Comparison of Enhanced-PSO and Classical Optimization Methods: A Case Study for STATCOM Placement

2009 ◽  
Author(s):  
Yamille del Valle ◽  
Ronald G. Harley ◽  
Ganesh K. Venayagamoorthy
Keyword(s):  
Optimization Methods ◽  
Classical Optimization

scholarly journals Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3385
Author(s):  
Erickson Puchta ◽  
Priscilla Bassetto ◽  
Lucas Biuk ◽  
Marco Itaborahy Filho ◽  
Attilio Converti ◽  
...  
Keyword(s):  
Pid Controller ◽  
Optimization Algorithms ◽  
Optimization Methods ◽  
Buck Converter ◽  
Algebraic Solution ◽  
Nonlinear Load ◽  
Bee Colony ◽  
Arduous Task ◽  
Whale Optimization

This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results.

Urban Vehicle Emissions Management under Uncertainty - A Traffic Planning Model with Interval-Parameter Programming

2013 ◽  
Vol 864-867 ◽  
pp. 1586-1591
Author(s):  
Hong Liang Zhang
Keyword(s):  
Vehicle Emissions ◽  
Optimization Methods ◽  
Transportation System ◽  
Programming Method ◽  
Planning Model ◽  
Traffic Planning ◽  
Interval Parameter ◽  
Interval Linear ◽  
Virtual City

In this study, an interval-parameter programming method has been used for urban vehicle emissions management under uncertainty. The model improves upon the existing optimization methods with advantages in uncertainty reflection, system costs and limitation of emission. Moreover, the model is applied to a case study of urban vehicle emissions management in a virtual city. The results indicate that the interval linear traffic planning model can effectively reduce the vehicles emission and provide strategies for authorities to deal with problems of transportation system.

scholarly journals Metaheuristic techniques comparison to optimize robotic end-effector behavior and its workspace

2018 ◽  
Vol 15 (5) ◽  
pp. 172988141880113
Author(s):  
Miguel Angel Funes Lora ◽  
Edgar Alfredo Portilla-Flores ◽  
Raul Rivera Blas ◽  
Emmanuel Alejandro Merchán Cruz ◽  
Manuel Faraón Carbajal Romero
Keyword(s):  
Numerical Technique ◽  
Cuckoo Search ◽  
Optimization Methods ◽  
Metaheuristic Algorithms ◽  
End Effector ◽  
Bee Colony ◽  
Ill Posed ◽  
Metaheuristic Techniques

Many robots are dedicated to replicate trajectories recorded manually; the recorded trajectories may contain singularities, which occur when positions and/or orientations are not achievable by the robot. The optimization of those trajectories is a complex issue and classical optimization methods present a deficient performance when solving them. Metaheuristics are well-known methodologies for solving hard engineering problems. In this case, they are applied to obtain alternative trajectories that pass as closely as possible to the original one, reorienting the end-effector and displacing its position to avoid the singularities caused by limitations of inverse kinematics equations, the task, and the workspace. In this article, alternative solutions for an ill-posed problem concerning the behavior of the robotic end-effector position and orientation are proposed using metaheuristic algorithms such as cuckoo search, differential evolution, and modified artificial bee colony. The case study for this work considers a three-revolute robot (3R), whose trajectories can contain or not singularities, and an optimization problem is defined to minimize the objective function that represents the error of the alternative trajectories. A tournament in combination with a constant of proportionality allows the metaheuristics to modify the gradual orientation and position of the robot when a singularity is present. Consequently, the procedure selects from all the possible elbow-configurations the best that fits the trajectory. A classical numerical technique, Newton’s method, is used to compare the results of the applied metaheuristics, to evaluate their quality. The results of this implementation indicate that metaheuristic algorithms are an efficient tool to solve the problem of optimizing the end-effector behavior, because of the quality of the alternative trajectory produced.

Identification of the Optimal Product Configuration and Parameters for Improving Manufacturability

1996 ◽  
Author(s):  
Deyi Xue
Keyword(s):  
Global Optimization ◽  
Optimization Methods ◽  
Product Configuration ◽  
Design System ◽  
Optimization Approach ◽  
Continuous Variables ◽  
Integer Variables ◽  
System A ◽  
Global Optimization Methods

Abstract A global optimization approach for identifying the optimal product configuration and parameters is proposed to improve manufacturability measures including feasibility, cost, and time of production. Different product configurations, including alternative design candidates and production processes, are represented by an AND/OR graph. Product parameters are described by variables including continuous variables, integer variables, Boolean variables, and discrete variables. Two global optimization methods, genetic algorithm and simulated annealing, are employed for identifying the optimal product configuration and parameters. The introduced approach serves as a key component in an integrated concurrent design system. A case study example is given to show how the proposed method is used for solving the engineering problems.

Optimized System-Level Design Methods for NoC-Based Many Core Embedded Systems

2014 ◽  
pp. 150-179
Author(s):  
Haoyuan Ying ◽  
Klaus Hofmann ◽  
Thomas Hollstein
Keyword(s):  
Embedded Systems ◽  
Design Optimization ◽  
Optimization Methods ◽  
Design Methods ◽  
System Level ◽  
System Level Design ◽  
Level Design ◽  
On Chip ◽  
Many Core

Due to the growing demand on high performance and low power in embedded systems, many core architectures are proposed the most suitable solutions. While the design concentration of many core embedded systems is switching from computation-centric to communication-centric, Network-on-Chip (NoC) is one of the best interconnect techniques for such architectures because of the scalability and high communication bandwidth. Formalized and optimized system-level design methods for NoC-based many core embedded systems are desired to improve the system performance and to reduce the power consumption. In order to understand the design optimization methods in depth, a case study of optimizing many core embedded systems based on 3-Dimensional (3D) NoC with irregular vertical link distribution topology through task mapping, core placement, routing, and topology generation is demonstrated in this chapter. Results of cycle-accurate simulation experiments prove the validity and efficiency of the design methods. Specific to the case study configuration, in maximum 60% vertical links can be saved while maintaining the system efficiency in comparison to full vertical link connection 3D NoCs by applying the design optimization methods.

Afterword

2010 ◽  
pp. 269-272
Author(s):  
E. Parsopoulos Konstantinos ◽  
N. Vrahatis Michael
Keyword(s):  
Optimization Problems ◽  
Optimization Methods ◽  
Special Structure ◽  
Integer Optimization ◽  
Hybrid Schemes ◽  
Research Results ◽  
Global Minimizers ◽  
Classical Optimization

In the previous chapters, we presented the fundamental concepts and variants of PSO, as along with a multitude of recent research results. The reported results suggest that PSO can be a very useful tool for solving optimization problems from different scientific and technological fields, especially in cases where classical optimization methods perform poorly or their application involves formidable technical difficulties due to the problem’s special structure or nature. PSO was capable of addressing continuous and integer optimization problems, handling noisy and multiobjective cases, and producing efficient hybrid schemes in combination with specialized techniques or other algorithms in order to detect multiple (local or global) minimizers or control its own parameters.

scholarly journals Computer-Aided Design of Microfluidic Circuits

2020 ◽  
Vol 22 (1) ◽  
pp. 285-307 ◽  
Author(s):  
Elishai Ezra Tsur
Keyword(s):  
Computer Aided Design ◽  
Integrated Design ◽  
Optimization Methods ◽  
Performance Requirements ◽  
Computer Aided ◽  
Paper Based Microfluidics ◽  
Specification Synthesis ◽  
Description Languages ◽  
Aided Design

Microfluidic devices developed over the past decade feature greater intricacy, increased performance requirements, new materials, and innovative fabrication methods. Consequentially, new algorithmic and design approaches have been developed to introduce optimization and computer-aided design to microfluidic circuits: from conceptualization to specification, synthesis, realization, and refinement. The field includes the development of new description languages, optimization methods, benchmarks, and integrated design tools. Here, recent advancements are reviewed in the computer-aided design of flow-, droplet-, and paper-based microfluidics. A case study of the design of resistive microfluidic networks is discussed in detail. The review concludes with perspectives on the future of computer-aided microfluidics design, including the introduction of cloud computing, machine learning, new ideation processes, and hybrid optimization.

scholarly journals Comparison of different algorithms for simultaneous estimation of multiple parameters in kinetic metabolic models

2010 ◽  
Vol 7 (3) ◽  
Author(s):  
Syed Murtuza Baker ◽  
Kai Schallau ◽  
Björn H. Junker
Keyword(s):  
Parameter Estimation ◽  
Computational Models ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Simultaneous Estimation ◽  
Multiple Parameters ◽  
Reliable Parameter ◽  
Metabolic Models ◽  
Parameter Values

SummaryComputational models in systems biology are usually characterized by a lack of reliable parameter values. This is especially true for kinetic metabolic models. Experimental data can be used to estimate these missing parameters. Different optimization techniques have been explored to solve this challenging task but none has proved to be superior to the other. In this paper we review the problem of parameter estimation in kinetic models. We focus on the suitability of four commonly used optimization techniques of parameter estimation in biochemical pathways and make a comparison between those methods. The suitability of each technique is evaluated based on the ability of converging to a solution within a reasonable amount of time. As most local optimization methods fail to arrive at a satisfactory solution we only considered the global optimization techniques. A case study of the upper part of Glycolysis consisting 15 parameters is taken as the benchmark model for evaluating these methods.

Unit Cell Synthesis for Design of Materials With Targeted Nonlinear Deformation Response

2017 ◽  
Vol 139 (12) ◽  
Author(s):  
Zachary Satterfield ◽  
Neehar Kulkarni ◽  
Georges Fadel ◽  
Gang Li ◽  
Nicole Coutris ◽  
...  
Keyword(s):  
Nonlinear Deformation ◽  
Nonlinear Behavior ◽  
Target Material ◽  
Optimization Methods ◽  
Unit Cell ◽  
Deformation Response ◽  
Unit Cells ◽  
Cell Synthesis ◽  
Future Work

A systematic unit cell synthesis approach is presented for designing metamaterials from a unit cell level, which are made out of linearly elastic constitutive materials to achieve tunable nonlinear deformation characteristics. This method is expected to serve as an alternative to classical Topology Optimization methods (solid isotropic material with penalization or homogenization) in specific cases by carrying out unit cell synthesis and subsequent size optimization (SO). The unit cells are developed by synthesizing elemental components with simple geometries that display geometric nonlinearity under deformation. The idea is to replace the physical nonlinear behavior of the target material by adding geometric nonlinearities associated with the deforming entities and thus, achieve large overall deformations with small linear strains in each deformed entity. A case study is presented, which uses the proposed method to design a metamaterial that mimics the nonlinear deformation behavior of a military tank track rubber pad under compression. Two unit cell concepts that successfully match the nonlinear target rubber compression curve are evaluated. Conclusions and scope for future work to develop the method are discussed.

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