Species co-evolutionary algorithm: a novel evolutionary algorithm based on the ecology and environments for optimization

Li, Wuzhao; Wang, Lei; Cai, Xingjuan; Hu, Junjie; Guo, Weian

doi:10.1007/s00521-015-1971-3

Cited by 17 publications

(6 citation statements)

References 29 publications

(27 reference statements)

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“…erefore, the goal of data mining has become how to effectively acquire more valuable Internet knowledge and information [20]. So far, there is no strict standard for the discipline of data mining [21,22]. In a broad sense, data mining (DM) is to find things that are valuable to users from vast databases or data systems and to extract and analyze relationships that users will not easily detect or assert from huge observation data sets and then give a valuable conclusion that the user can fully understand [23].…”

Section: Data Mining Technologymentioning

confidence: 99%

Intelligent Fuzzy Optimization Algorithm of Data Mining Based on BP Neural Network

Wei

Jiang

et al. 2022

Wireless Communications and Mobile Computing

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This article aims to explore the intelligent fuzzy optimization algorithm for data mining based on BP neural network. Although the database technology has been improved with the increase of the amount of data, facing the explosive growth of the amount of data, the previous database management methods have been unable to meet and analyze the hidden knowledge in this scale of data. Therefore, it is important to find better automated data processing methods to satisfy the classification and analysis of massive data. However, the current BP neural network is not yet perfect. This method has some problems, such as slow convergence speed. The problem is reflected in the problem of pattern recognition and insufficient generalization ability and stability. Based on the above description, the research content of this paper is an intelligent fuzzy optimization algorithm for data mining based on BP neural network. Considering that the training of the BP algorithm is based on the weight correction principle of error gradient descent, the genetic algorithm is good at global search, but it does not have accurate local searchability. Therefore, this paper uses the weight of the genetic algorithm. This paper improves BP neural network based on a genetic algorithm. The experimental simulation results of Iris show that the quantity of hidden nodes usually increases with the number of training samples. ACBP algorithm can construct a better network structure based on the number of training samples. And through the experimental comparison of the traditional BP neural network algorithm, it is concluded that the improved algorithm can allow data mining technology to mine relatively more ideal data from complex environments.

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Section: Data Mining Technologymentioning

confidence: 99%

Intelligent Fuzzy Optimization Algorithm of Data Mining Based on BP Neural Network

Wei

Jiang

et al. 2022

Wireless Communications and Mobile Computing

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“…From the perspective of the industrial chain, such distribution characteristics reflect the following. (1) ere is no significant difference in the number of partners between node enterprises, which means that although a small number of core enterprises with comparative advantages have been formed in the industrial chain, this advantage is not obvious.…”

Section: E Need To Improve the Level And Efficiency Of The Aged Care ...mentioning

confidence: 99%

“…With the deepening of China's aging population, the pension industry has gradually become a new sunrise industry, with an immeasurable development prospect [1]. China has been involved in a serious wave of aging, and the number of elderly people is increasing at an unprecedented rate.…”

Section: Introductionmentioning

confidence: 99%

Transformation and Optimization of Rural Ecological Endowment Industry Chain Based on Constrained Clustering Algorithm

Zhao

Zhi-yi

2022

Scientific Programming

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China is experiencing a serious aging crisis, with the number of elderly people rising at an unprecedented rate. First and foremost, the pension industry's service object is the pension. To build an old-age industry complex, make use of the unique natural resources in the countryside, provide the original ecological old-age care services, and transform, optimize, and make efficient use of the existing policies and technologies. The drawbacks of the traditional pension model are becoming increasingly apparent. We must investigate a new pension model in order to ensure the long-term development of China's pension system and reduce the increased pension burden on governments at all levels. The rural areas' unique natural resources should be utilized to provide unique ecological elderly care services, and transformation and optimization must be implemented (that is, making efficient use of existing policies and technology to construct an elderly care industry complex). As a new pension mode for the elderly to become more connected to the natural world and improve their physical and mental health, ecological pensions have emerged as a new economic growth point that can fill the gap in the pension industry's supply while also meeting the elderly's growing spiritual and cultural needs. A constrained clustering algorithm is proposed in this article. Unsupervised learning is used in the constrained clustering algorithm. The clustering algorithm must determine the data objects to be clustered because they are not labeled. Because the data objects have no prior knowledge, the clustering algorithm analyzes them using the same principles. The effectiveness of the clustering results is determined by the dataset's adherence to the previously stated principles. The constrained clustering algorithm has greatly improved the transformation and optimization of the rural ecological elderly care industrial chain.

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“…It evolves the individual by simulating the principle of survival of the fittest in nature, and can obtain high-quality solutions while avoiding local optimal solutions. Ever since, many other evolutionary algorithms emerge and poorer, including differential evolution (DE) [10], evolutionary programming (EP) [11], genetic programming (GP) [12], evolutionary strategies (ES) [13], biogeography-based optimization (BBO) [14], monkey king evolution (MKE) [15] and species co-evolutionary algorithm (SCEA) [16], etc.…”

Section: Introductionmentioning

confidence: 99%

EJS: Multi-Strategy Enhanced Jellyfish Search Algorithm for En-Gineering Applications

Hu¹,

Wang²,

Li³

et al. 2022

Preprint

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Jellyfish search (JS) algorithm impersonates the foraging behavior of jellyfish in the ocean. It is a new developed meta-heuristic algorithm that solves complex and real world optimization problems. The global explore capability and robustness of JS are strong, but JS still has great development space in solving complex optimization problems with high dimensions and multiple local optima. Therefore, an enhanced jellyfish search (EJS) algorithm is developed in this study, and three improvements are made: (i) By adding sine and cosine learning factors, the jellyfish can learn from both random individual and best individual during Type B motion in swarm to enhance the optimization capability and convergence speed; (ii) Adding local escape operator can skip local optimal trap and boost the exploitation ability of JS; (iii) Opposition-based learning operator and quasi-opposition learning operator can increase and strengthen the population distribution more diversified, and better individuals are selected from present and new opposition-solution to participates in the next iteration, which can boost the solution’s quality, meanwhile convergence speed is fasted and its precision is increased. In addition, the performance contrast of the developed EJS and some previous outstanding and advanced methods are evaluated on CEC2017, CEC2019 test suite and six real engineering example of case. It is demonstrated that EJS algorithm escaped the trap of local optimum, enhanced the solution’s quality and the calculation speed. What’s more, the practical engineering applications of EJS algorithm also verify its superiority and effectiveness in solving both constrained and unconstrained optimization problems, and it stretched one’s mind for solving such optimization problems.

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Species co-evolutionary algorithm: a novel evolutionary algorithm based on the ecology and environments for optimization

Cited by 17 publications

References 29 publications

Intelligent Fuzzy Optimization Algorithm of Data Mining Based on BP Neural Network

Intelligent Fuzzy Optimization Algorithm of Data Mining Based on BP Neural Network

Transformation and Optimization of Rural Ecological Endowment Industry Chain Based on Constrained Clustering Algorithm

EJS: Multi-Strategy Enhanced Jellyfish Search Algorithm for En-Gineering Applications

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