scholarly journals A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm

2010 ◽  
Vol 2010 ◽  
pp. 1-16 ◽  
Author(s):  
Wenping Zou ◽  
Yunlong Zhu ◽  
Hanning Chen ◽  
Xin Sui
Keyword(s):  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Data Sets ◽  
Data Mining Technique ◽  
Bee Colony ◽  
Clustering Approach ◽  
Complex Optimization ◽  
Comparative Results ◽  
Clustering Problems ◽  
Work First

Artificial Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO), and its cooperative version (CPSO) are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed.

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.

scholarly journals An Improved Artificial Bee Colony Algorithm Based on Elite Strategy and Dimension Learning

Mathematics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 289
Author(s):  
Songyi Xiao ◽  
Wenjun Wang ◽  
Hui Wang ◽  
Dekun Tan ◽  
Yun Wang ◽  
...  
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Optimization Method ◽  
Computational Results ◽  
Bee Colony ◽  
Elite Strategy ◽  
Large Jump ◽  
Complex Optimization

Artificial bee colony is a powerful optimization method, which has strong search abilities to solve many optimization problems. However, some studies proved that ABC has poor exploitation abilities in complex optimization problems. To overcome this issue, an improved ABC variant based on elite strategy and dimension learning (called ABC-ESDL) is proposed in this paper. The elite strategy selects better solutions to accelerate the search of ABC. The dimension learning uses the differences between two random dimensions to generate a large jump. In the experiments, a classical benchmark set and the 2013 IEEE Congress on Evolutionary (CEC 2013) benchmark set are tested. Computational results show the proposed ABC-ESDL achieves more accurate solutions than ABC and five other improved ABC variants.

scholarly journals Constraint Consensus Based Artificial Bee Colony Algorithm for Constrained Optimization Problems

2019 ◽  
Vol 2019 ◽  
pp. 1-24 ◽  
Author(s):  
Liling Sun ◽  
Yuhan Wu ◽  
Xiaodan Liang ◽  
Maowei He ◽  
Hanning Chen
Keyword(s):  
Constrained Optimization ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Benchmark Problems ◽  
Constrained Optimization Problems ◽  
Constrained Problems ◽  
Bee Colony ◽  
Hybrid Heuristic Algorithm ◽  
Complex Optimization ◽  
Constraint Consensus

Over the last few decades, evolutionary algorithms (EAs) have been widely adopted to solve complex optimization problems. However, EAs are powerless to challenge the constrained optimization problems (COPs) because they do not directly act to reduce constraint violations of constrained problems. In this paper, the robustly global optimization advantage of artificial bee colony (ABC) algorithm and the stably minor calculation characteristic of constraint consensus (CC) strategy for COPs are integrated into a novel hybrid heuristic algorithm, named ABCCC. CC strategy is fairly effective to rapidly reduce the constraint violations during the evolutionary search process. The performance of the proposed ABCCC is verified by a set of constrained benchmark problems comparing with two state-of-the-art CC-based EAs, including particle swarm optimization based on CC (PSOCC) and differential evolution based on CC (DECC). Experimental results demonstrate the promising performance of the proposed algorithm, in terms of both optimization quality and convergence speed.

scholarly journals Particle Swarm and Bacterial Foraging Inspired Hybrid Artificial Bee Colony Algorithm for Numerical Function Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Li Mao ◽  
Yu Mao ◽  
Changxi Zhou ◽  
Chaofeng Li ◽  
Xiao Wei ◽  
...  
Keyword(s):  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Function Optimization ◽  
Local Optimum ◽  
Search Range ◽  
Bacterial Foraging Optimization ◽  
Numerical Function ◽  
Bacterial Foraging ◽  
Bee Colony

Artificial bee colony (ABC) algorithm has good performance in discovering the optimal solutions to difficult optimization problems, but it has weak local search ability and easily plunges into local optimum. In this paper, we introduce the chemotactic behavior of Bacterial Foraging Optimization into employed bees and adopt the principle of moving the particles toward the best solutions in the particle swarm optimization to improve the global search ability of onlooker bees and gain a hybrid artificial bee colony (HABC) algorithm. To obtain a global optimal solution efficiently, we make HABC algorithm converge rapidly in the early stages of the search process, and the search range contracts dynamically during the late stages. Our experimental results on 16 benchmark functions of CEC 2014 show that HABC achieves significant improvement at accuracy and convergence rate, compared with the standard ABC, best-so-far ABC, directed ABC, Gaussian ABC, improved ABC, and memetic ABC algorithms.

Stellar Mass Black Hole for Engineering Optimization

2017 ◽  
pp. 62-90
Author(s):  
Premalatha Kandhasamy ◽  
Balamurugan R ◽  
Kannimuthu S
Keyword(s):  
Black Hole ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Optimization Method ◽  
Stellar Mass ◽  
Bee Colony ◽  
Nature Inspired Algorithms ◽  
The Universe

In recent years, nature-inspired algorithms have been popular due to the fact that many real-world optimization problems are increasingly large, complex and dynamic. By reasons of the size and complexity of the problems, it is necessary to develop an optimization method whose efficiency is measured by finding the near optimal solution within a reasonable amount of time. A black hole is an object that has enough masses in a small enough volume that its gravitational force is strong enough to prevent light or anything else from escaping. Stellar mass Black hole Optimization (SBO) is a novel optimization algorithm inspired from the property of the gravity's relentless pull of black holes which are presented in the Universe. In this paper SBO algorithm is tested on benchmark optimization test functions and compared with the Cuckoo Search, Particle Swarm Optimization and Artificial Bee Colony systems. The experiment results show that the SBO outperforms the existing methods.

scholarly journals A Modified Artificial Bee Colony Algorithm with Firefly Algorithm Strategy for Continuous Optimization Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Amnat Panniem ◽  
Pikul Puphasuk
Keyword(s):  
Firefly Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Control Parameters ◽  
Exploration And Exploitation ◽  
Bee Colony ◽  
Continuous Problems ◽  
Local Solutions ◽  
Continuous Optimization Problems

Artificial Bee Colony (ABC) algorithm is one of the efficient nature-inspired optimization algorithms for solving continuous problems. It has no sensitive control parameters and has been shown to be competitive with other well-known algorithms. However, the slow convergence, premature convergence, and being trapped within the local solutions may occur during the search. In this paper, we propose a new Modified Artificial Bee Colony (MABC) algorithm to overcome these problems. All phases of ABC are determined for improving the exploration and exploitation processes. We use a new search equation in employed bee phase, increase the probabilities for onlooker bees to find better positions, and replace some worst positions by the new ones in onlooker bee phase. Moreover, we use the Firefly algorithm strategy to generate a new position replacing an unupdated position in scout bee phase. Its performance is tested on selected benchmark functions. Experimental results show that MABC is more effective than ABC and some other modifications of ABC.

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