Hybrid Metaheuristics

This book constitutes the refereed proceedings of the 10th International Workshop on Hybrid Metaheuristics, HM 2016, held in Plymouth, UK, in June 2016.

The 15 revised full papers presented were carefully reviewed and selected from 43 submissions. The selected papers are of interest for all the researchers working on integrating metaheuristics with other areas for solving both optimization and constraint satisfaction problems. They represent as well a sample of current research demonstrating how metaheuristics can be integrated with integer linear programming and other operational research techniques for tackling difficult and relevant problems.

Includes supplementary material: sn.pub/extras

Inhalt
Finding Uniquely Hamiltonian Graphs of Minimum Degree Three with Small Crossing Numbers.- Construct, Merge, Solve and Adapt: Application to Unbalanced Minimum Common String Partition.- Variable Neighbourhood Descent with Memory: A Hybrid Metaheuristic for Supermarket Resupply.- Hybridization as Cooperative Parallelism for the Quadratic Assignment Problem.- Investigating Edge-Reordering Procedures in a Tabu Search Algorithm for the Capacitated Arc Routing Problem.- Multi-chaotic Approach for Particle Acceleration in PSO.- Districting and Routing for Security Control.- A GRASP/VND Heuristic for a Generalized Ring Star Problem.- Neighborhood Composition Strategies in Stochastic Local Search.- A Hybrid Multi-objective Evolutionary Approach for Optimal Path Planning of a Hexapod Robot: A Preliminary Study.- Hybridization of Chaotic Systems and Success-History Based Adaptive Differential Evolution.- Tabu Search Hybridized with Multiple Neighborhood Structures for the Frequency Assignment Problem.- The Capacitated m Two-Node Survivable Star Problem: A Hybrid Metaheuristic Approach.- Robust Berth Allocation Using a Hybrid Approach Combining Branch-and-cut and the Genetic Algorithm.- Dealing with the Strategic Level of Decisions Related to Automated Transit Networks: A Hybrid Heuristic Approach.- DEEPSAM: A Hybrid Evolutionary Algorithm for the Prediction of Biomolecules Structure.-