Research on Solving Nonlinear Integer Programming Based on Multi-agent Genetic Algorithm

2009 ◽  
Author(s):  
Huadong Chen ◽  
Shuzong Wang ◽  
Hangyu Wang
Keyword(s):  
Genetic Algorithm ◽  
Integer Programming ◽  
Nonlinear Integer Programming ◽  
Multi Agent

Study of Solving Crossing Origin-Destination Matrix Based on Entropy Maximizing Model

2012 ◽  
Vol 182-183 ◽  
pp. 970-974
Author(s):  
Guo Jiang Fu
Keyword(s):  
Genetic Algorithm ◽  
Integer Programming ◽  
Maximum Entropy ◽  
Nonlinear Equations ◽  
Lagrange Multiplier ◽  
Programming Model ◽  
Nonlinear Integer Programming ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Flow Matrix

The maximum-entropy model is one of important methods in estimating traffic origin-destination matrix from observed traffic link flows, and it is a nonlinear integer programming model. To find the best solution, traditionally it was transformed to solve nonlinear equations by the introduction of Lagrange multiplier and Newton’s method is adopted to solve the nonlinear equations. In this paper, a entropy maximizing model to estimate the crossing origin-destination flow matrix from in-out flows is given, a genetic algorithm is proposed to solve the model and the introduction of Lagrange multiplier is avoid. A practical example showed the validity of the genetic algorithm.

scholarly journals A GENETIC APPROACH FOR LONG TERM VIRTUAL ORGANIZATION DISTRIBUTION

2011 ◽  
Vol 20 (02) ◽  
pp. 271-295 ◽  
Author(s):  
VÍCTOR SÁNCHEZ-ANGUIX ◽  
SOLEDAD VALERO ◽  
ANA GARCÍA-FORNES
Keyword(s):  
Genetic Algorithm ◽  
Virtual Organization ◽  
Genetic Material ◽  
Multi Agent System ◽  
Crossover Operator ◽  
Agent Based ◽  
Multi Agent ◽  
Global Goal ◽  
Computational Resources

An agent-based Virtual Organization is a complex entity where dynamic collections of agents agree to share resources in order to accomplish a global goal or offer a complex service. An important problem for the performance of the Virtual Organization is the distribution of the agents across the computational resources. The final distribution should provide a good load balancing for the organization. In this article, a genetic algorithm is applied to calculate a proper distribution across hosts in an agent-based Virtual Organization. Additionally, an abstract multi-agent system architecture which provides infrastructure for Virtual Organization distribution is introduced. The developed genetic solution employs an elitist crossover operator where one of the children inherits the most promising genetic material from the parents with higher probability. In order to validate the genetic proposal, the designed genetic algorithm has been successfully compared to several heuristics in different scenarios.

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.

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