Recent Advances in Computational Optimization: Results of the Workshop on Computational Optimization Wco 2013

ISBN-13: 9783319126302 / Angielski / Twarda / 2014 / 193 str.

Stefka Fidanova

Opis

Our everyday life is unthinkable without optimization. We try to minimize our effort and to maximize the achieved profit. Many real world and industrial problems arising in engineering, economics, medicine and other domains can be formulated as optimization tasks. This volume is a comprehensive collection of extended contributions from the Workshop on Computational Optimization 2013. It presents recent advances in computational optimization. The volume includes important real life problems like parameter settings for controlling processes in bioreactor, resource constrained project scheduling, problems arising in transport services, error correcting codes, optimal system performance and energy consumption and so on. It shows how to develop algorithms for them based on new metaheuristic methods like evolutionary computation, ant colony optimization, constrain programming and others.

Spis treści

Three-Stage Heuristic for the Capacitated Vehicle Routing Problem with Time Windows.- Fuzzy bicriteria optimization approach to distribution network design.- Constraint Propagation for the Dial-a-Ride Problem with Split Loads.- Multiple shooting SQP algorithm for optimal control of DAE systems with inconsistent initial conditions.- A Constructive Algorithm for Partial Latin Square Extension Problem that Solves Hardest Instances Effectively.- Branch and Price for Preemptive and Non Preemptive RCPSP Based on Interval Orders on Precedence Graphs.- Population Size Influence on the Genetic and Ant Algorithms Performance in Case of Cultivation Process Modeling.- Hybrid Approach to Modeling, Solving and Optimization of the Constrained Decision Problems.- Biased Random Key Genetic Algorithm for Multi-user Earth Observation Scheduling.- Efficient and Scalable Pareto Front Generation for Energy and Makespan in Heterogeneous Computing Systems.- Measuring Performance of a Hybrid Optimization Algorithm on a Set of Benchmark Functions.