Adaptive Genetic Algorithm Based Approach for Evolutionary Design and Multi-objective Optimization of Logic Circuits

2006 ◽  
Author(s):  
Shuguang Zhao ◽  
Jianxun Zhao ◽  
Licheng Jiao
Keyword(s):  
Genetic Algorithm ◽  
Logic Circuits ◽  
Evolutionary Design ◽  
Adaptive Genetic Algorithm ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals Self-adaptive Genetic Algorithm and Fuzzy Decision Based Multi-objective Optimization in Microgrid with DGs

2016 ◽  
Vol 10 (1) ◽  
pp. 46-57 ◽  
Author(s):  
Shanyi Xie ◽  
Ruicong Zhai ◽  
Xianhu Liu ◽  
Baoguo Li ◽  
Kai Long ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Transmission Loss ◽  
Environmental Cost ◽  
Adaptive Genetic Algorithm ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Fuzzy Decision ◽  
Real Time Control ◽  
Multi Objective ◽  
Self Adaptive

Microgrid is one practical infrastructure to integrate Distributed Generations (DGs) and local loads. Its optimal operating strategy has aroused great attention in recent years. This paper mainly focuses on the multi-objective optimization of DGs in microgrid by using self-adaptive genetic algorithm (GA) and fuzzy decision. Five objective functions are taken into account comprising voltage offset, transmission loss, construction cost, purchase cost and the environmental cost. In the algorithm, self-adaptation in population size, mutation probability, selection and standardization of objective functions is developed to enhance the speed and efficiency of the algorithm. Moreover, fuzzy decision is applied to determine the final solution. Simulation results show this algorithm can effectively find the optimal solution and improve the real-time control of microgrid, which implies the possibility of potential applications in microgrid energy management system.

Multi-Objective Optimization of Manufacturing Workshop Layout Based on Improved Genetic Algorithm

2013 ◽  
Vol 860-863 ◽  
pp. 2664-2668
Author(s):  
Bi Hong Tang ◽  
Zhi Xia Zhang
Keyword(s):  
Genetic Algorithm ◽  
Mutation Rate ◽  
Facility Layout ◽  
Adaptive Genetic Algorithm ◽  
Multi Objective Optimization ◽  
Improved Genetic Algorithm ◽  
Multi Objective ◽  
Crossover Rate ◽  
Mutation Strategy ◽  
Adaptive Crossover

A good manufacturing workshop layout can influence the profit of the manufacturing enterprises after the product coming on stream. Facility layout of workshop is a combinational optimization problem. The multi-objective optimization model which integrates the available problem of facility layout of workshop is established. Adaptive Genetic Algorithm is presented because of the disadvantage of simple Genetic Algorithm in solving this model. This algorithm use the adaptive crossover and mutation strategy which is used to nonlinear processing for crossover rate and mutation rate, then crossover rate and mutation rate are changed with the colony adaptation degree of each generation. It has some advantage, such as higher search speed, higher convergence precision, and so on. Finally an example is used to show the effectiveness of the method.

scholarly journals Multi-Objective Optimization Design of Corrugated Steel Sandwich Panel for Impact Resistance

Metals ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1378
Author(s):  
Li Ke ◽  
Kun Liu ◽  
Guangming Wu ◽  
Zili Wang ◽  
Peng Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Structural Parameters ◽  
Impact Resistance ◽  
Sandwich Panels ◽  
Adaptive Genetic Algorithm ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Sample Points

The application of corrugated steel sandwich panels on ships requires excellent structural performance in impact resistance, which is often achieved by increasing the weight without giving full play to the characteristics of the structure. Considering the mechanical properties of sandwich panels under static and impact loading, a multi-objective optimal method based on a back-propagation (BP) neural network and a genetic algorithm developed in MATLAB is proposed herein. The evaluation criteria for this method included structural mass, static and dynamic stress, static and dynamic deformation, and energy absorption. Before optimization, representative sample points were obtained through numerical simulation calculations. Then, the functional relationship between the design and output variables was generated using the BP neural network. Finally, a standard genetic algorithm (SGA) and an adaptive genetic algorithm (AGA) were used for multi-objective optimization analysis with the established function to obtain the best result. Through this study, a new design concept with high efficiency and reliability was developed to determine the structural parameters that provide the best impact resistance using limited sample points.

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