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Many-objective E-dominance dynamical evolutionary algorithm based on adaptive grid

SOFT COMPUTING 2018年第1期22卷 137-146页

作者： Yue, Xuezhi Guo, Zhaolu Yin, Yali Liu, Xiaosheng Jiangxi Univ Sci & Technol Sch Sci Ganzhou 341000 Peoples R China Jiangxi Univ Sci & Technol Sch Architectural & Surveying & Mapping Engn Ganzhou 341000 Peoples R China

In evolutionary multi-objective optimization, achieving a balance between convergence speed and population diversity remains a challenging topic especially for many-objective optimization problems (MaOPs). To accelerate convergence toward the Pareto front and maintain a high degree of diversity for MaOPs, we propose a new many-objective dynamical evolutionary algorithm based on E-dominance and adaptive-grid strategies (EDAGEA). In EDAGEA, it incorporates the E_dominance and adaptive strategies to enhance the search ability. Instead of the Pareto dominance mechanism in the traditional dynamical evolutionary algorithm, EDAGEA employs the E-dominance strategy to improve the selective pressure and to accelerate the convergence speed. Moreover, EDAGEA incorporates the adaptive-grid strategy to promote the uniformity and diversity of the population. In the experiments, the proposed EDAGEA algorithm is tested on DTLZ series problems under 3-8 objectives with diverse characteristics and is compared with two excellent many-objective evolutionary algorithms. Experimental results demonstrate that the proposed EDAGEA algorithm exhibits competitive performance in terms of both convergence speed and diversity of population.

关键词： Many-objective dynamical evolutionary algorithm Adaptive-grid E-dominance

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Hybrid dynamical evolutionary algorithm and Time Complexity Analysis

Hybrid Dynamical Evolutionary Algorithm and Time Complexity ...

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International Conference on Information Technology and Management Innovation (ICITMI2012)

作者： Li, Huiying Zhang, Yilai Xu, Xing Jingdezhen Ceram Inst Sch Informat Engn Jingdezhen Peoples R China

ISBN: (纸本)9783037855744

The dynamical evolutionary algorithm (DEA) is a new evolutionary algorithm based on the theory of statistical mechanics, however, DEA converges slowly and often converge at local optima for some function optimization problems. In this paper, a hybrid dynamical evolutionary algorithm (HDEA) with multi-parent crossover and differential evolution mutation is proposed for accelerating convergence velocity and easily escaping suboptimal solutions. Moreover, the population of HDEA is initialized by chaos. In order to confirm the effectiveness of our algorithm, HDEA is applied to solve the typical numerical function minimization problems. The computational complexity of HDEA is analyzed, and the experimental results show that HDEA outperforms the DEA in the aspect of convergence velocity and precision, even the two algorithms have the similar time complexity.

关键词： dynamical evolutionary algorithm chaos multi-parent crossover differential evolution time complexity

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Parameter Estimation for Asymptotic Regression Model by dynamical evolutionary algorithm

Parameter Estimation for Asymptotic Regression Model by Dyna...

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International Conference on Innovative Computing and Communication/Asia-Pacific Conference on Information Technology and Ocean Engineering

作者： Ye Hai-peng Hu Hao Wenzhou Vocat Coll Sci & Technol Dept Comp Wenzhou Zhejiang Peoples R China Jiangsu Polytech Univ Sch Informat Sci & Engn Changzhou Peoples R China

ISBN: (纸本)9780769539423

The dynamical evolutionary algorithm (DEA) is a novel evolutionary computation technology, which is based on the theory of statistical mechanics. In this paper, an improved dynamical evolutionary algorithm (IDEA) with multi-parent crossover and differential evolution mutation is proposed and IDEA is applied to estimate parameters for asymptotic regression model for the first time. In order to confirm performance of our algorithm, IDEA is verified on six groups of actual data and several sets of random sampling data, and then how sampling range and data with Gaussian noise influence on the performance of IDEA is considered. Experimental results show that IDEA is a stable, reliable and effective method in parameter estimation for asymptotic regression model and it's robust to noise.

关键词： dynamical evolutionary algorithm asymptotic regression model parameter estimation improved algorithm

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Hybrid dynamical evolutionary algorithm and Time Complexity Analysis

Hybrid Dynamical Evolutionary Algorithm and Time Complexity ...

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2010 International Conference on Circuit and Signal Processing (ICCSP 2010) & 2010 Second IITA International Joint Conference on Artificial Intelligence (IITA-JCAI 2010)

作者： Huiying LI School of Information Engineering Jingdezhen Ceramic Institute Jingdezhen,China

The dynamical evolutionary algorithm(DEA) is a new evolutionary algorithm based on the theory of statistical mechanics,however,DEA converges slowly and often converge at local optima for some function optimization *** this paper,a hybrid dynamical evolutionary algorithm(HDEA) with multi-parent crossover and differential evolution mutation is proposed for accelerating convergence velocity and easily escaping suboptimal ***,the population of HDEA is initialized by *** order to confirm the effectiveness of our algorithm,HDEA is applied to solve the typical numerical function minimization *** computational complexity of HDEA is analyzed,and the experimental results show that HDEA outperforms the DEA in the aspect of convergence velocity and precision,even the two algorithms have the similar time complexity.

关键词： dynamical evolutionary algorithm chaos multi-parent crossover differential evolution time complexity

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An Improved dynamical evolutionary algorithm with Multi-parent Crossover

An Improved Dynamical Evolutionary Algorithm with Multi-pare...

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2010 The 2nd International Conference on Industrial Mechatronics and Automation (ICIMA 2010)

作者： Li Zhenshuang,Wang Da College of Computer and Information Technology Nanyang Normal University Nanyang,China

This paper proposes an improved dynamical evolutionary algorithm (IDEA) based on Multi-parent *** preventing premature convergence effectively and keeping the population in good distribution,the new algorithm makes full use of Multi-parent Crossover,overcoming the disadvantage of big searching dead zone existed in conventional mutation *** numerical results show IDEA not only has good performance and a high degree of reliability while dealing with various complex problems,but also is superior to any other published results.

关键词： dynamical evolutionary algorithm Multi-parent Crossover Improved algorithm

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The Tracking dynamical evolutionary algorithm for Dynamic Environments

The Tracking Dynamical Evolutionary Algorithm for Dynamic En...

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International Symposium on Intelligent Ubiquitous Computing and Education

作者： Liu, Suming Zhao, Qiang Zhang, Yumei Hebei Univ Engn Sch Mech Engn Handan 056038 Peoples R China Hebei Univ Engn Sch Human Handan 056038 Peoples R China

ISBN: (纸本)9780769536194

In this paper, we proposed the Tracking dynamical evolutionary algorithm (TDEA) that can efficiently locate and track the optimal solution in a dynamically changing environment. In TDEA, the particle's structure is different from traditional DEA. Each particle's knowledge is applied an "evaporation constant" to gradually weaken the knowledge's validity. Through this mechanism, the knowledge of each particle will be gradually updated in a dynamically changing environment. Compared with the traditional DEA, TDEA can quickly converge to the area of the goal and maintain the shortest distance from the goal.

关键词： dynamical evolutionary algorithm Dynamic Environments

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The Tracking dynamical evolutionary algorithm for Dynamic Environments

The Tracking Dynamical Evolutionary Algorithm for Dynamic En...

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2009 International Symposium on Intelligent Ubiquitous Computing and Education(2009国际智能普适计算与教育研讨会 IUCE 2009)

作者： Suming Liu Qiang Zhao Yumei Zhang The School of Mechanical Engineering Hebei University of Engineering Handan. China 056038 The School of Mechanical Engineering Hebei University of Engineering Handan China056038 The School of Humanities Hebei University of Engineering Handan China056038

关键词： dynamical evolutionary algorithm Dynamic

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An improved dynamical evolutionary algorithm based on chaotic

An improved dynamical evolutionary algorithm based on chaoti...

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7th International Conference on Machine Learning and Cybernetics

作者： Jiang, Yi Wang, Ling Chen, Li Wuhan Univ Sch Comp Wuhan 430072 Peoples R China

ISBN: (纸本)9781424420957

An improved dynamical evolutionary algorithm based on the chaotic is proposed for optimizing. The new algorithm makes full use of initial value sensitivity and track ergodicity of chaos, overcoming the disadvantage of big searching dead zone existed in conventional chaotic mutation model. To achieve high performance in optimizing, the chaotic search mechanism is embedded in the standard dynamical evolutionary algorithm adaptively to avoid the stagnancy of population and increase the speed of convergence. The method keeps balance between the global search and the local search. It has been compared with other methods. In comparison, the proposed method shows its superiority in convergence property and robustness. It is validated by the simulation results.

关键词： dynamical evolutionary algorithm chaotic search optimization

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A New dynamical evolutionary algorithm By Using Chaos

A New Dynamical Evolutionary Algorithm By Using Chaos

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2006年国际电子、工程及科学领域的分布式计算和应用学术研讨会

作者： Xing Xu,Yuanxiang Li,Weiqin Ying The State Key Laboratory of Software Engineering,Wuhan University Wuhan,Hubei,430072,P.R.China

In this paper,a new dynamical evolutionary algorithm using chaos(CDEA) is proposed based on the statistical *** spread-spectrum characteristic of chaos, we generate multi-population in remote difference of the initial *** to the selecting strategy of traditional dynamical evolutionary algorithm the best individual of the multi-population is migrated into the other populations replacing the worst one of them affirmatively has the chance to *** algorithm presented in this paper by introducing chaos has bigger selective pressure, and can keep diversity of the *** order to verify the effectiveness of our algorithm,we apply CDEA to solve the typical numerical function minimization *** experimental results show that CDEA is fast and reliable.

关键词： dynamical evolutionary algorithm Statistical Mechanics Chaos Global Optimization Multi-population

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A New dynamical evolutionary algorithm Based on the Principle of Minimal Free Energy

A New Dynamical Evolutionary Algorithm Based on the Principl...

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智能计算及其应用国际会议

作者： HU Ting1, LI Yuanxiang1, 2, DING Wei1 (1. Computer School, Wuhan University, Wuhan 430079, China) (2. The State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China)

In this paper, a new dynamical evolutionary algorithm is proposed based on principle of minimal free energy from the statistical mechanics. Also, a definition of the entropy of the particle system is

关键词： dynamical evolutionary algorithm statistical mechanics information theory global optimization simulated annealing

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