Numerical Techniques in Relaxed Optimization Problems

2006 ◽  
pp. 157-178 ◽  
Author(s):  
Tomáš Roubíček
Keyword(s):  
Optimization Problems ◽  
Numerical Techniques

Numerical Techniques for Optimization Problems with PDE Constraints

2009 ◽  
pp. 191-284
Author(s):  
Matthias Heinkenschloss ◽  
Ronald Hoppe ◽  
Volker Schulz
Keyword(s):  
Optimization Problems ◽  
Numerical Techniques

Numerical Techniques for Optimization Problems with PDE Constraints

2006 ◽  
pp. 585-652
Author(s):  
Matthias Heinkenschloss ◽  
Ronald Hoppe ◽  
Volker Schulz
Keyword(s):  
Optimization Problems ◽  
Numerical Techniques

Numerical Techniques for Nonlinear Lagrangian Dynamical Systems and Their Application

2012 ◽  
Vol 476-478 ◽  
pp. 1513-1516
Author(s):  
Jin Li
Keyword(s):  
Dynamical Systems ◽  
General Framework ◽  
Optimization Problems ◽  
Lagrangian Function ◽  
Lagrangian System ◽  
Numerical Techniques ◽  
Constrained Optimization Problems ◽  
First Order ◽  
Nonlinear Lagrangian ◽  
Special Case

We provide a general framework for solving constrained optimization problems, this framework relies on dynamical systems using a class of nonlinear Lagrangian function, we construct a first order derivatives based and a second order derivatives based differential systems. Under this framework, We show that the exponential Lagrangian system as the special case is discussed.

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.

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