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E-grāmata: Advances in Metaheuristics

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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.



Collecting work presented at the 9th Metaheuristics International Conference (2011), this book covers theoretical properties and performance guarantees, configuration of metaheuristic algorithms, combining metaheuristics and other algorithmic methods and more.
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.