scholarly journals 3-D Topology Optimization of Claw-Pole Alternator Using Gaussian-Basis Function With Global and Local Searches

2020 ◽  
Vol 56 (1) ◽  
pp. 1-4
Author(s):  
Yoshitsugu Otomo ◽  
Hajime Igarashi ◽  
Yuki Hidaka ◽  
Taiga Komatsu ◽  
Masaki Yamada
Keyword(s):  
Topology Optimization ◽  
Basis Function ◽  
Gaussian Basis Function ◽  
Local Searches ◽  
Global And Local

A New Fuzzy Firefly Algorithm with Adaptive Parameters

2017 ◽  
Vol 16 (03) ◽  
pp. 1750017 ◽  
Author(s):  
Tahereh Hassanzadeh ◽  
Mohammad Reza Meybodi ◽  
Masoumeh Shahramirad
Keyword(s):  
Firefly Algorithm ◽  
Optimization Problems ◽  
Function Optimization ◽  
Local Searches ◽  
Adaptive Parameters ◽  
Otsu Method ◽  
Method Evidence ◽  
Global Optima ◽  
Global And Local

Firefly algorithm is a swarm based algorithm that can be used for solving optimization problems. This paper proposed an improved fuzzy adaptive firefly algorithm (FAFA). In the proposed FAFA, a fuzzy system is used to adapt Firefly Algorithm’s parameters in order to improve its ability in global and local searches. Also, we used different fireflies initializing intervals and different iteration numbers to show the algorithm capability to find global optima. Results focus on the two case study categories of function optimization (seven benchmark functions) and presented a novel optimal multilevel thresholding approach for histogram-based image segmentation by using proposed FAFA and Otsu method. Evidence indicates that the optimization results of proposed FAFA approach are so better than the standard FA.

A new method for wavelength interval selection that intelligently optimizes the locations, widths and combinations of the intervals

The Analyst ◽  
2015 ◽  
Vol 140 (6) ◽  
pp. 1876-1885 ◽  
Author(s):  
Bai-Chuan Deng ◽  
Yong-Huan Yun ◽  
Pan Ma ◽  
Chen-Chen Lin ◽  
Da-Bing Ren ◽  
...  
Keyword(s):  
Selection Method ◽  
New Method ◽  
Wavelength Interval ◽  
Local Searches ◽  
Interval Selection ◽  
Global And Local

An interval selection method that combines global and local searches to optimize locations, widths and combinations of the intervals.

Evolving Basis Function Networks for System Identification

2001 ◽  
Vol 5 (4) ◽  
pp. 229-238 ◽  
Author(s):  
Yuehui Chen ◽  
◽  
Shigeyasu Kawaji
Keyword(s):  
Basis Function ◽  
Learning Algorithm ◽  
Search Algorithm ◽  
Random Search ◽  
Parameter Tuning ◽  
Unified Framework ◽  
B Spline ◽  
Gaussian Basis Function ◽  
Structure Adaptation ◽  
Random Search Algorithm

This paper is concerned with learning and optimization of different basis function networks in the aspect of structure adaptation and parameter tuning. Basis function networks include the Volterra polynomial, Gaussian radial, B-spline, fuzzy, recurrent fuzzy, and local Gaussian basis function networks. Based on creation and evolution of the type constrained sparse tree, a unified framework is constructed, in which structure adaptation and parameter adjustment of different basis function networks are addressed using a hybrid learning algorithm combining a modified probabilistic incremental program evolution (MPIPE) and random search algorithm. Simulation results for the identification of nonlinear systems show the feasibility and effectiveness of the proposed method.

2102 Parameter Optimization of Mass Damper using Global and Local Searches

2014 ◽  
Vol 2014.11 (0) ◽  
pp. _2102-1_-_2102-5_
Author(s):  
Toma HASEGAWA ◽  
Makoto OHSAKI ◽  
Seita TSUDA
Keyword(s):  
Parameter Optimization ◽  
Local Searches ◽  
Mass Damper ◽  
Global And Local

scholarly journals Hybridization of Multi-Objective Deterministic Particle Swarm with Derivative-Free Local Searches

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 546 ◽  
Author(s):  
Riccardo Pellegrini ◽  
Andrea Serani ◽  
Giampaolo Liuzzi ◽  
Francesco Rinaldi ◽  
Stefano Lucidi ◽  
...  
Keyword(s):  
Local Search ◽  
Hybrid Algorithm ◽  
Hull Form ◽  
Multi Objective ◽  
Local Searches ◽  
Derivative Free ◽  
Form Design ◽  
Hull Form Design ◽  
Global And Local ◽  
Objective Derivative

The paper presents a multi-objective derivative-free and deterministic global/local hybrid algorithm for the efficient and effective solution of simulation-based design optimization (SBDO) problems. The objective is to show how the hybridization of two multi-objective derivative-free global and local algorithms achieves better performance than the separate use of the two algorithms in solving specific SBDO problems for hull-form design. The proposed method belongs to the class of memetic algorithms, where the global exploration capability of multi-objective deterministic particle swarm optimization is enriched by exploiting the local search accuracy of a derivative-free multi-objective line-search method. To the authors best knowledge, studies are still limited on memetic, multi-objective, deterministic, derivative-free, and evolutionary algorithms for an effective and efficient solution of SBDO for hull-form design. The proposed formulation manages global and local searches based on the hypervolume metric. The hybridization scheme uses two parameters to control the local search activation and the number of function calls used by the local algorithm. The most promising values of these parameters were identified using forty analytical tests representative of the SBDO problem of interest. The resulting hybrid algorithm was finally applied to two SBDO problems for hull-form design. For both analytical tests and SBDO problems, the hybrid method achieves better performance than its global and local counterparts.

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