Multi-objective Collaborative Optimization Research Articles

Multi-objective Collaborative Optimization for the Lightweight Design of an Electric Bus Body Frame

To analyze the rollover safety, finite element models were established for the electric bus body frame, rollover simulation platform, living space, and bus rollover. The strength and stiffness of the body frame were calculated under four typical working conditions considering the main low-order elastic modal characteristics. The results indicate that the initial body frame of the electric bus satisfies the required structural strength, stiffness, modes, and rollover safety, and it has great potential for lightweight design. Sensitivity and structural contribution analyses were performed to determine the design variables for lightweight optimization of the body frame, and a mathematical model was established for multi-objective collaborative optimization design of the electric bus. Then, the radial basis function neural network was used to approximate the optimization model. Besides, the accuracy of the approximate model was verified, and the non-dominated sorting genetic algorithm II was employed to determine solutions for the lightweight optimization. Compared with the initial model, the mass of the optimized model is reduced by 240 kg (9.0%) without any changes in the materials of the body frame.

Inverse simulation to optimize the rib-profile in a rectangular flow-channel

Abstract An inverse optimization is performed for the shape (profile) of a rib-element that is located within a rectangular flow-channel. The optimization is useful to improve not only heat transfer but also to reduce the pressure drop of the channel. A simplified conjugate-gradient method and a multi-objective collaborative optimization method were used to find a profile inversion simulation model, where the shape is constantly updated until the performance evaluation factor reaches a maximum. It is found that inversion-optimization can effectively improve the overall performance of the system when the area of a rib base is given. For different heights and different shapes, the simulation always produces the same optimal solution. Furthermore, when the number of points reaches a certain number, the shape does not change nearly. One specific profile, which is obtained for Re = 8000, performs always better than the triangular rib surface, even though it does not represent the ideal shape for all cases. Finally, an additional optimization is carried out for different Reynold numbers. For the final optimized geometry, as the Reynold number increases from 3000 to 30,000, the objective function increases from 1.942% to 11.79%.

Collaborative Optimization of Density and Surface Roughness of 316L Stainless Steel in Selective Laser Melting.

Although the concept of additive manufacturing has been proposed for several decades, momentum in the area of selective laser melting (SLM) is finally starting to build. In SLM, density and surface roughness, as the important quality indexes of SLMed parts, are dependent on the processing parameters. However, there are few studies on their collaborative optimization during SLM to obtain high relative density and low surface roughness simultaneously in the literature. In this work, the response surface method was adopted to study the influences of different processing parameters (laser power, scanning speed and hatch space) on density and surface roughness of 316L stainless steel parts fabricated by SLM. A statistical relationship model between processing parameters and manufacturing quality is established. A multi-objective collaborative optimization strategy considering both density and surface roughness is proposed. The experimental results show that the main effects of processing parameters on the density and surface roughness are similar. We observed that the laser power and scanning speed significantly affected the above objective quality, but the influence of the hatch spacing was comparatively low. Based on the above optimization, 316L stainless steel parts with excellent surface roughness and relative density can be obtained by SLM with optimized processing parameters.

Performance-based multi-objective collaborative optimization of steel frames with fuse-oriented buckling-restrained braces

A multi-objective collaborative optimal design procedure for steel frames equipped with buckling-restrained braces (BRBs) is proposed under the framework of performance-based seismic design (PBSD) in order to minimize the damage of the primary structure as well as the material cost. For this purpose, a so-called BRB energy dissipation ratio is defined and introduced in the optimization to characterize the involvement of fuse-type BRBs in hysteretic energy dissipation in building stories. Three groups of constraints obtained from the rules on geometrical and conceptual design, the requirements regarding strength and stability, and the PBSD-based story drift limits are considered. To address the discrete-continuous hybrid design variables, a hybrid coding scheme is proposed to modify the non-dominated sorting genetic algorithm II (NSGA-II). The rationality of the proposed procedure is demonstrated by a case study on a seven-story planar steel frame with BRBs. The demonstration indicates that the proposed optimization procedure can make BRBs play the role of a structural fuse successfully in steel frames. The BRB energy dissipation ratios of case C are distributed nearly uniformly along the structural height. The weak-beam-strong-column design principle can be satisfactorily achieved through the maximization of the story BRB energy dissipation ratios. Additionally, the modified hybrid coding NSGA-II algorithm is computationally efficient and stable.

Dynamic parameters optimization of straddle-type monorail vehicles based multiobjective collaborative optimization algorithm

ABSTRACTThe dynamic parameters of Straddle-type monorail vehicles with single-axle bogies are optimised by utilising the multi-body dynamic method. The dynamic parameter optimisation is conducted aiming at the curve negotiation properties, comfort and tire eccentric wear of monorail vehicles. The optimisation model is established with software ADAMS and modeFRONTIER and the Sobol is adopted for sensitivity analysis to acquire the optimised parameters. Finally, the multiobjective collaborative optimisation algorithm is utilised for optimised analysis. According to the results, the proposed method can conduct multi-objective optimisation to the monorail vehicles in a successful way. The curve negotiation properties and comfort are improved, and the tire wear is reduced with dynamic parameters after optimisation. Especially, a conversion strategy of the consistent objective function is proposed aiming at multiobjective nonlinear convergence.

面源污染最佳管理措施多目标协同优化配置研究进展

PDF HTML阅读 XML下载 导出引用 引用提醒 面源污染最佳管理措施多目标协同优化配置研究进展 DOI: 10.5846/stxb201804140860 作者: 作者单位: 生态环境部环境与经济政策研究中心,生态环境部环境与经济政策研究中心,生态环境部环境与经济政策研究中心,生态环境部环境与经济政策研究中心,生态环境部环境与经济政策研究中心 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金青年科学基金项目（41601551）；第二次全国污染源普查项目《农业源污染物入水体负荷核算方法及系数体系构建》（2110399）；环保部第三批城环总规试点项目（YGCQ-GGQY-201418） A review: multi-objective collaborative optimization of best management practices for non-point sources pollution control Author: Affiliation: Policy Research Center for Environment and Economy, Ministry of Ecology and Environment Protection, P.R.China,,Policy Research Center for Environment and Economy, Ministry of Ecology and Environment Protection, P.R.China,, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:随着点源污染逐渐得到有效控制，面源污染逐渐成为我国多数地区影响水环境质量安全的主要因素。推广实施最佳管理措施（Best Management Practices，BMPs）被认为是控制面源污染的有效途径。受到区域种植制度、耕作方式、政策以及经济成本等因素的影响，导致流域尺度配置BMPs存在一定的困难，特别是随着流域空间尺度的变化，会进一步加大BMPs配置难度，使得BMPs的配置工作变为了一项多目标决策优化问题，即如何在有限的成本投入下，实现水环境质量改善的目标。需要在不同空间尺度下对流域BMPs进行多目标协同优化配置。从面源污染关键源区识别、BMPs削减效率评估以及BMPs多目标协同优化模拟3个方面对面源污染BMPs多目标协同优化配置研究进行了综述。结果表明：1）包含地块尺度和流域尺度的多尺度模型耦合系统的构建，将是实现关键源区精准识别的有效途径；2）BMPs削减效率对水质改善响应的滞后性、不确定性、时空异质性、污染物形态转换风险等均是今后BMPs削减效率评估中需要重点解决的关键问题；3）建立流域污染物负荷削减量与水质改善之间的非线性响应关系，并以此为基础将BMPs组合数据库、成本数据库以及基于进化算法的的优化配置方案进行耦合，进而构建多目标决策支持系统，以获取BMPs空间优化配置方案以及多目标成本-效益最优曲线。 Abstract:With point source pollution gradually being controlled, non-point source pollution has become a threat for water quality in most regions of China. Best management practices (BMPs) have been regarded as the most effective way to control non-point source pollution. However, the effectiveness of regional cropping systems, cultivation methods, policies, and economic costs lead to difficulties in BMP allocation at the watershed scale. In particular, the difficulties further increase with the changes at spatial scale. As a result, the allocation of BMPs has been moved to a multi-objective decision optimization program, to achieve water quality improvement targets under limited inputs. Therefore, there is a need for multi-objective collaborative optimization of BMPs at different spatial scales. Herein, we reviewed the current research pertaining to multi-objective collaborative optimization of BMPs for non-point source pollution with respect to three aspects: identify the critical source areas (CSAs) of non-point source pollution, assessment of BMPs cutting efficiency, and imitate multi-objective collaborative optimization of BMPs. The results indicate that: i) building multi-scale model coupling system, including land scale and watershed scale, would be the most efficient way to accurately identify CSAs; ii) reducing time lag, uncertainty, and spatial and temporal heterogeneity as well as the risk of pollution to improve water quality will be the key to the cutting efficiency of BMPs; iii) building a nonlinear response relation between watershed pollutant reduction and water quality improvement is essential. The BMP database, cost database, and scientific allocation schemes based on evolutionary algorithm (EA) can be combined to build a multi-scale decision supporting system. The allocation scheme of BMPs and the optimum curve of multi-scale cost-effectiveness can then be acquired. 参考文献 相似文献 引证文献

Operation Optimization Technology of Integrated Energy System Considering Energy Storage and Automatic Demand Response

To solve the inefficient operation problem of integrated energy systems, a multi-objective collaborative optimization operation of integrated energy system considering automatic demand response and energy storage was constructed in this paper. And the model is solved by Tent mapping chaos optimization NSGA - II algorithm, applied to the actual example of a typical energy system in a typical park in China. The simulation results show that the integrated energy systems that consider automatic demand response and energy storage have significant economic, technical and environmental benefits compared to the other three scenarios, facilitating the integration of new energy.

An efficient metamodel-based multi-objective multidisciplinary design optimization framework

This paper presents an efficient metamodel-based multi-objective multidisciplinary design optimization (MDO) architecture for solving multi-objective high fidelity MDO problems. One of the important features of the proposed method is the development of an efficient surrogate model-based multi-objective particle swarm optimization (EMOPSO) algorithm, which is integrated with a computationally efficient metamodel-based MDO architecture. The proposed EMOPSO algorithm is based on sorted Pareto front crowding distance, utilizing star topology. In addition, a constraint-handling mechanism in non-domination appointment and fuzzy logic is also introduced to overcome feasibility complexity and rapid identification of optimum design point on the Pareto front. The proposed algorithm is implemented on a metamodel-based collaborative optimization architecture. The proposed method is evaluated and compared with existing multi-objective optimization algorithms such as multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II), using a number of well-known benchmark problems. One of the important results observed is that the proposed EMOPSO algorithm provides high diversity with fast convergence speed as compared to other algorithms. The proposed method is also applied to a multi-objective collaborative optimization of unmanned aerial vehicle wing based on high fidelity models involving structures and aerodynamics disciplines. The results obtained show that the proposed method provides an effective way of solving multi-objective multidisciplinary design optimization problem using high fidelity models.

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

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.

A robust multi-objective and multi-physics optimization of multi-physics behavior of microstructure

A new strategy is presented to solve robust multi-physics multi-objective optimization problem known as improved multi-objective collaborative optimization (IMOCO) and its extension improved multi-objective robust collaborative (IMORCO). In this work, the proposed IMORCO approach combined the IMOCO method, the worst possible point (WPP) constraint cuts and the Genetic algorithm NSGA-II type as an optimizer in order to solve the robust optimization problem of multi-physics of microstructures with uncertainties. The optimization problem is hierarchically decomposed into two levels: a microstructure level, and a disciplines levels. For validation purposes, two examples were selected: a numerical example, and an engineering example of capacitive micro machined ultrasonic transducers (CMUT) type. The obtained results are compared with those obtained from robust non-distributed and distributed optimization approach, non-distributed multi-objective robust optimization (NDMORO) and multi-objective collaborative robust optimization (McRO), respectively. Results obtained from the application of the IMOCO approach to an optimization problem of a CMUT cell have reduced the CPU time by 44% ensuring a Pareto front close to the reference non-distributed multi-objective optimization (NDMO) approach (mahalanobis distance, DM2 =0.9503 and overall spread, So=0.2309). In addition, the consideration of robustness in IMORCO approach applied to a CMUT cell of optimization problem under interval uncertainty has reduced the CPU time by 23% keeping a robust Pareto front overlaps with that obtained by the robust NDMORO approach (DM2 =10.3869 and So=0.0537).

Multi-objective collaborative optimization using linear physical programming with dynamic weight

The multi-objective collaborative optimization problem with multi-objective subsystems has a bi-level optimization architecture, that consists of the system and subsystem levels. Combining the multi-objective optimization algorithm with a bi-level optimization structure can obtain a satisfactory solution. Given that the preference-based algorithm requires minimal running time, the Linear physical programming (LPP) method, one of the typical preference-based algorithms, is adopted. Considering that setting the preference values for the incompatibility function is difficult, the weighted incompatibility function is added to the piecewise linear function of the LPP model. An expression of dynamic weight is also presented according to the inconsistency among the subsystems, which is caused by the sharing and auxiliary variables relative to the different subsystems. Using an engineering example, this study reveals that the interdisciplinary consistency is satisfactory when the dynamic weight is used in the LPP model, which thereby demonstrates the effectiveness of the presented method.

A goal-programming enhanced collaborative optimization approach to reducing lifecyle costs for naval vessels

Understanding the trade-offs involved in assessing lifetime cost for engineering systems requires understanding trends in various engineering disciplines that require significantly different analysis methods to efficiently explore. The corresponding design spaces can be flat, defined by weak minima, and thus difficult to understand using traditixonal optimization methods. This paper presents a new multi-disciplinary framework that uses a goal-programming enhanced multi-objective collaborative optimization (eMOCO) approach to facilitate the development of the spaces. In order to further increase its efficiency in discrete or flat spaces well-suited to evolutionary optimization a unique discipline level genetic algorithm is proposed. Naval vessels are an example of an engineering system that has a difficult design space with respect to lifetime cost, however, one where it is critical to understand. As these costs are increasing, they are becoming limiting factors in a vessel's operational life. Though they are so important, the interaction between different cost categories such as production and operation has not been explored in depth and is not always clear. Understanding the trade-offs between different aspects of a vessel's total ownership costs early in the design stage can aid in the production of new ships where they are minimized. The proposed framework is verified on mathematical problems, and then used to develop trade-spaces between resistance and production for a nominal naval combatant vessel. These trade-spaces show both the knowledge gained by designers in understanding these trade-offs and the ability of the proposed eMOCO framework to develop them effectively.

Design and application of lightweight multi-objective collaborative optimization for a parametric body-in-white structure

The static bending and torsional stiffnesses, the lower-order modal frequencies of a body-in-white structure and the full frontal-crash and side-impact passive safety performances are simulated with finite element models which are generated on the basis of the implicit parametric model. The implicit parametric model is established through SFE CONCEPT software. The simulation results are compared with tests to validate the simulation analysis results. It is proposed that the multi-objective optimization is divided into non-safety parts optimization, frontal-crash safety parts optimization and side-impact safety parts optimization, which is computationally more efficient than optimizing the non-safety parts, the frontal-crash safety parts and the side-impact safety parts simultaneously. In this paper, the lightweight multi-objective collaborative optimization design of the body-in-white structure is conducted for a passenger car by optimizing the thickness, the beam section shape and the size; while maintaining the performances of the static bending and torsional stiffnesses, the lower-order modal frequency decreases to less than 5% of the initial value, and the full-frontal-crash and side-impact passive safety performances remain almost the same. Structural modifications are applied by means of implicit parametric technology, providing changes in the geometry in a fully controllable manner. After comparison between the optimized body-in-white structure and the initial structure, the mass decreased in total by 32.41 kg (i.e. by as high as 7.63%). The decreases in the performances of the bending and torsional stiffnesses are less than 2.54%; the bending and torsional frequencies increased a little, and the frontal-crash and side-impact passive safety performances underwent almost no change.

Research on Lightweight Multi-Objective Optimization for Closed Body-in-White Structure

The 100% frontal crash and side impact performances of a passenger car are analyzed and compared with tests. The structural optimization of the Closed Body-in-White (BIW) is divided into two stages which are 100% frontal crash safe part optimization and side impact safe part optimization. Use the Optimal Latin hypercube (Opt LHD) design method to generate sample points. Take the Radial Basis Functions (RBF) neural network method to establish optimization approximation model. The non-dominated sorting genetic algorithm (NSGA-II) was used to conduct multi-objective collaborative optimization design. The results show that the total mass of the closed BIW is reduced 9.745kg; the light weight rate was 10.27%. The Crashworthiness performance of the closed BIW does not change obviously.

A Land-use Spatial Allocation Model Based on Modified Ant Colony Optimization

Land-use spatial allocation is a multi-objective collaborative spatial optimization methodfor rational use of the land use. Based on global search capabilities and the information feedbackmechanism of ant colony optimization (ACO), a land-use spatial allocation model (ACO-LA) isproposed. FirstlyFirst, a construction graph is built for modeling the land-use spatial allocationproblem. SecondlySecond, the behaviors of artificial ants are improved so that the solution can befoundobtained quickly in the searchingsearch space. Finally, the ant colony generates optimizedsolutions by reconciling the conflicts between different planning objectives or by setting the relativedominance of different land-use types. Our study focuses on Gaoqiao Town of Fuyang City inZhejiang Province, China. The model maximizes the land-use suitability and spatial compactness,and minimizes the cost of changing the land use, based on a variety of constraints, e.g., the optimalland-use structure and land-use policies. The results suggest that this model can obtain an optimizedland-use spatial pattern from different sets of sub-objective weights and different developmentscenarios. With the constraint of the land-use structure, the land-use types can be distributed morereasonably by different sets of sub-objective weights. In different development scenarios, the modelencourageencourages areas of land-use types in line with the development direction, adapting tomeet different development needs by setting the relative dominance of the different land-use types,Wdominance, which is added to the component selection probability Pij.

Multi-Objective Collaborative Optimization of Spring

A framework for solving the multi-objective optimization problems of spring in multidisciplinary design environment is advised in this paper. Based on the collaborative optimization (CO) algorithm, a new system level objective function is advised to minimize relative value among the structural mass, the height and the natural vibration frequency. The proposed models were demonstrated with a multi-objective optimization problem of a spring. The optimal design of the spring obtained indicates the great potential of decreasing structural mass and vibration level and increasing natural frequency reserve under the constraints. The analysis progress and results show that the model is feasible and well-suited for using in actual optimization problems of spring design.

Collaborative Optimization of Excavator Boom Mechanism Parameters under Multiple Loading Conditions

On the base of explaining Multi-Objective Optimization Problems (MOOP) and Collaborative Optimization Techniques (COT), this paper puts forward an original and efficient approach—Collaborative Optimization Technique for Multi-Objective Optimization Problems (COTMOOP)—which combines Collaborative Optimization (CO) techniques and Evolutionary Algorithms (EAs). It has been developed so as to facilitate the organizations and increase the performances of complex coupling systems design and optimization. The newly method has been succeeded in applying to the design and optimization of excavator boom mechanism that is a complex coupling system. The optimal solutions can prove that the approach is efficient and practical.

New Approximation Assisted Multi-objective collaborative Robust Optimization (new AA-McRO) under interval uncertainty

Existing collaborative optimization techniques with multiple coupled subsystems are predominantly focused on single-objective deterministic optimization. However, many engineering optimization problems have system and subsystems that can each be multi-objective, constrained and with uncertainty. The literature reports on a few deterministic Multi-objective Multi-Disciplinary Optimization (MMDO) techniques. However, these techniques in general require a large number of function calls and their computational cost can be exacerbated when uncertainty is present. In this paper, a new Approximation-Assisted Multi-objective collaborative Robust Optimization (New AA-McRO) under interval uncertainty is presented. This new AA-McRO approach uses a single-objective optimization problem to coordinate all system and subsystem multi-objective optimization problems in a Collaborative Optimization (CO) framework. The approach converts the consistency constraints of CO into penalty terms which are integrated into the system and subsystem objective functions. The new AA-McRO is able to explore the design space better and obtain optimum design solutions more efficiently. Also, the new AA-McRO obtains an estimate of Pareto optimum solutions for MMDO problems whose system-level objective and constraint functions are relatively insensitive (or robust) to input uncertainties. Another characteristic of the new AA-McRO is the use of online approximation for objective and constraint functions to perform system robustness evaluation and subsystem-level optimization. Based on the results obtained from a numerical and an engineering example, it is concluded that the new AA-McRO performs better than previously reported MMDO methods.

Multi-objective collaborative multidisciplinary design optimization using particle swarm techniques and fuzzy decision making

This article focuses on the efficient multi-objective particle swarm optimization algorithm to solve multidisciplinary design optimization problems. The objective is to extend the formulation of collaborative optimization which has been widely used to solve single-objective optimization problems. To examine the proposed structure, racecar design problem is taken as an example of application for three objective functions. In addition, a fuzzy decision maker is applied to select the best solution along the pareto front based on the defined criteria. The results are compared to the traditional optimization, and collaborative optimization formulations that do not use multi-objective particle swarm optimization. It is shown that the integration of multi-objective particle swarm optimization into collaborative optimization provides an efficient framework for design and analysis of hierarchical multidisciplinary design optimization problems.

Multi-Objective Collaborative Optimization Based on Evolutionary Algorithms

This paper proposes a novel multi-objective collaborative optimization (MOCO) approach based on multi-objective evolutionary algorithms for complex systems with multiple disciplines and objectives, especially for those systems in which most of the disciplinary variables are shared. The shared variables will conflict when the disciplinary optimizers are implemented concurrently. In order to avoid the confliction, the shared variables are treated as fixed parameters at the discipline level in most of the MOCO approaches. But in this paper, a coordinator is introduced to handle the confliction, which allocates more design freedom and independence to the disciplinary optimizers. A numerical example is solved, and the results are discussed.