Keywords

agent swarm optimization, multi-objective optimization, distributed artificial intelligence

Start Date

1-7-2010 12:00 AM

Description

Agent Swarm Optimization (ASO) is a generalization of Particle SwarmOptimization (PSO) orientated towards distributed artificial intelligence, taking as a basethe concept of multi-agent systems. It is aimed at supporting decision-making processes bysolving either single or multi-objective optimization problems. ASO offers a commonframework for the plurality of co-existent population-based algorithms and other heuristics.A particle from a PSO swarm, an ant from an ACO (Ant Colony Optimization) system, anda chromosome from a GA (Genetic Algorithm) structure do exhibit different behaviour.Yet, they all share a common feature: each represents a potential solution for the problemto be solved. In a combined environment, a PSO particle could help reinforce pheromoneon the ants’ paths; an ant could be reproduced with a chromosome; a chromosome could bethe leader of a particle swarm, and so on. This framework is a dynamic environment wherenew agents/swarms can be added in real time to contribute to the solution of the problem.During the solution process, the own user can add new agents/swarms to the environmentand even contribute to the solution process with problem-based personal proposals. In thiswork the ASO framework is described, and used to solve a complex problem in watermanagement, namely the optimal design of water distribution systems (including, sizing ofcomponents, reliability, renewal and rehabilitation strategies, etc.) using a multi-objectiveapproach.

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Jul 1st, 12:00 AM

Agent Swarm Optimization: a paradigm to tackle complex problems. Application to Water Distribution System Design

Agent Swarm Optimization (ASO) is a generalization of Particle SwarmOptimization (PSO) orientated towards distributed artificial intelligence, taking as a basethe concept of multi-agent systems. It is aimed at supporting decision-making processes bysolving either single or multi-objective optimization problems. ASO offers a commonframework for the plurality of co-existent population-based algorithms and other heuristics.A particle from a PSO swarm, an ant from an ACO (Ant Colony Optimization) system, anda chromosome from a GA (Genetic Algorithm) structure do exhibit different behaviour.Yet, they all share a common feature: each represents a potential solution for the problemto be solved. In a combined environment, a PSO particle could help reinforce pheromoneon the ants’ paths; an ant could be reproduced with a chromosome; a chromosome could bethe leader of a particle swarm, and so on. This framework is a dynamic environment wherenew agents/swarms can be added in real time to contribute to the solution of the problem.During the solution process, the own user can add new agents/swarms to the environmentand even contribute to the solution process with problem-based personal proposals. In thiswork the ASO framework is described, and used to solve a complex problem in watermanagement, namely the optimal design of water distribution systems (including, sizing ofcomponents, reliability, renewal and rehabilitation strategies, etc.) using a multi-objectiveapproach.