A hybrid biogeography-based optimization and fuzzy C-means algorithm for image segmentation

Zhang, Minxia; Jiang, Weixuan; Zhou, Xiaohan; Xue, Yu; Chen, Shengyong

doi:10.1007/s00500-017-2916-9

Cited by 54 publications

(31 citation statements)

References 38 publications

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“…If the lines of each object in the graph are distributed in [0, 1], then the better convergence of the algorithm is proved. If the lines can be evenly distributed in [0, 1], then the better distribution of the algorithm is [ 22 , 23 ].…”

Section: Resultsmentioning

confidence: 99%

Decomposition and adaptive weight adjustment method with biogeography/complex algorithm for many-objective optimization

Chen

Guohua

2020

PLoS ONE

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In the EMO (evolutionary multi-objective, EMO) algorithm, MaOPs (many objective optimization problems, MaOPs) are sometimes difficult to keep the balance of convergence and diversity. The decomposition based EMO developed for MaOPs has been proved to be effective, and BBO/Complex (the biogeography based optimization for complex system, BBO/Complex) algorithm is a low complexity algorithm. In this paper, a decomposition and adaptive weight adjustment based BBO/Complex algorithm (DAWA-BBO/Complex) for MaOPs is proposed. First, a new method based on crowding distance is designed to generate a set of weight vectors with good uniformly. Second, an adaptive weight adjustment method is used to solve MaOPs with complex Pareto optimal front. Subsystem space obtains a non-dominated solution by a new selection strategy. The experimental results show that the algorithm is superior to other new algorithms in terms of convergence and diversity in DTLZ benchmark problems. Finally, the algorithm is used to solve the problem of NC (numerical control machine, NC) cutting parameters, and the final optimization result is obtained by AHP (Analytic Hierarchy Process, AHP) method. The results show that the cutting speed is 10.8m/min, back cutting depth is 0.13mm, the cutting time is 504s and the cutting cost is 22.15yuan. The proposed algorithm can effectively solve the practical optimization problem.

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Section: Resultsmentioning

confidence: 99%

Decomposition and adaptive weight adjustment method with biogeography/complex algorithm for many-objective optimization

Chen

Guohua

2020

PLoS ONE

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“…The performance of FCM clustering heavily depends on the quality of initial cluster centroids [28], [29]. Instead of randomly setting initial cluster centroids, we use a metaheuristic algorithm, ecogeography-based optimization (EBO) [30], to optimize initial cluster centroids [29]…”

Section: B a Metaheuristic For Optimizing Clustersmentioning

confidence: 99%

Intelligent Optimization of Diversified Community Prevention of COVID-19 Using Traditional Chinese Medicine

Zheng

Yang

et al. 2020

IEEE Comput. Intell. Mag.

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Tr aditional Chinese medicine (TCM) has played an important role in the prevention and control of the novel coronavirus pneumonia (COVID-19), and community prevention has become the most essential part in reducing the risk of spread and protecting public health. However, most communities use a unified TCM prevention program for all residents, which violates the "treatment based on syndrome differentiation"

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“…The EAs mainly include genetic algorithm, evolutionary programming and evolutionary strategy [2]. There were some other swarm intelligence EAs like ant colony algorithm [3], particle swarm [4], cultural algorithm [5], differential evolution [6], artificial bee colony [7], gravitational search algorithm [8], biogeography based optimization [9] and teaching-learning-based optimization [10]. In contrast, there's not a lot of research about the following EAs yet very promising.…”

Section: Definition 2 (Pareto Solution)mentioning

confidence: 99%

Adaptive Truncation technique for Constrained Multi-Objective Optimization

2019

KSII TIIS

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The performance of evolutionary algorithms can be seriously weakened when constraints limit the feasible region of the search space. In this paper we present a constrained multi-objective optimization algorithm based on adaptive ε-truncation (ε-T-CMOA) to further improve distribution and convergence of the obtained solutions. First of all, as a novel constraint handling technique, ε-truncation technique keeps an effective balance between feasible solutions and infeasible solutions by permitting some excellent infeasible solutions with good objective value and low constraint violation to take part in the evolution, so diversity is improved, and convergence is also coordinated. Next, an exponential variation is introduced after differential mutation and crossover to boost the local exploitation ability. At last, the improved crowding density method only selects some Pareto solutions and near solutions to join in calculation, thus it can evaluate the distribution more accurately. The comparative results with other state-of-the-art algorithms show that ε-T-CMOA is more diverse than the other algorithms and it gains better in terms of convergence in some extent.

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A hybrid biogeography-based optimization and fuzzy C-means algorithm for image segmentation

Cited by 54 publications

References 38 publications

Decomposition and adaptive weight adjustment method with biogeography/complex algorithm for many-objective optimization

Decomposition and adaptive weight adjustment method with biogeography/complex algorithm for many-objective optimization

Intelligent Optimization of Diversified Community Prevention of COVID-19 Using Traditional Chinese Medicine

Adaptive Truncation technique for Constrained Multi-Objective Optimization

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