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.

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.