In this book a generic library of efficientmetaheuristics for combinatorial optimization ispresented. In the version at hand classes thatfeature local search, simulated annealing, tabusearch, guided local search and greedy randomizedadaptive search procedure were implemented.Most notably a generic implementation features theadvantage that the problem dependent classes andmethods only need to be realized once withouttargeting a specific algorithm because these parts ofthe source code are shared among all presentalgorithms contained in EAlib.This main advantage is then exemplary demonstratedwith the quadratic assignment problem. The sourcecode of the QAP example can also be used as ancommented reference for future problems.Concluding the experimental results of the individualmetaheuristics reached with the presentedimplementation are presented.
Wagner DanielnStudies of computer sciences and computermanagement at the Vienna University of Technology. Applications-and Systemsmanager at Verbund Management Service GmbH, Vienna.
Über den Autor
Studies of computer sciences and computer
management at the Vienna University of Technology. Applications-
and Systemsmanager at Verbund Management Service GmbH, Vienna.
Klappentext
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.
