Global optimization
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Distributed problem solving and planning
Multiagent systems
Journal of Parallel and Distributed Computing
Multi-agent oriented constraint satisfaction
Artificial Intelligence
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Journal of Global Optimization
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
An evolutionary autonomous agents approach to image featureextraction
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Optimization based on bacterial chemotaxis
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A multi-agent approach for solving optimization problems involving expensive resources
Proceedings of the 2005 ACM symposium on Applied computing
How autonomy oriented computing (AOC) tackles a computationally hard optimization problem
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Collaborative diffusion: programming antiobjects
Companion to the 21st ACM SIGPLAN symposium on Object-oriented programming systems, languages, and applications
Excuse me, I need better AI!: employing collaborative diffusion to make game AI child's play
Proceedings of the 2006 ACM SIGGRAPH symposium on Videogames
Towards harmony-oriented programming
Companion to the 23rd ACM SIGPLAN conference on Object-oriented programming systems languages and applications
Ant Colony Models for a Virtual Educational Environment Based on a Multi-Agent System
Large-Scale Scientific Computing
Harmony-oriented programming and software evolution
Proceedings of the 24th ACM SIGPLAN conference companion on Object oriented programming systems languages and applications
Advanced Engineering Informatics
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Distributed problem solving by a multiagent system represents a promising approach to solving complex computational problems. However, many multiagent systems require certain degree of planning, coordination and negotiation to achieve the given goal. This paper presents a multiagent framework for tackling global optimization tasks inspired by diffusion in nature. The framework is designed for situations where agent communication must be kept to a minimal. Hence, complicated coordination and negotiation is not possible. Distributed agents in this framework share the common goal of finding the global optimal solution. They cooperate to achieve this common goal by sharing and updating a common belief that captures their estimation of the whereabouts of the optimal solution. To facilitate this, agents are naturally organized in families with a parent and its offsprings as members. This paper also presents an algorithm called Evolutionary Diffusion Optimization, which is implemented base on the proposed agent framework. Experimental results on some benchmark problems are presented together with performance comparison with a simulated annealing algorithm.