scholarly journals Grey Wolf Algorithm and Multi-Objective Model for the Manycast RSA Problem in EONs

Information ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 398
Author(s):  
Hejun Xuan ◽  
Lidan Lin ◽  
Lanlan Qiao ◽  
Yang Zhou
Keyword(s):  
Optical Networks ◽  
Optimization Model ◽  
High Performance ◽  
Uniform Design ◽  
Optimization Method ◽  
Research Field ◽  
Grey Wolf ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
High Efficient

Manycast routing and spectrum assignment (RSA) in elastic optical networks (EONs) has become a hot research field. In this paper, the mathematical model and high efficient algorithm to solve this challenging problem in EONs is investigated. First, a multi-objective optimization model, which minimizes network power consumption, the total occupied spectrum, and the maximum index of used frequency spectrum, is established. To handle this multi-objective optimization model, we integrate these three objectives into one by using a weighted sum strategy. To make the population distributed on the search domain uniformly, a uniform design method was developed. Based on this, an improved grey wolf optimization method (IGWO), which was inspired by PSO (Particle Swarm Optimization, PSO) and DE (Differential Evolution, DE), is proposed to solve the maximum model efficiently. To demonstrate high performance of the designed algorithm, a series of experiments are conducted using several different experimental scenes. Experimental results indicate that the proposed algorithm can obtain better results than the compared algorithm.

A Multi-Objective Optimization Method for Thin-Walled Tube NC Bending

2011 ◽  
Vol 213 ◽  
pp. 383-387
Author(s):  
Jie Xu ◽  
He Yang ◽  
Heng Li
Keyword(s):  
Uniform Design ◽  
Optimization Method ◽  
Maximum Height ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Thin Walled Tube ◽  
Design Values ◽  
Thin Walled ◽  
Aluminum Alloy Tube ◽  
Nc Bending

A multi-objective optimization method for thin-walled tube NC bending is presented. Firstly, a half-symmetry 3D elastic-plastic FEM model is established based on the initial design values, applying the dynamic explicit code ABAQUS/Explicit. Secondly, virtual orthogonal arrays are designed to optimize friction coefficients, with minimizing the maximum wall-thinning ratio, the maximum cross section distortion ratio and the maximum height of wrinkling waves as the multi-objectives. Lastly, the mandrel radius is optimized by sequential quadratic programming with approximate regressive models fit from uniform design values in the allowed range. Application is put forward for Ф50×1×100 (tube outside diameter ×tube wall thickness × central line bending radius) and Ф100×1.5×200 aluminum alloy tube bending. It is proved that the forming quality has been improved by the method.

scholarly journals Multi-objective optimization of traffic signal timing using non-dominated sorting artificial bee colony algorithm for unsaturated intersections

2018 ◽  
Vol 46 (2) ◽  
pp. 85-97 ◽  
Author(s):  
Hongxing Zhao ◽  
Ruichun He ◽  
Jiangsheng Su
Keyword(s):  
Optimization Model ◽  
Artificial Bee Colony ◽  
Optimization Method ◽  
Signal Timing ◽  
Multi Objective Optimization ◽  
Abc Algorithm ◽  
Multi Objective ◽  
Bee Colony ◽  
Vehicle Delay ◽  
Single Objective

Vehicle delay and stops at intersections are considered targets for optimizing signal timing for an isolated intersection to overcome the limitations of the linear combination and single objective optimization method. A multi-objective optimization model of a fixed-time signal control parameter of unsaturated intersections is proposed under the constraint of the saturation level of approach and signal time range. The signal cycle and green time length of each phase were considered decision variables, and a non-dominated sorting artificial bee colony (ABC) algorithm was used to solve the multi-objective optimization model. A typical intersection in Lanzhou City was used for the case study. Experimental results showed that a single-objective optimization method degrades other objectives when the optimized objective reaches an optimal value. Moreover, a reasonable balance of vehicle delay and stops must be achieved to flexibly adjust the signal cycle in a reasonable range. The convergence is better in the non-dominated sorting ABC algorithm than in non-dominated sorting genetic algorithm II, Webster timing, and weighted combination methods. The proposed algorithm can solve the Pareto front of a multi-objective problem, thereby improving the vehicle delay and stops simultaneously.

scholarly journals A Novel Multi-Efficiency Optimization Method for Disassembly Line Balancing Problem

2019 ◽  
Vol 11 (24) ◽  
pp. 6969 ◽  
Author(s):  
Jianhua Cao ◽  
Xuhui Xia ◽  
Lei Wang ◽  
Zelin Zhang ◽  
Xiang Liu
Keyword(s):  
Optimization Model ◽  
Optimization Method ◽  
High Energy ◽  
Line Balancing ◽  
Multi Objective Optimization ◽  
Automotive Engine ◽  
Multi Objective ◽  
Proposed Model ◽  
Disassembly Line Balancing ◽  
Disassembly Line

Disassembly is an indispensable part in remanufacturing process. Disassembly line balancing and disassembly mode have direct effects on the disassembly efficiency and resource utilization. Recent researches about disassembly line balancing problem (DLBP) either considered the highest productivity, lowest disassembly cost or some other performance measures. No one has considered these metrics comprehensively. In practical production, ignoring the ratio of resource input and value output within remanufacturing oriented disassembly can result in inefficient or pointless remanufacturing operations. To address the problem, a novel multi-efficiency DLBP optimization method is proposed. Different from the conventional DLBP, destructive disassembly mode is considered not only on un-detachable parts, but also on detachable parts with low value, high energy consumption, and long task time. The time efficiency, energy efficiency, and value efficiency are newly defined as the ultimate optimization objectives. For the characteristics of the multi-objective optimization model, a dual-population discrete artificial bee colony algorithm is proposed. The proposed model and algorithm are validated by different scales examples and applied to an automotive engine disassembly line. The results show that the proposed model is more efficient, and the algorithm is well suited to the multi-objective optimization model.

scholarly journals Design of AC-DC Grid Connected Converter using Multi-Objective Optimization

2014 ◽  
Vol 5 (1) ◽  
pp. 11-19 ◽  
Author(s):  
Szymon Piasecki ◽  
Robert Szmurlo ◽  
Marek Jasinski
Keyword(s):  
Design Procedure ◽  
Optimization Method ◽  
Theoretical Research ◽  
Multi Objective Optimization ◽  
Performance Indices ◽  
Short Introduction ◽  
Multi Objective ◽  
High Efficient ◽  
Optimization Parameters ◽  
And Performance

Abstract Power electronic circuits, in particular AC-DC converters are complex systems, many different parameters and objectives have to be taken into account during the design process. Implementation of Multi-Objective Optimization (MOO) seems to be attractive idea, which used as designer supporting tool gives possibility for better analysis of the designed system. This paper presents a short introduction to the MOO applied in the field of power electronics. Short introduction to the subject is given in section I. Then, optimization process and its elements are briefly described in section II. Design procedure with proposed optimization parameters and performance indices for AC-DC Grid Connected Converter (GCC) interfacing distributed systems is introduced in section III. Some preliminary optimization results, achieved on the basis of analytical and simulation study, are shown at each stage of designing process. Described optimization parameters and performance indices are part of developed global optimization method dedicated for ACDC GCC introduced in section IV. Described optimization method is under development and only short introduction and basic assumptions are presented. In section V laboratory prototype of high efficient and compact 14 kVA AC-DC converter is introduced. The converter is elaborated based on performed designing and optimization procedure with the use of silicon carbide (SiC) power semiconductors. Finally, the paper is summarized and concluded in section VI. In presented work theoretical research are conducted in parallel with laboratory prototyping e.g. all theoretical ideas are verified in laboratory using modern DSP microcontrollers and prototypes of the ACDC GCC.

Study on Cutting Parameters Optimization Based on Uniform Design Method

2010 ◽  
Vol 26-28 ◽  
pp. 764-769
Author(s):  
Deng Wan Li ◽  
Hong Tao Chen ◽  
Ming Heng Xu ◽  
Cheng Ming Zhong
Keyword(s):  
Titanium Alloy ◽  
Optimization Model ◽  
Design Method ◽  
Uniform Design ◽  
Multiple Linear Regression Model ◽  
Cutting Parameters ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Uniform Design Method ◽  
Cutting Parameters Optimization

In order to explore the cutting rules and optimize the cutting parameters of titanium alloy, multiple sets of test parameters were schemed out by using the uniform design method. Test cutting researches with these parameters were conducted under the condition of 12°C dry cutting and -50°C cold blast machining respectively. Through the regression analysis about the results of the test, a multiple linear regression model which is applicable for titanium alloy cutting on its surface roughness and cutting force has been established. The variance analysis shows that it is of remarkable linear relationship. On this basis, a multi-objective optimization model of titanium alloy has been set up. And by means of multi-objective data weighted method, successfully convert the multi-objective optimization model into a single-objective one. Verification tests were done under these cutting parameters, and the results are in good agreement with the calculated.

A Robust Design Optimization Method Considering Correlated Intervals

2019 ◽  
Vol 17 (10) ◽  
pp. 1950079
Author(s):  
Qiong Wang
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Optimization Model ◽  
Optimization Method ◽  
Robust Design Optimization ◽  
System Response ◽  
Multi Objective Optimization ◽  
Convex Model ◽  
Interval Model ◽  
Multi Objective

In the robust design, correlations of uncertain parameters exist widely and have an influence on the results in most cases. It is essential to develop a robust design optimization method considering parametric correlation to future improve the analysis accuracy and engineering applicability. In this paper, a robust design optimization method based on multidimensional parallelepiped convex model is presented. Considering the effects of the interval uncertainties and their correlations, a robust design optimization model considering correlated intervals is established. In the established model, the average performance and robustness of the system response of concern are taken as the design optimization objectives, and the correlations among interval parameters are quantified by integrating the multidimensional parallelepiped convex model. And then, through an independence transforming procedure it can be converted into an independent interval model, which is ultimately converted into a deterministic multi-objective optimization model by using the interval possibility degree to cope with the uncertain constraints. Finally, the deterministic multi-objective optimization model is treated by coupling an efficient micro multi-objective genetic algorithm with the first order Taylor expansion. The feasibility and practicability of the proposed method are demonstrated by the numerical and engineering examples.

scholarly journals Altered Grey Wolf Optimization and Taguchi Method with FEA for Six-Phase Copper Squirrel Cage Rotor Induction Motor Design

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2282 ◽  
Author(s):  
Chih-Hong Lin
Keyword(s):  
Taguchi Method ◽  
High Performance ◽  
Optimization Design ◽  
Input Power ◽  
Grey Wolf ◽  
Multi Objective Optimization ◽  
Grey Wolf Optimization ◽  
Two Phase ◽  
Performance Property ◽  
Multi Objective

This paper presents an altered grey wolf optimization, the Taguchi method, and finite element analysis (FEA) with two-phase multi-objective optimization for the design of a six-phase copper squirrel cage rotor induction motor (SCSCRIM). The multi-objective optimization design with high-performance property aims to achieve lower starting current, lower losses, lower input power, higher efficiency, higher output torque, and higher power factor. The multi-objective optimization design with high-performance property using the altered grey wolf optimization, the Taguchi method, and FEA in the first-phase program is used for minimizing the starting current, stator iron loss, stator copper loss, and input power. The multi-objective optimization design with high-performance property using the altered grey wolf optimization, the Taguchi method, and FEA in the second-phase program is used for maximizing the efficiency, output torque, and power factor. Finally, the proposed skill with higher performances is evaluated and verified via a two-phase program design and some performance tests.

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