Metaheuristics have been a very active research topic for more than two decades. During this time many new metaheuristic strategies have been devised, they have been experimentally tested and improved on challenging benchmark problems, and they have proven to be important tools for tackling optimization tasks in a large number of practical applications. In other words, metaheuristics are nowadays established as one of the main search paradigms for tackling computationally hard problems. Still, there are a large number of research challenges in the area of metaheuristics. These challenges range from more fundamental questions on theoretical properties and performance guarantees, empirical algorithm analysis, the effective configuration of metaheuristic algorithms, approaches to combine metaheuristics with other algorithmic techniques, towards extending the available techniques to tackle ever more challenging problems.

This edited volume grew out of the contributions presented at the ninth Metaheuristics International Conference that was held in Udine, Italy, 25-28 July 2011. The conference comprised 117 presentations of peer-reviewed contributions and 3 invited talks, and it has been attended by 169 delegates. The chapters that are collected in this book exemplify contributions to several of the research directions outlined above.

Presents the latest research in metaheuristics methods and applications Contributors are leading names in the field Takes readers inside the methodology of metaheuristics, then explains applications in classical problems and real-world situations??

Inhalt
Finite First Hitting Time versus Stochastic convergence in Particle Swarm Optimisation.- Using Performance Profiles for the Analysis and Design of Benchmark Experiments.- Real-World Parameter Tuning using Factorial Design with Parameter Decomposition.- Evolving Pacing Strategies for Team Pursuit Track Cycling.- A Dual Mutation Operator to Solve the Multi-objective Production Planning of Perishable Goods.- Brain cine-MRI Registration using MLSDO Dynamic Optimization Algorithm.- GRASP with Path Relinking for the Two-Echelon Vehicle Routing Problem.- A Hybrid (1+1)-Evolutionary Strategy for the Open Vehicle Routing Problem.- A Timeslot-Filling Heuristic Approach to Construct High-School Timetables.- A GRASP for Supply Chain Optimization with Financial Constraints per Production Unit.