Multi-objective collaborative optimization in cement calcination process: A time domain rolling optimization method based on Jaya algorithm

2021 ◽  
Vol 105 ◽  
pp. 117-128
Author(s):  
Xiaochen Hao ◽  
Yong Gao ◽  
Xunian Yang ◽  
Junwei Wang
Keyword(s):  
Time Domain ◽  
Optimization Method ◽  
Collaborative Optimization ◽  
Jaya Algorithm ◽  
Calcination Process ◽  
Multi Objective ◽  
Rolling Optimization

Multidisciplinary design optimization of stiffened panels using collaborative optimization and artificial neural network

2017 ◽  
Vol 232 (20) ◽  
pp. 3595-3611 ◽  
Author(s):  
Hamda Chagraoui ◽  
Mohamed Soula
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Problem ◽  
Optimization Method ◽  
Collaborative Optimization ◽  
System Level ◽  
Stiffened Panels ◽  
Multi Objective ◽  
Efficiency And Effectiveness ◽  
Artificial Neural

A new method for solving the multidisciplinary design optimization problems with a minimal computational effort is presented. The proposed methodology is based on the combination of artificial neural network model and Improved Multi-Objective Collaborative Optimization. In the artificial neural network–Improved Multi-Objective Collaborative Optimization scheme, the back-propagation algorithm is used for training the artificial neural network metamodel and the Non-dominated Sorting Genetic Algorithm-II is used to search a Pareto optimality set for the objective functions of stiffened panels. The artificial neural network–Improved Multi-Objective Collaborative Optimization algorithm aims firstly to decompose the global optimization problem hierarchically into optimization design problem at system level and several sub-problems at sub-system level and secondly to replace each optimization problem at the system and subsystem levels by artificial neural network model to limit the computational cost. To highlight the efficiency and effectiveness of the proposed artificial neural network–Improved Multi-Objective Collaborative Optimization method, mathematical and engineering examples are presented. Results obtained from the application of the artificial neural network–Improved Multi-Objective Collaborative Optimization approach to an optimization problem of a stiffened panel are compared with those obtained by traditional optimization without using prediction tools. The new method (artificial neural network–Improved Multi-Objective Collaborative Optimization) was proven to be superior to traditional optimization. These results have confirmed the efficiency and effectiveness of the artificial neural network–Improved Multi-Objective Collaborative Optimization method. In addition, it converges at faster rate than traditional optimization. The traditional optimization method converges within 7918 s, while artificial neural network–Improved Multi-Objective Collaborative Optimization requires only 42 s, clearly, the artificial neural network–Improved Multi-Objective Collaborative Optimization method is much more efficient.

Multi-Objective Optimization Using Asynchronous Distributed Applications

2004 ◽  
Vol 126 (5) ◽  
pp. 767-774 ◽  
Author(s):  
Alessandro Giassi ◽  
Fouad Bennis ◽  
Jean-Jacques Maisonneuve
Keyword(s):  
Concurrent Engineering ◽  
Optimization Method ◽  
Distributed Applications ◽  
Asynchronous Communication ◽  
Collaborative Optimization ◽  
Multi Objective Optimization ◽  
Communication Tool ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Optimization Loop

In the context of concurrent engineering, this paper presents a quite innovative approach to the collaborative optimization process, which couples a multi-objective genetic algorithm with an asynchronous communication tool. This optimization method allows the collaborative and multi-sites design to be performed without requiring significant investments or changes in the company organization. To illustrate this methodology, the collaboration of three European companies on the optimization of a ship hull is described. The hull shape is automatically optimised distributing the elements of the optimization loop among three distant sites. Our study demonstrates that when multi-objective optimization is carried out in a distributed manner it can provide a powerful tool for concurrent product design.

scholarly journals Research on a Bi-Level Collaborative Optimization Method for Planning and Operation of Multi-Energy Complementary Systems

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7930
Author(s):  
Changrong Liu ◽  
Hanqing Wang ◽  
Zhiqiang Liu ◽  
Zhiyong Wang ◽  
Sheng Yang
Keyword(s):  
Optimization Theory ◽  
Optimization Method ◽  
Collaborative Optimization ◽  
Energy Utilization ◽  
Energy Structure ◽  
Optimization Strategy ◽  
Multi Objective ◽  
Multi Objective Programming ◽  
Practical Engineering ◽  
Planning And Operation

Multi-energy complementary systems (MCSs) are complex multilevel systems. In the process of system planning, many aspects—such as power planning, investment cost, and environmental impact—should be considered. However, different decision makers tend to have different levels of control objectives, and the multilevel problems of the system need to be solved effectively with comprehensive judgment. Therefore, based on the terminal MCS energy structure model, the optimization method of MCS planning and operation coordination, considering the influence of planning and operation in the system’s life cycle, is studied in this paper. Consequently, the research on the collaborative optimization strategy of MCS construction and operation was carried out based on the bi-level multi-objective optimization theory in this paper. Considering the mutual restraint and correlation between system construction and operation in practical engineering, a bi-level optimization model for collaborative optimization of MCS construction and operation was constructed. To solve the model effectively, the existing non-dominated sorting genetic algorithm III (NSGA-III) was improved by the authors on the basis of previous research, which could enhance the global search ability and convergence speed of the algorithm. To effectively improve and strengthen the reliability of energy supply, and increase the comprehensive energy utilization of the system, the effects of carbon transaction cost and renewable energy penetration were considered in the optimization process. Based on an engineering example, the bi-level model was solved and analyzed. It should be noted that the optimization results of the model were verified to be applicable and effective by comparison with the single-level multi-objective programming optimization. The findings of this paper could provide theoretical reference and practical guidance for the planning and operation of MCSs, making them significant for social application.

APPLICATION OF IMPROVED COLLABORATIVE OPTIMIZATION METHOD TO MECHANICAL MULTI-OBJECTIVE OPTIMAL DESIGN PROBLEM

2012 ◽  
Vol 11 (02) ◽  
pp. 151-157 ◽  
Author(s):  
FENGTAO WEI ◽  
LI SONG ◽  
YAN LI ◽  
KUN SHI
Keyword(s):  
Optimal Design ◽  
Efficient Method ◽  
Design Problem ◽  
Optimization Method ◽  
Collaborative Optimization ◽  
Flow Chart ◽  
Optimal Design Problem ◽  
Design Problems ◽  
Multi Objective ◽  
Result Show

In order to solve the mechanical multi-objective optimal design problems, the basic idea and flow chart of collaborative optimization method are introduced in this paper. In view of the shortcomings that exist in standard collaborative optimization method, this method has been improved by applying the dynamic slack factor method. Taking a mechanical multi-objective optimal design of spring as an example, the multi-objective optimal design problem has been solved by the improved collaborative optimization method. The process and result show that the improved collaborative optimization method has higher accuracy and efficiency. This paper has provided an efficient method to solve the complicated mechanical multi-objective optimal design problems.

Student Admission Assesment using Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA)

2018 ◽  
Author(s):  
Rivalri Kristianto Hondro ◽  
Mesran Mesran ◽  
Andysah Putera Utama Siahaan
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Selection Process ◽  
Initial Step ◽  
Optimization Method ◽  
Ratio Analysis ◽  
Multi Objective Optimization ◽  
Prospective Students ◽  
Multi Objective

Procurement selection process in the acceptance of prospective students is an initial step undertaken by private universities to attract superior students. However, sometimes this selection process is just a procedural process that is commonly done by universities without grouping prospective students from superior students into a class that is superior compared to other classes. To process the selection results can be done using the help of computer systems, known as decision support systems. To produce a better, accurate and objective decision result is used a method that can be applied in decision support systems. Multi-Objective Optimization Method by Ratio Analysis (MOORA) is one of the MADM methods that can perform calculations on the value of criteria of attributes (prospective students) that helps decision makers to produce the right decision in the form of students who enter into the category of prospective students superior.

scholarly journals Optimized stable gait planning of biped robot using multi-objective evolutionary JAYA algorithm

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142097634
Author(s):  
Huan Tran Thien ◽  
Cao Van Kien ◽  
Ho Pham Huy Anh
Keyword(s):  
Inverse Kinematics ◽  
Biped Robot ◽  
Step Length ◽  
Jaya Algorithm ◽  
Kinetic Chain ◽  
Biped Walking ◽  
Gait Planning ◽  
Multi Objective ◽  
Simulation And Experiment ◽  
Stable Gait

This article proposes a new stable biped walking pattern generator with preset step-length value, optimized by multi-objective JAYA algorithm. The biped robot is modeled as a kinetic chain of 11 links connected by 10 joints. The inverse kinematics of the biped is applied to derive the specified biped hip and feet positions. The two objectives related to the biped walking stability and the biped to follow the preset step-length magnitude have been fully investigated and Pareto optimal front of solutions has been acquired. To demonstrate the effectiveness and superiority of proposed multi-objective JAYA, the results are compared to those of MO-PSO and MO-NSGA-2 optimization approaches. The simulation and experiment results investigated over the real small-scaled biped HUBOT-4 assert that the multi-objective JAYA technique ensures an outperforming effective and stable gait planning and walking for biped with accurate preset step-length value.

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