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.

Kinematic Multi-Objective Optimization of Circular-Toothed Gerotor Pumps by Genetic Algorithm

2017 ◽  
Author(s):  
Andrew J. Robison ◽  
Andrea Vacca
Keyword(s):  
Genetic Algorithm ◽  
Design Method ◽  
Third Party ◽  
Low Flow ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Generation Algorithm ◽  
Multi Objective ◽  
Pareto Curve

A gerotor gear generation algorithm has been developed that evaluates key performance objective functions to be minimized or maximized, and then an optimization algorithm is applied to determine the best design. Because of their popularity, circular-toothed gerotors are the focus of this study, and future work can extend this procedure to other gear forms. Parametric equations defining the circular-toothed gear set have been derived and implemented. Two objective functions were used in this kinematic optimization: maximize the ratio of displacement to pump radius, which is a measure of compactness, and minimize the kinematic flow ripple, which can have a negative effect on system dynamics and could be a major source of noise. Designs were constrained to ensure drivability, so the need for additional synchronization gearing is eliminated. The NSGA-II genetic algorithm was then applied to the gear generation algorithm in modeFRONTIER, a commercial software that integrates multi-objective optimization with third-party engineering software. A clear Pareto front was identified, and a multi-criteria decision-making genetic algorithm was used to select three optimal designs with varying priorities of compactness vs low flow variation. In addition, three pumps used in industry were scaled and evaluated with the gear generation algorithm for comparison. The scaled industry pumps were all close to the Pareto curve, but the optimized designs offer a slight kinematic advantage, which demonstrates the usefulness of the proposed gerotor design method.

Multi-Objective Optimization of a 2-DoF Purely Translational Parallel Manipulator

2011 ◽  
Vol 317-319 ◽  
pp. 794-798
Author(s):  
Zhi Bin Li ◽  
Yun Jiang Lou ◽  
Yong Sheng Zhang ◽  
Ze Xiang Li
Keyword(s):  
Genetic Algorithm ◽  
Kinematic Analysis ◽  
Parallel Manipulator ◽  
Pareto Front ◽  
Parallel Robot ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Evolution Algorithm

The paper addresses the multi-objective optimization of a 2-DoF purely translational parallel manipulator. The kinematic analysis of the Proposed T2 parallel robot is introduced briefly. The objective functions are optimized simultaneously to improve Regular workspace Share (RWS) and Global Conditioning Index (GCI). A Multi-Objective Evolution Algorithm (MOEA) based on the Control Elitist Non-dominated Sorting Genetic Algorithm (controlled ENSGA-II) is used to find the Pareto front. The optimization results show that this method is efficient. The parallel manipulator prototype is also exhibited here.

scholarly journals MULTI-OBJECTIVE OPTIMIZATION OF REINFORCED CONCRETE FRAMES USING NSGA-II ALGORITHM

2016 ◽  
Vol 8 (4) ◽  
pp. 157-164 ◽  
Author(s):  
Mehdi Babaei ◽  
Masoud Mollayi
Keyword(s):  
Genetic Algorithm ◽  
Reinforced Concrete ◽  
Steel Structures ◽  
American Concrete Institute ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Design Variables ◽  
Pareto Fronts

In recent decades, the use of genetic algorithm (GA) for optimization of structures has been highly attractive in the study of concrete and steel structures aiming at weight optimization. However, it has been challenging for multi-objective optimization to determine the trade-off between objective functions and to obtain the Pareto-front for reinforced concrete (RC) and steel structures. Among different methods introduced for multi-objective optimization based on genetic algorithms, Non-Dominated Sorting Genetic Algorithm II (NSGA II) is one of the most popular algorithms. In this paper, multi-objective optimization of RC moment resisting frame structures considering two objective functions of cost and displacement are introduced and examined. Three design models are optimized using the NSGA-II algorithm. Evaluation of optimal solutions and the algorithm process are discussed in details. Sections of beams and columns are considered as design variables and the specifications of the American Concrete Institute (ACI) are employed as the design constraints. Pareto-fronts for the objective space have been obtained for RC frame models of four, eight and twelve floors. The results indicate smooth Pareto-fronts and prove the speed and accuracy of the method.

Optimization of Force and Moment Balance of a Four-Bar Linkage Using Multi-Objective Genetic Algorithm

2009 ◽  
Author(s):  
Mohammad Reza Farmani ◽  
A. Jaamiolahmadi
Keyword(s):  
Genetic Algorithm ◽  
Objective Functions ◽  
Fuzzy Decision ◽  
Nsga Ii ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Feasible Solutions ◽  
Four Bar Linkage ◽  
External Loads ◽  
Vibration Noise

In this study, force and moment balance of a four-bar linkage is implemented by using a Multi-Objective Genetic Algorithm (MOGA). During the time that an unbalanced linkage moves, it transmits shaking forces and moments to its surroundings. These transmitted forces and moments may cause some serious and undesirable problems such as vibration, noise, wear, and fatigue. In the current problem, the concepts of inertia counterweights and physical pendulum are utilized to complete balance of all mass effects (both linear and rotary, but excluding external loads), independent of input angular velocity. In this paper, Non-Dominated Genetic Algorithm (NSGA-II) is applied to minimize two objective functions subject to some different design constraints. The applied algorithm produced a set of feasible solutions called Pareto optimal solutions for the design problem. Finally, a fuzzy decision maker is applied to select the best solution among the obtained Pareto solutions based on design criteria. The results show that obtained solutions minimize the weights of applied counterweights and eliminate both shaking forces and moments transmitted to the ground, simultaneously.

A Simple GA Based Approach for Multi-Objective Optimization of Machining Parameters

2014 ◽  
Vol 974 ◽  
pp. 402-407 ◽  
Author(s):  
Akhtar Waseem ◽  
Jian Fei Su ◽  
Wu Yi Chen ◽  
Peng Fei Sun
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Simple Approach ◽  
Machining Parameters ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective

A simple approach to multi-objective optimization of machining parameters is presented. Regression analysis of experimental data is carried out to obtain the correlation between cutting parameters and response variables. Finally, Genetic Algorithm (GA) toolbox ofMATLABis used to carry out multi-objective optimization of two objective functions (surface roughness “Ra” & material removal rate “MRR”). Genetic algorithm is found to be a powerful tool for multi-objective optimization of machining parameters in this study.

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.

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