Pinhole-imaging-based learning butterfly optimization algorithm for global optimization and feature selection

2021 ◽  
Vol 103 ◽  
pp. 107146
Author(s):  
Wen Long ◽  
Jianjun Jiao ◽  
Ximing Liang ◽  
Tiebin Wu ◽  
Ming Xu ◽  
...  
Keyword(s):  
Feature Selection ◽  
Global Optimization ◽  
Optimization Algorithm ◽  
Pinhole Imaging

Novel self-adjusted particle swarm optimization algorithm for feature selection

Computing ◽  
2021 ◽  
Author(s):  
Bo Wei ◽  
Xuan Wang ◽  
Xuewen Xia ◽  
Mingfeng Jiang ◽  
Zuohua Ding ◽  
...  
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization

scholarly journals A Hybrid Whale Optimization Algorithm for Global Optimization

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1477
Author(s):  
Chun-Yao Lee ◽  
Guang-Lin Zhuo
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Whale Optimization Algorithm ◽  
Initial Population ◽  
Test Functions ◽  
Benchmark Test ◽  
Thermal Exchange ◽  
Whale Optimization ◽  
Benchmark Test Functions

This paper proposes a hybrid whale optimization algorithm (WOA) that is derived from the genetic and thermal exchange optimization-based whale optimization algorithm (GWOA-TEO) to enhance global optimization capability. First, the high-quality initial population is generated to improve the performance of GWOA-TEO. Then, thermal exchange optimization (TEO) is applied to improve exploitation performance. Next, a memory is considered that can store historical best-so-far solutions, achieving higher performance without adding additional computational costs. Finally, a crossover operator based on the memory and a position update mechanism of the leading solution based on the memory are proposed to improve the exploration performance. The GWOA-TEO algorithm is then compared with five state-of-the-art optimization algorithms on CEC 2017 benchmark test functions and 8 UCI repository datasets. The statistical results of the CEC 2017 benchmark test functions show that the GWOA-TEO algorithm has good accuracy for global optimization. The classification results of 8 UCI repository datasets also show that the GWOA-TEO algorithm has competitive results with regard to comparison algorithms in recognition rate. Thus, the proposed algorithm is proven to execute excellent performance in solving optimization problems.

scholarly journals Chaos-enhanced moth-flame optimization algorithm for global optimization

2019 ◽  
Vol 30 (6) ◽  
pp. 1144-1159 ◽  
Author(s):  
Hongwei LI ◽  
Jianyong LIU ◽  
Liang CHEN ◽  
Jingbo BAI ◽  
Yangyang SUN ◽  
...  
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Moth Flame Optimization Algorithm

DEFORMABLE MESH FOR AUTOMATED SURFACE EXTRACTION FROM NOISY IMAGES

2004 ◽  
Vol 04 (03) ◽  
pp. 405-432 ◽  
Author(s):  
JUSSI TOHKA ◽  
JOUNI M. MYKKÄNEN
Keyword(s):  
Global Optimization ◽  
Internal Energy ◽  
Optimization Algorithm ◽  
Automatic Segmentation ◽  
Medical Image Analysis ◽  
Surface Extraction ◽  
Deformable Surface ◽  
Surface Models ◽  
Deformable Mesh ◽  
Parameter Values

Surface extraction from noisy volumetric images is a problem commonly encountered in medical image analysis. Deformable surface models can, in principle, solve the problem in an automatic manner. However, it is often essential that a reasonably close initialization and good parameter values for deformable models are provided. In this paper, novel algorithms for global minimization of the energy of deformable meshes are presented. We demonstrate that global optimization by these algorithms reduces the sensitivity of the deformable mesh to its initialization and its parameter values. Consequently, it becomes easier to automate the initialization process and the selection of parameter values. As the second contribution, the internal energy function is derived in a novel way in the framework of deformable surface models. The construction of the internal energy in this way features a simple way to derive the variants of our global optimization algorithm. The experiments with synthetic images are performed to compare variants of the proposed optimization algorithm. Also, we present a practical application of our deformable model to automatic segmentation of positron emission tomography images.

Sign in / Sign up

Export Citation Format

Share Document