AMOBH: Adaptive Multiobjective Black Hole Algorithm

Wu, Chong; Wu, Tao; Fu, Kaiyuan; Zhu, Yuan; Li, Yongbo; He, Weifeng; Tang, Shengwen

doi:10.1155/2017/6153951

Cited by 23 publications

(10 citation statements)

References 34 publications

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“…It is difficult to evaluate the advantages and disadvantages of the solution derived from multiobjective problem objectively [24]. is is because of the mutual restriction (through the decision variables) among the objectives in the multiobjective optimization problems.…”

Section: Multiobjective Optimization Based On Geneticmentioning

confidence: 99%

Planning the Structure of University Teaching Staff Based on Multiobjective Optimization Method

Zhang¹

2021

Scientific Programming

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University education is a hot topic of research in this era of outcome-based education in a learning-centric atmosphere, as people struggle for a higher quality of life and technological advancements. The key problems remain in structuring the teaching staff to achieve optimal information transmission and quality. Existing research aims to improve the quality of teaching of the staff, but majority of them fail to achieve their objectives. Multiobjective (MO) optimization has attracted researchers’ interest, particularly, in the context of performance monitoring and improving teaching quality. The goal of this research is to look into techniques for improving academic accomplishment through the planning structure of university teaching staff. I have adopted the Jaynes maximum entropy principle and fuzzy entropy concept to solve the structural optimization problem in the development of teaching staff in colleges and universities. The objective function and constraints in multiobjective optimization are determined, and the multiobjective optimization issue in the development of teaching staff structure is solved using the nondominated sorting genetic algorithm (NSGA-II) multiobjective genetic algorithm. The results show that the optimized structure of the teaching staff can reflect the goal of the construction of the teaching staff in colleges and universities and provide a scientific basis for the construction and planning of the teaching staff.

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Section: Multiobjective Optimization Based On Geneticmentioning

confidence: 99%

Planning the Structure of University Teaching Staff Based on Multiobjective Optimization Method

Zhang¹

2021

Scientific Programming

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“…To solve above MOP, we use the adaptive multi-objective black hole algorithm (AMOBH) [14] which has several advantages: lower computational complexity, faster convergence rate, and better population diversity compared to stateof-the-art methods. The Pareto solution set of above MOP is corresponding to a set of different weighted combination of two objectives.…”

Section: Identification Of Essential Proteins Using Adaptive Multi-obmentioning

confidence: 99%

“…However, the generalization ability of these methods above is not good. In this paper, to improve the generalization ability, we consider the identification of essential proteins as a multi-objective optimization problem and use the adaptive multi-objective black hole algorithm (AMOBH) [14] to solve it, the new method is called IMAMOBH. After the optimization, we get a Pareto solution set.…”

Section: Introductionmentioning

confidence: 99%

Identification of Essential Proteins using a Novel Multi-objective Optimization Method

Wang¹,

Zhang²,

Zhang³

et al. 2020

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<p>Using graph theory to identify essential proteins is a hot topic at present. These methods are called network-based methods. However, the generalization ability of most network-based methods is not satisfactory. Hence, in this paper, we consider the identification of essential proteins as a multi-objective optimization problem and use a novel multi-objective optimization method to solve it. The optimization result is a set of Pareto solutions. Every solution in this set is a vector which has a certain number of essential protein candidates and is considered as an independent predictor or voter. We use a voting strategy to assemble the results of these predictors. To validate our method, we apply it on the protein-protein interactions (PPI) datasets of two species (Yeast and Escherichia coli). The experiment results show that our method outperforms state-of-the-art methods in terms of sensitive, specificity, F-measure, accuracy, and generalization ability.</p>

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“…Pires et al adopted the Shannon entropy to study the dynamics of MOPSO [ 19 ] and nonsorting GA II [ 20 ] during their execution. Wu et al proposed a MOEA considering individual density (cell density) where the Shannon entropy was used to estimate the evolution state [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Shannon Performance in a Multiobjective Particle Swarm Optimization

Pires

Machado

Oliveira

2019

Entropy

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Particle swarm optimization (PSO) is a search algorithm inspired by the collective behavior of flocking birds and fishes. This algorithm is widely adopted for solving optimization problems involving one objective. The evaluation of the PSO progress is usually measured by the fitness of the best particle and the average fitness of the particles. When several objectives are considered, the PSO may incorporate distinct strategies to preserve nondominated solutions along the iterations. The performance of the multiobjective PSO (MOPSO) is usually evaluated by considering the resulting swarm at the end of the algorithm. In this paper, two indices based on the Shannon entropy are presented, to study the swarm dynamic evolution during the MOPSO execution. The results show that both indices are useful for analyzing the diversity and convergence of multiobjective algorithms.

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AMOBH: Adaptive Multiobjective Black Hole Algorithm

Cited by 23 publications

References 34 publications

Planning the Structure of University Teaching Staff Based on Multiobjective Optimization Method

Planning the Structure of University Teaching Staff Based on Multiobjective Optimization Method

Identification of Essential Proteins using a Novel Multi-objective Optimization Method

Dynamic Shannon Performance in a Multiobjective Particle Swarm Optimization

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