Improving Multi-agent Negotiations Using Multi-Objective PSO Algorithm
نویسندگان
چکیده
Negotiation over limited resources, as a way for the agents to reach agreement, is one of the significant topics in Multi-Agent Systems (MASs). Most of the models proposed for negotiation suffer from different limitations in the number of the negotiation parties and issues as well as some constraining assumptions such as availability of unlimited computational resources and complete information about the participants. In this paper we make an attempt to ease the limitations specified above by means of a distributive agent based mechanism underpinned by Multi-Objective Swarm Optimization (MOPSO), as a fast and effective learning technique to handle the complexity and dynamics of the real-world negotiations. The experimental results of the proposed method reveal its effectiveness and high performance in presence of limited computational resources and tough deadlines.
منابع مشابه
Pareto-optimal Solutions for Multi-objective Optimal Control Problems using Hybrid IWO/PSO Algorithm
Heuristic optimization provides a robust and efficient approach for extracting approximate solutions of multi-objective problems because of their capability to evolve a set of non-dominated solutions distributed along the Pareto frontier. The convergence rate and suitable diversity of solutions are of great importance for multi-objective evolutionary algorithms. The focu...
متن کاملAnalysis of Multi-Robots Transportation with Multi-objective PSO Algorithm in an Artificial Capital Market
In this paper, to analyze the transport of autonomous robots, an artificial Capital market is used. Capital market is considered as a pier which loading and unloading of cargo is done. Autonomous robots load and unload from the ship to the warehouse wharf or vice versa. All the robots have the ability of transporting the loads, but depending on loads and the location of unloading (or loading) a...
متن کاملModeling and Hybrid Pareto Optimization of Cyclone Separators Using Group Method of Data Handling (GMDH) and Particle Swarm Optimization (PSO)
In present study, a three-step multi-objective optimization algorithm of cyclone separators is catered for the design objectives. First, the pressure drop (Dp) and collection efficiency (h) in a set of cyclone separators are numerically evaluated. Secondly, two meta models based on the evolved Group Method of Data Handling (GMDH) type neural networks are regarded to model the Dp and h as the re...
متن کاملSolving Multi-objective Optimal Power Flow Using Modified GA and PSO Based on Hybrid Algorithm
The Optimal Power Flow (OPF) is one of the most important issues in the power systems. Due to the complexity and discontinuity of some parameters of power systems, the classic mathematical methods are not proper for this problem. In this paper, the objective function of OPF is formulated to minimize the power losses of transmission grid and the cost of energy generation and improve the voltage ...
متن کاملIdentifying overlapping communities using multi-agent collective intelligence
The proposed algorithm in this research is based on the multi-agent particle swarm optimization as a collective intelligence due to the connection between several simple components which enables them to regulate their behavior and relationships with the rest of the group according to certain rules. As a result, self-organizing in collective activities can be seen. Community structure is crucial...
متن کامل