Solving Multi-Agent Pickup and Delivery Problems Using a Genetic Algorithm

2020 ◽  
pp. 140-153
Author(s):  
Ana Carolina L. C. Queiroz ◽  
Heder S. Bernardino ◽  
Alex B. Vieira ◽  
Helio J. C. Barbosa
Keyword(s):  
Genetic Algorithm ◽  
Pickup And Delivery ◽  
Multi Agent

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.

A Genetic Algorithm-Oriented Model of Agent Persuasion for Multi-agent System Negotiation

2020 ◽  
pp. 281-291
Author(s):  
Samantha Jiménez ◽  
Víctor H. Castillo ◽  
Bogart Yail Márquez ◽  
Arnulfo Alanis ◽  
Leonel Soriano-Equigua ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Multi Agent System ◽  
Agent System ◽  
Multi Agent

scholarly journals Genetic Optimization-Based Consensus Control of Multi-Agent 6-DoF UAV System

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3576 ◽  
Author(s):  
Aws Najm ◽  
Ibraheem Ibraheem ◽  
Ahmad Azar ◽  
Amjad Humaidi
Keyword(s):  
Genetic Algorithm ◽  
Optimization Technique ◽  
Switching Topology ◽  
Control Law ◽  
Genetic Optimization ◽  
Multi Agent System ◽  
Control Parameters ◽  
Consensus Control ◽  
Communication Topology ◽  
Multi Agent

A consensus control law is proposed for a multi-agent system of quadrotors with leader–follower communication topology for three quadrotor agents. The genetic algorithm (GA) is the proposed optimization technique to tune the consensus control parameters. The complete nonlinear model is used without any further simplifications in the simulations, while simplification in the model is used to theoretically design the controller. Different case studies and tests are done (i.e., trajectory tracking formation and switching topology) to show the effectiveness of the proposed controller. The results show good performance in all tests while achieving the consensus of the desired formations.

A Hybrid Approach for Shape Retrieval Using Genetic Algorithms and Approximate Distance

2021 ◽  
pp. 205-222
Author(s):  
Saliha Mezzoudj ◽  
Kamal Eddine Melkemi
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Hybrid Approach ◽  
Shape Context ◽  
Similar Shape ◽  
Complex Shapes ◽  
Ga Algorithm ◽  
Approximate Distance ◽  
Multi Agent ◽  
Part Structure

This article describes how the classical algorithm of shape context (SC) is still unable to capture the part structure of some complex shapes. To overcome this insufficiency, the authors propose a novel shape-based retrieval approach that is called HybMAS-GA using a multi-agent system (MAS) and a genetic algorithm (GA). They define a new distance called approximate distance (AD) to define a SC method by AD, which called approximate distance shape context (ADSC) descriptor. Furthermore, the authors' proposed HybMAS-GA is a star architecture where all shape context agents, N, are directly linked to a coordinator agent. Each retrieval agent must perform either a SC or an ADSC method to obtain a similar shape, started from its own initial configuration of sample points. This combination increases the efficiency of the proposed HybMAS-GA algorithm and ensures its convergence to an optimal images retrieval as it is shown through experimental results.

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