Generic Metaheuristics: a Presentation of a Generic Library for Metaheuristics and Its Application to the Quadratic Assignment Problem

In this book a generic library of efficient metaheuristics for combinatorial optimization is presented. In the version at hand classes that feature local search, simulated annealing, tabu search, guided local search and greedy randomized adaptive search procedure were implemented. Most notably a generic implementation features the advantage that the problem dependent classes and methods only need to be realized once without targeting a specific algorithm because these parts of the source code are shared among all present algorithms contained in EAlib. This main advantage is then exemplary demonstrated with the quadratic assignment problem. The source code of the QAP example can also be used as an commented reference for future problems. Concluding the experimental results of the individual metaheuristics reached with the presented implementation are presented.