A Multi-agent Negotiation Model Applied in Multi-objective Optimization

2006 ◽  
pp. 305-314 ◽  
Author(s):  
Chuan Shi ◽  
Jiewen Luo ◽  
Fen Lin
Keyword(s):  
Multi Objective Optimization ◽  
Negotiation Model ◽  
Multi Objective ◽  
Agent Negotiation ◽  
Multi Agent

scholarly journals Multi-agent collaborative search: an agent-based memetic multi-objective optimization algorithm applied to space trajectory design

2011 ◽  
Vol 225 (11) ◽  
pp. 1211-1227 ◽  
Author(s):  
M Vasile ◽  
F Zuiani
Keyword(s):  
Selection Criterion ◽  
Search Space ◽  
Trajectory Design ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Agent Based ◽  
Multi Objective ◽  
Collaborative Search ◽  
Multi Agent ◽  
Space Trajectory Design

This article presents an algorithm for multi-objective optimization that blends together a number of heuristics. A population of agents combines heuristics that aim at exploring the search space both globally and in a neighbourhood of each agent. These heuristics are complemented with a combination of a local and global archive. The novel agent-based algorithm is tested at first on a set of standard problems and then on three specific problems in space trajectory design. Its performance is compared against a number of state-of-the-art multi-objective optimization algorithms that use the Pareto dominance as selection criterion: non-dominated sorting genetic algorithm (NSGA-II), Pareto archived evolution strategy (PAES), multiple objective particle swarm optimization (MOPSO), and multiple trajectory search (MTS). The results demonstrate that the agent-based search can identify parts of the Pareto set that the other algorithms were not able to capture. Furthermore, convergence is statistically better although the variance of the results is in some cases higher.

Multi-Agent Negotiation Paradigm for Agent Selection in B2C E-Commerce

2012 ◽  
pp. 164-186
Author(s):  
Bireshwar Dass Mazumdar ◽  
Swati Basak ◽  
Neelam Modanwal
Keyword(s):  
Cognitive Model ◽  
Multi Agent System ◽  
Agent System ◽  
Negotiation Model ◽  
Negotiation Strategies ◽  
Agent Negotiation ◽  
Customer Relation Management ◽  
Agent Selection ◽  
Multi Agent

Multi agent system (MAS) model has been extensively used in the different tasks of E-Commerce such as customer relation management (CRM), negotiation and brokering. The objective of this paper is to evaluate a seller agent’s various cognitive parameters like capability, trust, and desire. After selecting a best seller agent from ordering queue, it applies negotiation strategies to find the most profitable proposal for both buyer and seller. This mechanism belongs to a semi cooperative negotiation type, and selecting a seller and buyer agent pair using mental and cognitive parameters. This work provides a logical cognitive model, logical negotiation model between buyer agent and selected seller agent.

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