An Immunological Algorithm for Global Numerical Optimization

2006 ◽  
pp. 284-295 ◽  
Author(s):  
Vincenzo Cutello ◽  
Giuseppe Narzisi ◽  
Giuseppe Nicosia ◽  
Mario Pavone
Keyword(s):  
Numerical Optimization ◽  
Global Numerical Optimization

ONE IMPROVED AGENT GENETIC ALGORITHM — RING-LIKE AGENT GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION

2009 ◽  
Vol 26 (04) ◽  
pp. 479-502 ◽  
Author(s):  
BIN LIU ◽  
TEQI DUAN ◽  
YONGMING LI
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Genetic Operators ◽  
Global Numerical Optimization ◽  
Candidate Solution ◽  
Low Dimension ◽  
Multi Agent ◽  
Agent Structure

In this paper, a novel genetic algorithm — dynamic ring-like agent genetic algorithm (RAGA) is proposed for solving global numerical optimization problem. The RAGA combines the ring-like agent structure and dynamic neighboring genetic operators together to get better optimization capability. An agent in ring-like agent structure represents a candidate solution to the optimization problem. Any agent interacts with neighboring agents to evolve. With dynamic neighboring genetic operators, they compete and cooperate with their neighbors, and they can also use knowledge to increase energies. Global numerical optimization problems are the most important ones to verify the performance of evolutionary algorithm, especially of genetic algorithm and are mostly of interest to the corresponding researchers. In the corresponding experiments, several complex benchmark functions were used for optimization, several popular GAs were used for comparison. In order to better compare two agents GAs (MAGA: multi-agent genetic algorithm and RAGA), the several dimensional experiments (from low dimension to high dimension) were done. These experimental results show that RAGA not only is suitable for optimization problems, but also has more precise and more stable optimization results.

A decremental stochastic fractal differential evolution for global numerical optimization

2016 ◽  
Vol 372 ◽  
pp. 470-491 ◽  
Author(s):  
Noor H. Awad ◽  
Mostafa Z. Ali ◽  
Ponnuthurai N. Suganthan ◽  
Edward Jaser
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Global Numerical Optimization

scholarly journals Erratum to: Incorporating mutation scheme into krill herd algorithm for global numerical optimization

2013 ◽  
Vol 24 (5) ◽  
pp. 1231-1231 ◽  
Author(s):  
Gaige Wang ◽  
Lihong Guo ◽  
Heqi Wang ◽  
Hong Duan ◽  
Luo Liu ◽  
...  
Keyword(s):  
Numerical Optimization ◽  
Global Numerical Optimization ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Mutation Scheme

Discovering Promising Regions to Help Global Numerical Optimization Algorithms

2007 ◽  
pp. 72-82 ◽  
Author(s):  
Vinícius V. de Melo ◽  
Alexandre C. B. Delbem ◽  
Dorival L. Pinto Júnior ◽  
Fernando M. Federson
Keyword(s):  
Numerical Optimization ◽  
Optimization Algorithms ◽  
Global Numerical Optimization
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