scholarly journals Global optimization via neural network approximation of inverse coordinate mappings with evolutionary parameter control

2019 ◽  
Vol 10 (2) ◽  
pp. 3-31
Author(s):  
Kirill Vladimirovich Pushkaryov
Keyword(s):  
Neural Network ◽  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
Particle Swarm ◽  
Test Problems ◽  
Short Term ◽  
Swarm Optimization ◽  
Multiple Variables ◽  
Objective Function Values

A hybrid method of global optimization NNAICM-PSO is presented. It uses neural network approximation of inverse mappings of objective function values to coordinates combined with particle swarm optimization to find the global minimum of a continuous objective function of multiple variables with bound constraints. The objective function is viewed as a black box. The method employs groups of moving probe points attracted by goals like in particle swarm optimization. One of the possible goals is determined via mapping of decreased objective function values to coordinates by modified Dual Generalized Regression Neural Networks constructed from probe points. The parameters of the search are controlled by an evolutionary algorithm. The algorithm forms a population of evolving rules each containing a tuple of parameter values. There are two measures of fitness: short-term (charm) and long-term (merit). Charm is used to select rules for reproduction and application. Merit determines survival of an individual. This two-fold system preserves potentially useful individuals from extinction due to short-term situation changes. Test problems of 100 variables were solved. The results indicate that evolutionary control is better than random variation of parameters for NNAICM-PSO. With some problems, when rule bases are reused, error progressively decreases in subsequent runs, which means that the method adapts to the problem.

scholarly journals Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2873 ◽  
Author(s):  
Dinh Thanh Viet ◽  
Vo Van Phuong ◽  
Minh Quan Duong ◽  
Quoc Tuan Tran
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Electricity Market ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Short Term ◽  
Swarm Optimization

As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to adjust the parameters of the first particle swarm optimization algorithm to enhance the accuracy of the forecasting results. The models were tested with actual data collected from the Tuy Phong wind power plant in Binh Thuan Province, Vietnam. The testing showed improved accuracy and that this model can be widely implemented at other wind farms.

Neural Network Assisted Particle Swarm Optimization of Machining Process

2013 ◽  
Vol 581 ◽  
pp. 511-516
Author(s):  
Uros Zuperl ◽  
Franci Cus
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Machining Process ◽  
Neural Network Modeling ◽  
Swarm Optimization ◽  
Turning Operation ◽  
Optimization System

In this paper, optimization system based on the artificial neural networks (ANN) and particle swarm optimization (PSO) algorithm was developed for the optimization of machining parameters for turning operation. The optimization system integrates the neural network modeling of the objective function and particle swarm optimization of turning parameters. New neural network assisted PSO algorithm is explained in detail. An objective function based on maximum profit, minimum costs and maximum cutting quality in turning operation has been used. This paper also exhibits the efficiency of the proposed optimization over the genetic algorithms (GA), ant colony optimization (ACO) and simulated annealing (SA).

scholarly journals Designing a Multistage Supply Chain in Cross-Stage Reverse Logistics Environments: Application of Particle Swarm Optimization Algorithms

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Tzu-An Chiang ◽  
Z. H. Che ◽  
Zhihua Cui
Keyword(s):  
Mathematical Model ◽  
Particle Swarm Optimization ◽  
Supply Chain ◽  
Objective Function ◽  
Reverse Logistics ◽  
Particle Swarm ◽  
Supply Chain Design ◽  
Reverse Supply Chain ◽  
Swarm Optimization ◽  
Objective Function Values

This study designed a cross-stage reverse logistics course for defective products so that damaged products generated in downstream partners can be directly returned to upstream partners throughout the stages of a supply chain for rework and maintenance. To solve this reverse supply chain design problem, an optimal cross-stage reverse logistics mathematical model was developed. In addition, we developed a genetic algorithm (GA) and three particle swarm optimization (PSO) algorithms: the inertia weight method (PSOA_IWM),VMaxmethod (PSOA_VMM), and constriction factor method (PSOA_CFM), which we employed to find solutions to support this mathematical model. Finally, a real case and five simulative cases with different scopes were used to compare the execution times, convergence times, and objective function values of the four algorithms used to validate the model proposed in this study. Regarding system execution time, the GA consumed more time than the other three PSOs did. Regarding objective function value, the GA, PSOA_IWM, and PSOA_CFM could obtain a lower convergence value than PSOA_VMM could. Finally, PSOA_IWM demonstrated a faster convergence speed than PSOA_VMM, PSOA_CFM, and the GA did.

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