scholarly journals Coordinated Control of PV Generation and EVs Charging Based on Improved DECell Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Guo Zhao ◽  
Xueliang Huang ◽  
Hao Qiang
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Control Strategy ◽  
Optimization Model ◽  
Pareto Front ◽  
Optimal Solution ◽  
Coordinated Control ◽  
Nsga Ii ◽  
Nondominated Solutions ◽  
Pv Generation

Recently, the coordination of EVs’ charging and renewable energy has become a hot research all around the globe. Considering the requirements of EV owner and the influence of the PV output fluctuation on the power grid, a three-objective optimization model was established by controlling the EVs charging power during charging process. By integrating the meshing method into differential evolution cellular (DECell) genetic algorithm, an improved differential evolution cellular (IDECell) genetic algorithm was presented to solve the multiobjective optimization model. Compared to the NSGA-II and DECell, the IDECell algorithm showed better performance in the convergence and uniform distribution. Furthermore, the IDECell algorithm was applied to obtain the Pareto front of nondominated solutions. Followed by the normalized sorting of the nondominated solutions, the optimal solution was chosen to arrive at the optimized coordinated control strategy of PV generation and EVs charging. Compared to typical charging pattern, the optimized charging pattern could reduce the fluctuations of PV generation output power, satisfy the demand of EVs charging quantity, and save the total charging cost.

A Bi-Objective Order Allocation Optimization Model for Horizontal Manufacturing Collaborative Alliance

2010 ◽  
Vol 102-104 ◽  
pp. 836-840 ◽  
Author(s):  
Fang Qi Cheng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Optimal Solution ◽  
Order Allocation ◽  
Nsga Ii ◽  
Manufacturing Enterprises ◽  
Proposed Model ◽  
Optimal Solution Set ◽  
Simulation Results ◽  
Enterprise Community

Horizontal manufacturing collaborative alliance is a dispersed enterprise community consisting of several enterprises which produce the same kind of products. To correctly assign order among member companies of horizontal manufacturing collaborative alliance is one of the most important ways to improve the agility and competitiveness of manufacturing enterprises. For the order allocation problem, a bi-objective optimization model is developed to minimize the comprehensive cost and balance the production loads among the selected manufacturing enterprises. Non-dominated sorting genetic algorithm (NSGA-II) is applied to solve the optimization functions. The optimal solution set of Pareto is obtained. The simulation results indicate that the proposed model and algorithm is able to obtain satisfactory solutions.

scholarly journals Study on Parameter Optimization Design of Drum Brake Based on Hybrid Cellular Multiobjective Genetic Algorithm

2012 ◽  
Vol 2012 ◽  
pp. 1-23 ◽  
Author(s):  
Yi Zhang ◽  
Hu Zhang ◽  
Chao Lu
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Parameter Optimization ◽  
Optimization Design ◽  
Pareto Front ◽  
Nsga Ii ◽  
Multiobjective Genetic Algorithm ◽  
Drum Brake ◽  
Multiobjective Problems ◽  
Parameter Optimization Design

In consideration of the significant role the brake plays in ensuring the fast and safe running of vehicles, and since the present parameter optimization design models of brake are far from the practical application, this paper proposes a multiobjective optimization model of drum brake, aiming at maximizing the braking efficiency and minimizing the volume and temperature rise of drum brake. As the commonly used optimization algorithms are of some deficiency, we present a differential evolution cellular multiobjective genetic algorithm (DECell) by introducing differential evolution strategy into the canonical cellular genetic algorithm for tackling this problem. For DECell, the gained Pareto front could be as close as possible to the exact Pareto front, and also the diversity of nondominated individuals could be better maintained. The experiments on the test functions reveal that DECell is of good performance in solving high-dimension nonlinear multiobjective problems. And the results of optimizing the new brake model indicate that DECell obviously outperforms the compared popular algorithm NSGA-II concerning the number of obtained brake design parameter sets, the speed, and stability for finding them.

scholarly journals Carbon Emission Reduction of Apparel Material Distribution Based on Multi-Objective Genetic Algorithm (NSGA-II)

2019 ◽  
Vol 11 (9) ◽  
pp. 2571
Author(s):  
Xujing Zhang ◽  
Lichuan Wang ◽  
Yan Chen
Keyword(s):  
Genetic Algorithm ◽  
Carbon Emissions ◽  
Optimal Solution ◽  
Material Distribution ◽  
Low Carbon ◽  
Nsga Ii ◽  
Management Objectives ◽  
Multi Objective Genetic Algorithm ◽  
Distribution Process ◽  
The Cost

Low-carbon production has become one of the top management objectives for every industry. In garment manufacturing, the material distribution process always generates high carbon emissions. In order to reduce carbon emissions and the number of operators to meet enterprises’ requirements to control the cost of production and protect the environment, the paths of material distribution were analyzed to find the optimal solution. In this paper, the model of material distribution to obtain minimum carbon emissions and vehicles (operators) was established to optimize the multi-target management in three different production lines (multi-line, U-shape two-line, and U-shape three-line), while the workstations were organized in three ways: in the order of processes, in the type of machines, and in the components of garment. The NSGA-II algorithm (non-dominated sorting genetic algorithm-II) was applied to obtain the results of this model. The feasibility of the model and algorithm was verified by the practice of men’s shirts manufacture. It could be found that material distribution of multi-line layout produced the least carbon emissions when the machines were arranged in the group of type.

Multi-Objective Geometric Optimization of Elliptical-Toothed Gerotor Pumps for Kinematics and Wear by Genetic Algorithm

2018 ◽  
Author(s):  
Andrew J. Robison ◽  
Andrea Vacca
Keyword(s):  
Genetic Algorithm ◽  
Pareto Front ◽  
Adhesive Wear ◽  
Geometric Optimization ◽  
Objective Functions ◽  
Computationally Efficient ◽  
Nsga Ii ◽  
Generation Algorithm ◽  
Flow Ripple ◽  
Gerotor Pumps

A computationally efficient gerotor gear generation algorithm has been developed that creates elliptical-toothed gerotor gear profiles, identifies conditions to guarantee a feasible geometry, evaluates several performance objectives, and is suitable to use for geometric optimization. Five objective functions are used in the optimization: minimize pump size, flow ripple, adhesive wear, subsurface fatigue (pitting), and tooth tip leakage. The gear generation algorithm is paired with the NSGA-II optimization algorithm to minimize each of the objective functions subject to the constraints to define a feasible geometry. The genetic algorithm is run with a population size of 1000 for a total of 500 generations, after which a clear Pareto front is established and displayed. A design has been selected from the Pareto front which is a good compromise between each of the design objectives and can be scaled to any desired displacement. The results of the optimization are also compared to two profile geometries found in literature. Two alternative geometries are proposed that offer much lower adhesive wear while respecting the size constraints of the published profiles and are thought to be an improvement in design.

A new methodology for surcharge risk management in urban areas (case study: Gonbad-e-Kavus city)

2016 ◽  
Vol 75 (4) ◽  
pp. 823-832 ◽  
Author(s):  
Farhad Hooshyaripor ◽  
Jafar Yazdi
Keyword(s):  
Urban Areas ◽  
Pareto Front ◽  
Low Impact Development ◽  
Optimal Solution ◽  
Curve Number ◽  
Hydrologic Model ◽  
Storm Event ◽  
Nsga Ii ◽  
Urban Flood ◽  
Conflicting Objectives

This research presents a simulation-optimization model for urban flood mitigation integrating Non-dominated Sorting Genetic Algorithm (NSGA-II) with Storm Water Management Model (SWMM) hydraulic model under a curve number-based hydrologic model of low impact development technologies in Gonbad-e-Kavus, a small city in the north of Iran. In the developed model, the best performance of the system relies on the optimal layout and capacity of retention ponds over the study area in order to reduce surcharge from the manholes underlying a set of storm event loads, while the available investment plays a restricting role. Thus, there is a multi-objective optimization problem with two conflicting objectives solved successfully by NSGA-II to find a set of optimal solutions known as the Pareto front. In order to analyze the results, a new factor, investment priority index (IPI), is defined which shows the risk of surcharging over the network and priority of the mitigation actions. The IPI is calculated using the probability of pond selection for candidate locations and average depth of the ponds in all Pareto front solutions. The IPI can help the decision makers to arrange a long-term progressive plan with the priority of high-risk areas when an optimal solution has been selected.

A One to Multiple Order Allocation Optimization Model of Horizontal Manufacturing Collaborative Alliance

2012 ◽  
Vol 201-202 ◽  
pp. 996-999
Author(s):  
Jin Gao
Keyword(s):  
Optimization Model ◽  
Optimal Solution ◽  
Multi Objective Optimization ◽  
Order Allocation ◽  
Nsga Ii ◽  
Manufacturing Enterprises ◽  
Proposed Model ◽  
Optimal Solution Set ◽  
Simulation Results ◽  
Enterprise Community

Horizontal manufacturing collaborative alliance is a dispersed enterprise community consisting of several enterprises which produce the same kind of products. To correctly assign order among member companies of horizontal manufacturing collaborative alliance is one of the most important ways to improve the agility and competitiveness of manufacturing enterprises. For the order allocation problem, a multi-objective optimization model is developed to minimize the comprehensive cost and balance the production loads among the selected manufacturing enterprises. Non-dominated sorting genetic algorithm (NSGA-II) is applied to solve the optimization functions. The optimal solution set of Pareto is obtained. The simulation results indicate that the proposed model and algorithm is able to obtain satisfactory solutions.

An optimization model for workgroup-based repetitive scheduling

2006 ◽  
Vol 33 (9) ◽  
pp. 1172-1194 ◽  
Author(s):  
Rong-yau Huang ◽  
Kuo-Shun Sun
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Construction Projects ◽  
Optimal Solution ◽  
Planning And Scheduling ◽  
Construction Scheduling ◽  
Chromosome Representation ◽  
Scheduling Method ◽  
Model Formation ◽  
System Project

Most construction repetitive scheduling methods developed so far have been based on the premise that a repetitive project is comprised of many identical production units. Recently, Huang and Sun (2005) developed a workgroup-based repetitive scheduling method that takes the view that a repetitive construction project consists of repetitive activities of workgroups. Instead of repetitive production units, workgroups with repetitive or similar activities in a repetitive project are identified and employed in the planning and scheduling. The workgroup-based approach adds more flexibility to the planning and scheduling of repetitive construction projects and enhances the effectiveness of repetitive scheduling. This work builds on previous research and develops an optimization model for workgroup-based repetitive scheduling. A genetic algorithm (GA) is employed in model formation for finding the optimal or near-optimal solution. A chromosome representation, as well as specification of other parameters for GA analysis, is described in the paper. Two sample case studies, one simple and one sewer system project, are used for model validation and demonstration. Results and findings are reported.Key words: construction scheduling, repetitive project, workgroup, optimization, genetic algorithm.

scholarly journals Hybrid method for achieving Pareto front on economic emission dispatch

2020 ◽  
Vol 10 (4) ◽  
pp. 3358
Author(s):  
Kummari Rajesh ◽  
N. Visali
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Hybrid Method ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Test System ◽  
Swarm Optimization ◽  
Metaheuristic Methods

In this paper hybrid method, Modified Nondominated Sorted Genetic Algorithm (MNSGA-II) and Modified Population Variant Differential Evolution(MPVDE) have been placed in effect in achieving the best optimal solution of Multiobjective economic emission load dispatch optimization problem. In this technique latter, one is used to enforce the assigned percent of the population and the remaining with the former one. To overcome the premature convergence in an optimization problem diversity preserving operator is employed, from the tradeoff curve the best optimal solution is predicted using fuzzy set theory. This methodology validated on IEEE 30 bus test system with six generators, IEEE 118 bus test system with fourteen generators and with a forty generators test system. The solutions are dissimilitude with the existing metaheuristic methods like Strength Pareto Evolutionary Algorithm-II, Multiobjective differential evolution, Multi-objective Particle Swarm optimization, Fuzzy clustering particle swarm optimization, Nondominated sorting genetic algorithm-II.

scholarly journals TỐI ƯU HÓA TIN CẬY VA ĐẬP CỦA ỐNG ĐA TẾ BÀO HÌNH VUÔNG CHỊU TẢI VA ĐẬP XIÊN

2021 ◽  
Vol 45 (03) ◽  
Author(s):  
TRẦN TRỌNG NHÂN
Keyword(s):  
Genetic Algorithm ◽  
Response Surface ◽  
Pareto Front ◽  
Nsga Ii ◽  
Knee Point

Tối ưu hóa tin cậy va đập của những ống hình vuông đa đa tế bào trong trường hợp va đập xiên được nghiên cứu trong bài báo này. Đối với các cấu trúc này, các chỉ số tin cậy va đập SEA và PCF được thu thập bằng cách sử dụng HYPERMESH / LS-DYNA. Pareto front thu được bẳng cách kết hợp response surface (RS) và Non-dominated Sorting Genetic Algorithm II (NSGA-II). Một “giải pháp tốt hơn” (hay còn gọi là knee point) được xác định từ Pareto front. Kết quả của nghiên cứu này là cơ sở tham khảo cho việc thiết kế các cấu trúc đa tế bào có khả năng tin cậy va đập tốt hơn.

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