather than random selection firstly. Secondly, in order to enhance the exploitation ability, the employed bee, onlookers, and scouts utilize the mechanism of PSO to search new candidate solutions. Finally, for further improving the searching ability, the chaotic search operator is adopted in the best solution of the current iteration. Our algorithm is tested on some well-known benchmark functions and compared with other algorithms. Results show that our algorithm has good performance.

scholarly journals A Modified Artificial Bee Colony Algorithm forp-Center Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Alkın Yurtkuran ◽  
Erdal Emel
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
State Of The Art ◽  
Benchmark Problems ◽  
Center Problem ◽  
Exploration And Exploitation ◽  
Abc Algorithm ◽  
Coding Schemes ◽  
Bee Colony ◽  
Bee Colonies

The objective of thep-center problem is to locatep-centers on a network such that the maximum of the distances from each node to its nearest center is minimized. The artificial bee colony algorithm is a swarm-based meta-heuristic algorithm that mimics the foraging behavior of honey bee colonies. This study proposes a modified ABC algorithm that benefits from a variety of search strategies to balance exploration and exploitation. Moreover, random key-based coding schemes are used to solve thep-center problem effectively. The proposed algorithm is compared to state-of-the-art techniques using different benchmark problems, and computational results reveal that the proposed approach is very efficient.

scholarly journals A Multistrategy Optimization Improved Artificial Bee Colony Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Wen Liu
Keyword(s):  
Convergence Rate ◽  
Learning Strategies ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Population Diversity ◽  
Local Optimum ◽  
Simulation Experiments ◽  
Bee Colony ◽  
Search Mechanism ◽  
Improved Algorithm

Being prone to the shortcomings of premature and slow convergence rate of artificial bee colony algorithm, an improved algorithm was proposed. Chaotic reverse learning strategies were used to initialize swarm in order to improve the global search ability of the algorithm and keep the diversity of the algorithm; the similarity degree of individuals of the population was used to characterize the diversity of population; population diversity measure was set as an indicator to dynamically and adaptively adjust the nectar position; the premature and local convergence were avoided effectively; dual population search mechanism was introduced to the search stage of algorithm; the parallel search of dual population considerably improved the convergence rate. Through simulation experiments of 10 standard testing functions and compared with other algorithms, the results showed that the improved algorithm had faster convergence rate and the capacity of jumping out of local optimum faster.

scholarly journals Improving Artificial Bee Colony Algorithm Using a Dynamic Reduction Strategy for Dimension Perturbation

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Gan Yu ◽  
Hongzhi Zhou ◽  
Hui Wang
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Food Source ◽  
Artificial Bee Colony ◽  
Convergence Speed ◽  
One Dimension ◽  
Exploration And Exploitation ◽  
Abc Algorithm ◽  
Reduction Strategy ◽  
One Dimensional ◽  
Bee Colony

To accelerate the convergence speed of Artificial Bee Colony (ABC) algorithm, this paper proposes a Dynamic Reduction (DR) strategy for dimension perturbation. In the standard ABC, a new solution (food source) is obtained by modifying one dimension of its parent solution. Based on one-dimensional perturbation, both new solutions and their parent solutions have high similarities. This will easily cause slow convergence speed. In our DR strategy, the number of dimension perturbations is assigned a large value at the initial search stage. More dimension perturbations can result in larger differences between offspring and their parent solutions. With the growth of iterations, the number of dimension perturbations dynamically decreases. Less dimension perturbations can reduce the dissimilarities between offspring and their parent solutions. Based on the DR, it can achieve a balance between exploration and exploitation by dynamically changing the number of dimension perturbations. To validate the proposed DR strategy, we embed it into the standard ABC and three well-known ABC variants. Experimental study shows that the proposed DR strategy can efficiently accelerate the convergence and improve the accuracy of solutions.

scholarly journals A Novel Artificial Bee Colony Algorithm for Function Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Song Zhang ◽  
Sanyang Liu
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Function Optimization ◽  
Experimental Results ◽  
Knapsack Problems ◽  
Search Process ◽  
Challenging Problem ◽  
Exploration And Exploitation ◽  
Bee Colony

It is known that both exploration and exploitation are important in the search equations of ABC algorithms. How to well balance the two abilities in the search process is still a challenging problem in ABC algorithms. In this paper, we propose a novel artificial bee algorithm named as “NABC,” by incorporating the information of the global best solution into the solution search equation of the onlookers stage to improve the exploitation. At the same time, we improve the search equation of the employed bees to keep the exploration. The experimental results of NABC tested on a set of 11 numerical benchmark functions show good performance and fast convergence in solving function optimization problems, compared with variant ABC, DE, and PSO algorithms. The application of NABC on solving five standard knapsack problems shows its effectiveness and practicability.

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