Co-evolving bee colonies by forager migration: A multi-swarm based Artificial Bee Colony algorithm for global search space

2014 ◽  
Vol 232 ◽  
pp. 216-234 ◽  
Author(s):  
Subhodip Biswas ◽  
Swagatam Das ◽  
Shantanab Debchoudhury ◽  
Souvik Kundu
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Search Space ◽  
Global Search ◽  
Bee Colony ◽  
Bee Colonies

scholarly journals Artificial Bee Colony Algorithm with Directed Scout

2021 ◽  
Author(s):  
Radhwan A.A. Saleh ◽  
Rüştü Akay
Keyword(s):  
Design Problem ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Real Life ◽  
Search Space ◽  
Bee Colony ◽  
Life Problems ◽  
Tension And Compression ◽  
Teaching Learning

Abstract As a relatively new model, the Artificial Bee Colony Algorithm (ABC) has shown impressive success in solving optimization problems. Nevertheless, its efficiency is still not satisfactory for some complex optimization problems. This paper has modified ABC and its other recent variants to improve its performance by modify the scout phase. This modification enhances its exploitation ability by intensifying the regions in the search space, which probably includes reasonable solutions. The experiments were performed on the CEC2014 benchmark suite, CEC2015 benchmark functions, and three real-life problems: pressure vessel design problem, tension and compression spring design problem, and Frequency-Modulated (FM) problem. And the proposed modification was applied to basic ABC, Gbest-Guided ABC, Depth First Search ABC, and Teaching Learning Based ABC, and they were compared with their modified counterparts. The results have shown that our modification can successfully increase the performance of the original versions. Moreover, the proposed modified algorithm was compared with the state-of-the-art optimization algorithms, and it produced competitive results.

An Improved Method of Artificial Bee Colony Algorithm

2011 ◽  
Vol 101-102 ◽  
pp. 315-319 ◽  
Author(s):  
Xin Jie Wu ◽  
Duo Hao ◽  
Chao Xu
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Simulation Experiment ◽  
Optimization Problems ◽  
Local Extremum ◽  
Global Search ◽  
Improved Method ◽  
Object Function ◽  
Bee Colony ◽  
Better Than

The basic artificial bee colony algorithm gets local extremum easily and converges slowly in optimization problems of the multi-object function. In order to enhance the global search ability of basic artificial bee colony algorithm, an improved method of artificial bee colony algorithm is proposed in this paper. The basic idea of this method is as follows: On the basis of traditional artificial bee colony algorithm, the solution vectors that found by each bee colony are recombined after each iteration, then the solution vectors of combinations are evaluated again, thus the best result is found in this iteration. In this way the possibility of sticking at local extremum is reduced. Finally the simulation experiment has been finished. The simulation experiment results have shown that the method proposed in this paper is feasible and effective, it is better than basic artificial bee colony algorithm in the global search ability.

scholarly journals A Hybrid Artificial Bee Colony Algorithm for the Service Selection Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Changsheng Zhang ◽  
Bin Zhang
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Service Selection ◽  
Search Space ◽  
Selection Problem ◽  
Foraging Strategies ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Related Service ◽  
Bee Colony Optimization

To tackle the QoS-based service selection problem, a hybrid artificial bee colony algorithm calledh-ABC is proposed, which incorporates the ant colony optimization mechanism into the artificial bee colony optimization process. In this algorithm, a skyline query process is used to filter the candidates related to each service class, which can greatly shrink the search space in case of not losing good candidates, and a flexible self-adaptive varying construct graph is designed to model the search space based on a clustering process. Then, based on this construct graph, different foraging strategies are designed for different groups of bees in the swarm. Finally, this approach is evaluated experimentally using different standard real datasets and synthetically generated datasets and compared with some recently proposed related service selection algorithms. It reveals very encouraging results in terms of the quality of solutions.

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.

Artificial Bee Colony Algorithm for Solving Educational Timetabling Problems

2012 ◽  
Vol 3 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Asaju La’aro Bolaji ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Mohammed A. Awadallah
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Solution Method ◽  
Search Space ◽  
Initial Population ◽  
Timetabling Problem ◽  
Bee Colony ◽  
Educational Timetabling ◽  
Problem Instances ◽  
Uncapacitated Examination Timetabling Problem

This paper presents an artificial bee colony algorithm (ABC) for Education Timetabling Problem (ETP). It is aimed at developing a good-quality solution for the problem. The initial population of solutions was generated using Saturation Degree (SD) and Backtracking Algorithm (BA) to ensure the feasibility of the solutions. At the improvement stage in the solution method, ABC uses neighbourhood structures iteratively within the employed and onlooker bee operators, in order to rigorously navigate the UTP search space. The technique was evaluated using curriculum-based course timetabling (CB-CTT) and Uncapacitated Examination Timetabling Problem (UETP) problem instances. The experimental results on UETP showed that the technique is comparable with other state-of-the-art techniques and provides encouraging results on CB-CTT.

Extensive Particle Swarm Artificial Bee Colony Algorithm for Function Optimization

2014 ◽  
Vol 496-500 ◽  
pp. 1808-1811
Author(s):  
Zhen Yuan ◽  
Ya Zhou ◽  
Wei Lan Zhong ◽  
Li Zhou
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Particle Swarm ◽  
Global Search ◽  
Function Optimization ◽  
Swarm Optimization ◽  
Bee Colony ◽  
On Line ◽  
Search Capability ◽  
Modified Algorithm

An extensive particle swarm artificial bee colony algorithm is proposed, which integrates the global best solution into the solution search equation of artificial bee colony to improve the exploitation. The memory weight and neighborhood dynamic step are introduced to keep the balance between the global search and local search, and to improve the search accuracy. Particle swarm optimization is embedded into the modified algorithm for on-line parameter optimizing. The simulations have shown that the new algorithm outperforms the ABC algorithm on search accuracy, convergence rate and global search capability. It has been found many applications in optimization of manufacturing and design process.

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.

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.

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