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Featuring the latest and most promising research directions in agent-based optimization, this volume focuses on approaches deploying the multi-agent system paradigm to support, enhance, or even replace traditional solutions to optimization problems.

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This volume presents a collection of original research works by leading specialists focusing on novel and promising approaches in which the multi-agent system paradigm is used to support, enhance or replace traditional approaches to solving difficult optimization problems. The editors have invited several well-known specialists to present their solutions, tools, and models falling under the common denominator of the agent-based optimization . The book consists of eight chapters covering examples of application of the multi-agent paradigm and respective customized tools to solve difficult optimization problems arising in different areas such as machine learning, scheduling, transportation and, more generally, distributed and cooperative problem solving.

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Machine Learning and Multiagent Systems as Interrelated Technologies.- Ant Colony Optimization for the Multi-criteria Vehicle Navigation Problem.- Solving Instances of the Capacitated Vehicle Routing Problem Using Multi-Agent Non-Distributed and Distributed Environment.- Structure vs. Efficiency of the Cross-Entropy Based Population Learning Algorithm for Discrete-Continuous Scheduling with Continuous Resource Discretisation.- Triple-Action Agents Solving the MRCPSP/max Problem.- Team of A-Teams - a Study of the Cooperation Between Program Agents Solving Difficult Optimization Problems.- Distributed Bregman-Distance Algorithms for Min-Max Optimization.- A Probability Collectives Approach for Multi-Agent Distributed and Cooperative Optimization with Tolerance for Agent Failure.

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