Fractional stochastic resonance multi-parameter adaptive optimization algorithm based on genetic algorithm

Zheng, Yongjun; Huang, Ming; Yi, Lu; Li, Wenjun

doi:10.1007/s00521-018-3910-6

Cited by 31 publications

(13 citation statements)

References 15 publications

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“…It applies the principles: genetics, mutation, natural selection, and crossover. A set of initial candidates is created, and their corresponding fitness values are calculated [44][45][46]. In GA, many processes are random, like in evolution.…”

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

A Comparative Study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in Estimating the Heating Load of Buildings’ Energy Efficiency for Smart City Planning

et al. 2019

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Energy-efficiency is one of the critical issues in smart cities. It is an essential basis for optimizing smart cities planning. This study proposed four new artificial intelligence (AI) techniques for forecasting the heating load of buildings’ energy efficiency based on the potential of artificial neural network (ANN) and meta-heuristics algorithms, including artificial bee colony (ABC) optimization, particle swarm optimization (PSO), imperialist competitive algorithm (ICA), and genetic algorithm (GA). They were abbreviated as ABC-ANN, PSO-ANN, ICA-ANN, and GA-ANN models; 837 buildings were considered and analyzed based on the influential parameters, such as glazing area distribution (GLAD), glazing area (GLA), orientation (O), overall height (OH), roof area (RA), wall area (WA), surface area (SA), relative compactness (RC), for estimating heating load (HL). Three statistical criteria, such as root-mean-squared error (RMSE), coefficient determination (R2), and mean absolute error (MAE), were used to assess the potential of the aforementioned models. The results indicated that the GA-ANN model provided the highest performance in estimating the heating load of buildings’ energy efficiency, with an RMSE of 1.625, R2 of 0.980, and MAE of 0.798. The remaining models (i.e., PSO-ANN, ICA-ANN, ABC-ANN) yielded lower performance with RMSE of 1.932, 1.982, 1.878; R2 of 0.972, 0.970, 0.973; MAE of 1.027, 0.980, 0.957, respectively.

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Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

A Comparative Study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in Estimating the Heating Load of Buildings’ Energy Efficiency for Smart City Planning

et al. 2019

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“…A crossover operation that exchanges some parts of the two parents to produce a new pair of offspring is conducted to seek promising solutions from the search space [63]. Given a crossover probability ε, if generating a random number R with a standard uniform distribution is less than ε, a locus is chosen between two neighbouring chromosomes, i.e., parents 1 and 2. e chromosomes after this locus are exchanged, thereby forming new offsprings 1 and 2 [64]. e crossover processing can be expressed as follows:…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…e mutation operation ensures the diversity of individuals and prevents solutions from being too similar to avoid obtaining the local optimal solution [65]. Similar to the crossover operation, given a mutation probability ε, if a random number R with a standard uniform distribution is generated and is less than ε, a locus is chosen randomly on the parent, and the value of the locus is reversed, thereby producing a new offspring [64]. e mutation process can be expressed as follows:…”

Section: Genetic Algorithmmentioning

confidence: 99%

Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction

Jun

Chen

2021

Discrete Dynamics in Nature and Society

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Although socially responsible investment (SRI) has developed into an important investment style, only a small number of studies discuss SRI portfolio construction. In view of the overwhelming breakthrough of machine learning in prediction, this paper proposes SRI portfolio construction models by combining a double-screening mechanism considering machine learning prediction and an extended global minimum variance (GMV) model (or extended maximum Sharpe ratio (MSPR) model), which are, respectively, named double-screening socially responsible investment (DSSRI) portfolio models I and II. The proposed models consist of two stages, i.e., stock screening and asset allocation. First, this paper develops a novel double-screening mechanism incorporating environmental, social, and corporate governance (ESG) and return potential criteria to ensure that high-quality stocks with good ESG performance and high-return potential are input into the optimal portfolio. Specifically, to obtain accurate stock return predictions, an extreme learning machine model optimized by the genetic algorithm is employed to predict stock prices. Next, to trade off the financial and ESG objectives of SRI investors, an extended GMV model (or extended MSPR model) considering the ESG factor is introduced to determine the capital allocation proportion of the stocks. We take the A-share market of China as the sample to verify the effectiveness of the proposed models. The empirical results demonstrate that compared with alternative models, the proposed models can yield better annualized return and ESG score performance as well as competitive Sharpe ratio performance.

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“…The genetic algorithm (GA) represents one branch of evolutionary computation that it applies the principles: genetics, mutation, natural selection, and crossover. A set of initial candidates is created, and their corresponding fitness values are calculated [36]- [38]. In GA, many processes are random, like in evolution.…”

Section: B Ga For the Proposed Empimentioning

confidence: 99%

Design of Multivariable PI Controller Using Evolutionary Algorithms for VSP based AC/DC Interconnected Systems

Naveh

Rakhshani

Mehrjerdi

et al. 2019

2019 2nd International Conference on Smart Grid and Renewable Energy (SGRE)

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A new application of MultivariableProportional-Integral (MPI) controller with using evolutionary algorithms for a VSP based AC/DC interconnected power system model is proposed. The VSP based HVDC model is added for mitigation of system frequency dynamics by emulating virtual inertia. The designed heuristic-based multivariable PI controller is proposed for better performance of the system's states during contingencies. The proposed MPI (MPI) controller is modified by adding optimisation-based evolutionary algorithms to improve characteristic performance model versus conventional control methods. Simulations results demonstrate how the proposed MPI controller can optimally improve the performance of the power system, especially when a VSP base inertia emulation is activated in the system.

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Fractional stochastic resonance multi-parameter adaptive optimization algorithm based on genetic algorithm

Cited by 31 publications

References 15 publications

A Comparative Study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in Estimating the Heating Load of Buildings’ Energy Efficiency for Smart City Planning

A Comparative Study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in Estimating the Heating Load of Buildings’ Energy Efficiency for Smart City Planning

Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction

Design of Multivariable PI Controller Using Evolutionary Algorithms for VSP based AC/DC Interconnected Systems

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