Evolutionary Optimisation of Kernel and Hyper-Parameters for SVM

2008 ◽  
pp. 107-116
Author(s):  
Laura Dioşan ◽  
Alexandrina Rogozan ◽  
Jean-Pierre Pécuchet
Keyword(s):  
Evolutionary Optimisation

scholarly journals Multi-Mode Wave Energy Converter Design Optimisation Using an Improved Moth Flame Optimisation Algorithm

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3737
Author(s):  
Mehdi Neshat ◽  
Nataliia Sergiienko ◽  
Seyedali Mirjalili ◽  
Meysam Majidi Nezhad ◽  
Giuseppe Piras ◽  
...  
Keyword(s):  
Wave Energy ◽  
Wave Energy Converter ◽  
Total Power ◽  
Energy Converter ◽  
Wave Regime ◽  
Optimisation Algorithm ◽  
Wide Range ◽  
Evolutionary Optimisation ◽  
Optimisation Methods ◽  
Optimisation Algorithms

Ocean renewable wave power is one of the more encouraging inexhaustible energy sources, with the potential to be exploited for nearly 337 GW worldwide. However, compared with other sources of renewables, wave energy technologies have not been fully developed, and the produced energy price is not as competitive as that of wind or solar renewable technologies. In order to commercialise ocean wave technologies, a wide range of optimisation methodologies have been proposed in the last decade. However, evaluations and comparisons of the performance of state-of-the-art bio-inspired optimisation algorithms have not been contemplated for wave energy converters’ optimisation. In this work, we conduct a comprehensive investigation, evaluation and comparison of the optimisation of the geometry, tether angles and power take-off (PTO) settings of a wave energy converter (WEC) using bio-inspired swarm-evolutionary optimisation algorithms based on a sample wave regime at a site in the Mediterranean Sea, in the west of Sicily, Italy. An improved version of a recent optimisation algorithm, called the Moth–Flame Optimiser (MFO), is also proposed for this application area. The results demonstrated that the proposed MFO can outperform other optimisation methods in maximising the total power harnessed from a WEC.

Performative computational architecture using swarm and evolutionary optimisation: A review

2019 ◽  
Vol 147 ◽  
pp. 356-371 ◽  
Author(s):  
Berk Ekici ◽  
Cemre Cubukcuoglu ◽  
Michela Turrin ◽  
I. Sevil Sariyildiz
Keyword(s):  
Computational Architecture ◽  
Evolutionary Optimisation

scholarly journals The development of an advanced composite structure using evolutionary design methods

2009 ◽  
Author(s):  
◽  
David Van Wyk
Keyword(s):  
Composite Materials ◽  
Composite Structure ◽  
Composite Structures ◽  
Empirical Method ◽  
Optimum Shape ◽  
Evolutionary Design ◽  
Structural Optimisation ◽  
Design For Manufacture ◽  
Advanced Composite ◽  
Evolutionary Optimisation

The development of an evolutionary optimisation method and its application to the design of an advanced composite structure is discussed in this study. Composite materials are increasingly being used in various fields, and so optimisation of such structures would be advantageous. From among the various methods available, one particular method, known as Evolutionary Structural Optimisation (ESO), is shown here. ESO is an empirical method, based on the concept of removing and adding material from a structure, in order to create an optimum shape. The objective of the research is to create an ESO method, utilising MSC.Patran/Nastran, to optimise composite structures. The creation of the ESO algorithm is shown, and the results of the development of the ESO algorithm are presented. A tailfin of an aircraft was used as an application example. The aim was to reduce weight and create an optimised design for manufacture. The criterion for the analyses undertaken was stress based. Two models of the tailfin are used to demonstrate the effectiveness of the developed ESO algorithm. The results of this research are presented in the study.

SQUEAK AND RATTLE PREVENTION BY GEOMETRIC VARIATION MANAGEMENT USING A TWO-STAGE EVOLUTIONARY OPTIMISATION APPROACH

2021 ◽  
pp. 1-40
Author(s):  
Mohsen Bayani ◽  
Casper Wickman ◽  
Lars Lindkvist ◽  
Rikard Söderberg
Keyword(s):  
Lower Order ◽  
Uniform Space ◽  
Order Effect ◽  
Objective Functions ◽  
Two Stage ◽  
Second Stage ◽  
Evolutionary Optimisation ◽  
Space Filler ◽  
Computationally Expensive ◽  
Geometric Variation

Abstract Squeak and rattle are annoying sounds that are often regarded as the failure indicators by car users. Geometric variation is a key contributor to the generation of squeak and rattle sounds. Optimisation of the connection configuration in assemblies can be a provision to minimise this risk. However, the optimisation process for large assemblies can be computationally expensive. The focus of this work is to propose a two-stage evolutionary optimisation scheme to find the fittest connection configurations that minimise the risk for squeak and rattle. This was done by defining the objective functions as the measured variation and deviation in the rattle direction and the squeak plane. In the first stage, the location of the fasteners primarily contributing to the rattle direction measures are identified. In the second stage, fasteners primarily contributing to the squeak plane measures are added to the fittest configuration from phase one. It was assumed that the fasteners from the squeak group plane have a lower-order effect on the rattle direction measures, compared to the fasteners from the rattle direction group. This assumption was falsified for a set of simplified geometries. Also, a new uniform space filler algorithm was introduced to efficiently generate an inclusive and feasible starting population for the optimisation process by incorporating the problem constraints in the algorithm. For two industrial cases, it was shown that by using the proposed two-stage optimisation scheme the variation and deviation measures in critical interfaces for squeak and rattle improved compared to the baseline results.

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