Tools
Tools
Sheibani, Kaveh (2005) Fuzzy greedy evaluation in search, optimisation, and learning. Doctoral thesis, London Metropolitan University.
This thesis introduces a new concept, which is based on the greedy evaluation of objects from the viewpoint of fuzzy reasoning, and applying this to the field of combinatorial optimisation. The effectiveness and efficiency of this fuzzy greedy evaluation concept are investigated through the development of approximate solution methods for hard combinatorial problems. In this context, we develop a hybrid metaheuristic, which is a combination of a genetic algorithm (GA) and greedy randomised adaptive search procedures (GRASP) for the travelling salesman problem (TSP), a heuristic for the permutation flow-shop scheduling problem (PFSP), and a hybrid GA for the PFSP. Computational experiments using a wide range of standard benchmark problems gave very promising results. These were competitive with the results obtained by other researchers using GA-based metaheuristics. In addition, the heuristic for the PFSP problem gave results that were, in general, superior to those obtained by the well-known NEH heuristic.
 |
View Item |