Design and Analysis of Meta-heuristics for Constrained Optimization Problems
نویسندگان
چکیده
This goal of this senior design project is to implement and analyze the effectiveness of different meta-heuristic algorithms on solving two constrained optimization problems: the Bin Packing Problem and the Generalized Assignment Problem. Specifically, we are most interested in applying a certain meta-heuristic technique, the Genetic Algorithm. Although genetic algorithms have been used to solve these problems before, we implement a new type of genetic algorithm: the FeasibleInfeasible Two Population Genetic Algorithm. We compare its performance to traditional genetic algorithms and simulated annealing. In the case of the Bin Packing Problem we also compare performance to simple approximation algorithms like First Fit, Best Fit, and Worst Fit. Additionally, a visualization tool has been developed that demonstrates the different algorithms in action for the Bin Packing Problem. This tool may be used to visually compare the performance of the different algorithms or by instructors in the classroom for demonstrations.
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