Simultaneous Optimization of Actuator Placement and Structural Parameters by Mathematical and Genetic Optimization Algorithms

2001 ◽  
pp. 255-263
Author(s):  
G. Locatelli ◽  
H. Langer ◽  
M. Müller ◽  
H. Baier
Keyword(s):  
Structural Parameters ◽  
Optimization Algorithms ◽  
Simultaneous Optimization ◽  
Genetic Optimization ◽  
Actuator Placement

scholarly journals Robust Optimum Design of Multiple Tuned Mass Dampers for Vibration Control in Buildings Subjected to Seismic Excitation

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Luciara Silva Vellar ◽  
Sergio Pastor Ontiveros-Pérez ◽  
Letícia Fleck Fadel Miguel ◽  
Leandro Fleck Fadel Miguel
Keyword(s):  
Vibration Control ◽  
Structural Parameters ◽  
Simultaneous Optimization ◽  
Tuned Mass Dampers ◽  
Structural Dynamic ◽  
Energy Devices ◽  
Promising Tool ◽  
Optimization Methodology ◽  
Robust Optimum Design ◽  
Multiple Tuned Mass Dampers

Passive energy devices are well known due to their performance for vibration control in buildings subjected to dynamic excitations. Tuned mass damper (TMD) is one of the oldest passive devices, and it has been very much used for vibration control in buildings around the world. However, the best parameters in terms of stiffness and damping and the best position of the TMD to be installed in the structure are an area that has been studied in recent years, seeking optimal designs of such device for attenuation of structural dynamic response. Thus, in this work, a new methodology for simultaneous optimization of parameters and positions of multiple tuned mass dampers (MTMDs) in buildings subjected to earthquakes is proposed. It is important to highlight that the proposed optimization methodology considers uncertainties present in the structural parameters, in the dynamic load, and also in the MTMD design with the aim of obtaining a robust design; that is, a MTMD design that is not sensitive to the variations of the parameters involved in the dynamic behavior of the structure. For illustration purposes, the proposed methodology is applied in a 10-story building, confirming its effectiveness. Thus, it is believed that the proposed methodology can be used as a promising tool for MTMD design.

Integrated Design of Compliant Mechanisms and Embedded Rotary Actuators and Bending Actuators for Motion Generation

2016 ◽  
Author(s):  
Lin Cao ◽  
Wenjun (Chris) Zhang
Keyword(s):  
Compliant Mechanisms ◽  
Optimization Technique ◽  
Integrated Design ◽  
Simultaneous Optimization ◽  
Motion Generation ◽  
Element Analysis ◽  
Integrated Design Approach ◽  
3D Printed ◽  
Checking Scheme ◽  
Actuator Placement

This paper presents an integrated design approach, a new topology optimization technique, to simultaneously synthesizing the optimal structural topologies of compliant mechanisms (CMs) and actuator placement — bending actuators and rotary actuators — for motion generation. The approach has the following salient features: (1) the use of bending actuators and rotary actuators as the actuation of CMs, (2) the simultaneous optimization of the CM and the location and orientation of the actuator that is embedded in the CM, (3) the guiding of a flexible link from an initial configuration to a series of desired configurations (including precision positions, orientations, and shapes), and (4) a new connectivity checking scheme to check whether the regions of interest in a design candidate are well connected. A program was employed for the geometrically nonlinear finite element analysis of large-displacement CMs driven by either bending actuators or rotary actuators. Two design examples were presented to demonstrate the proposed approach. The design results were 3D printed, and they all achieved desired shape changes when actuated.

Multi-Objective Optimization Formulation to Make Creative Teams in Teamology

2010 ◽  
Author(s):  
Masao Arakawa
Keyword(s):  
Optimization Algorithms ◽  
Project Based Learning ◽  
Project Teams ◽  
Genetic Optimization ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Learning Teams ◽  
Optimization Formulation

Teamology is established by Prof. Wilde to make creative teams in project teams. As a first step, it needs questionnaires to characterize personality for each member who joins the projects. Assume in academic project based learning teams, a number of students join and we are going to make several teams. Each team should have the same potential if and only if we can make every team as that. In order to create these teams, we need to quantify students’ characters, and we need to formalize them to meet the guideline of Teamology. In this study, we are going to make multi-objective optimization formulation of Teamology, and show an example of team making by using a genetic optimization algorithms with data that was taken PBL course in Kagawa University.

Meta-heuristic optimization algorithms for simultaneous optimization of sidelobe level and directivity of uniformly excited concentric ring array antennas

2019 ◽  
Vol 12 (2) ◽  
pp. 183-192
Author(s):  
Kailash Pati Dutta ◽  
G. K. Mahanti
Keyword(s):  
Search Algorithm ◽  
Optimization Algorithms ◽  
Beam Width ◽  
Simultaneous Optimization ◽  
Heuristic Optimization ◽  
The Novel ◽  
Sidelobe Level ◽  
Concentric Ring ◽  
Array Antennas ◽  
Symbiotic Organism Search

AbstractThis paper proposes the novel application of three meta-heuristic optimization algorithms namely crow search algorithm, moth flame optimization, and symbiotic organism search algorithm for the synthesis of uniformly excited multiple concentric ring array antennas. The objective of this work is to minimize the sidelobe level (SLL) and maximize the peak directivity simultaneously. Three different cases are demonstrated separately with various constraints such as optimal inter-element spacing and/or optimal ring radii. Comparative study of the algorithms using common parameters such as SLL, directivity, first null beam width, best cost, and run time has been reported. Investigation results prove the superiority of case 3 over other cases in terms of directivity and SLL. This work demonstrates the potential of these algorithms.

scholarly journals Application of particle swarm optimization and genetic algorithm for optimization of a southern Iranian oilfield

2021 ◽  
Vol 11 (4) ◽  
pp. 1781-1796
Author(s):  
Milad Razghandi ◽  
Aliakbar Dehghan ◽  
Reza Yousefzadeh
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Simultaneous Optimization ◽  
Injection Wells ◽  
Swarm Optimization ◽  
Operational Conditions ◽  
Water Alternating Gas ◽  
Decision Variables

AbstractOptimization of the placement and operational conditions of oil wells plays an important role in the development of the oilfields. Several automatic optimization algorithms have been used by different authors in recent years. However, different optimizers give different results depending on the nature of the problem. In the current study, a comparison between the genetic algorithm and particle swarm optimization algorithms was made to optimize the operational conditions of the injection and production wells and also to optimize the location of the injection wells in a southern Iranian oilfield. The current study was carried out with the principal purpose of evaluating and comparing the performance of the two most used optimization algorithms for field development optimization on real-field data. Also, a comparison was made between the results of sequential and simultaneous optimization of the decision variables. Net present value of the project was used as the objective function, and the two algorithms were compared in terms of the profitability incremental added to the project over twelve years. First, the production rate of the producers was optimized, and then water alternating gas injection wells were added to the field at locations determined by engineering judgment. Afterward, the location, injection rate, and water alternating gas ratio of the injectors were optimized sequentially using the two algorithms. Next, the production rate of the producers was optimized again. Finally, a simultaneous optimization was done in two manners to evaluate its effect on the optimization results: simultaneous optimization of the last two steps and simultaneous optimization of all decision variables. Results showed the positive effect of the algorithms on the profitability of the project and superiority of the particle swarm optimization over the genetic algorithm at every stage. Also, simultaneous optimization was beneficial at finiding better results compared to sequential optimization approach. In the end, a sensitivity analysis was made to specify the most influencing decision variable on the project’s profitability.

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