scholarly journals Hierarchical Artificial Bee Colony Optimizer with Divide-and-Conquer and Crossover for Multilevel Threshold Image Segmentation

2014 ◽  
Vol 2014 ◽  
pp. 1-22 ◽  
Author(s):  
Maowei He ◽  
Kunyuan Hu ◽  
Yunlong Zhu ◽  
Lianbo Ma ◽  
Hanning Chen ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Information Exchange ◽  
Artificial Bee Colony ◽  
Complete Solution ◽  
Experimental Results ◽  
Divide And Conquer ◽  
Foraging Strategies ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization (HABC), for multilevel threshold image segmentation, which employs a pool of optimal foraging strategies to extend the classical artificial bee colony framework to a cooperative and hierarchical fashion. In the proposed hierarchical model, the higher-level species incorporates the enhanced information exchange mechanism based on crossover operator to enhance the global search ability between species. In the bottom level, with the divide-and-conquer approach, each subpopulation runs the original ABC method in parallel to part-dimensional optimum, which can be aggregated into a complete solution for the upper level. The experimental results for comparing HABC with several successful EA and SI algorithms on a set of benchmarks demonstrated the effectiveness of the proposed algorithm. Furthermore, we applied the HABC to the multilevel image segmentation problem. Experimental results of the new algorithm on a variety of images demonstrated the performance superiority of the proposed algorithm.

scholarly journals Artificial Bee Colony Optimizer with Bee-to-Bee Communication and Multipopulation Coevolution for Multilevel Threshold Image Segmentation

2015 ◽  
Vol 2015 ◽  
pp. 1-23 ◽  
Author(s):  
Jun-yi Li ◽  
Yi-ding Zhao ◽  
Jian-hua Li ◽  
Xiao-jun Liu
Keyword(s):  
Image Segmentation ◽  
Information Exchange ◽  
Artificial Bee Colony ◽  
Experimental Results ◽  
New Method ◽  
Communication Pattern ◽  
Communication Model ◽  
Von Neumann ◽  
Bee Colony ◽  
Segmentation Problem

This paper proposes a modified artificial bee colony optimizer (MABC) by combining bee-to-bee communication pattern and multipopulation cooperative mechanism. In the bee-to-bee communication model, with the enhanced information exchange strategy, individuals can share more information from the elites through the Von Neumann topology. With the multipopulation cooperative mechanism, the hierarchical colony with different topologies can be structured, which can maintain diversity of the whole community. The experimental results on comparing the MABC to several successful EA and SI algorithms on a set of benchmarks demonstrated the advantage of the MABC algorithm. Furthermore, we employed the MABC algorithm to resolve the multilevel image segmentation problem. Experimental results of the new method on a variety of images demonstrated the performance superiority of the proposed algorithm.

scholarly journals Discrete and Continuous Optimization Based on Hierarchical Artificial Bee Colony Optimizer

2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
Lianbo Ma ◽  
Kunyuan Hu ◽  
Yunlong Zhu ◽  
Ben Niu ◽  
Hanning Chen ◽  
...  
Keyword(s):  
Artificial Bee Colony ◽  
Complete Solution ◽  
Continuous Optimization ◽  
Benchmark Problems ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Discrete And Continuous Optimization ◽  
Upper Level ◽  
Abc Method

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization (HABC), to tackle complex high-dimensional problems. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operator is applied to enhance the global search ability between species. Experiments are conducted on a set of 20 continuous and discrete benchmark problems. The experimental results demonstrate remarkable performance of the HABC algorithm when compared with other six evolutionary algorithms.

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.

scholarly journals Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-21 ◽  
Author(s):  
Lianbo Ma ◽  
Hanning Chen ◽  
Kunyuan Hu ◽  
Yunlong Zhu
Keyword(s):  
Radio Frequency Identification ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Complete Solution ◽  
Network Planning ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Mutation Operators ◽  
Planning Optimization ◽  
Bee Colony Optimization

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.

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