On stopping criteria in verified nonlinear systems or optimization algorithms

2000 ◽  
Vol 26 (3) ◽  
pp. 373-389 ◽  
Author(s):  
R. B. Kearfott ◽  
G. W. Walster
Keyword(s):  
Nonlinear Systems ◽  
Optimization Algorithms ◽  
Stopping Criteria

scholarly journals Optimal control computation for nonlinear systems with state-dependent stopping criteria

Automatica ◽  
2012 ◽  
Vol 48 (9) ◽  
pp. 2116-2129 ◽  
Author(s):  
Qun Lin ◽  
Ryan Loxton ◽  
Kok Lay Teo ◽  
Yong Hong Wu
Keyword(s):  
Optimal Control ◽  
Nonlinear Systems ◽  
Stopping Criteria ◽  
State Dependent

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

Identification of Continuous-time Nonlinear Systems via Local Gaussian Process Models

2014 ◽  
Vol 134 (11) ◽  
pp. 1708-1715
Author(s):  
Tomohiro Hachino ◽  
Kazuhiro Matsushita ◽  
Hitoshi Takata ◽  
Seiji Fukushima ◽  
Yasutaka Igarashi
Keyword(s):  
Nonlinear Systems ◽  
Gaussian Process ◽  
Continuous Time ◽  
Process Models ◽  
Gaussian Process Models

Modeling of Nonlinear Systems using Genetic Algorithm

2012 ◽  
Vol 132 (6) ◽  
pp. 913-918 ◽  
Author(s):  
Kayoko Hayashi ◽  
Toru Yamamoto ◽  
Kazuo Kawada
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Systems
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