1 research outputs found
New local search in the space of infeasible solutions framework for the routing of vehicles
Combinatorial optimisation problems (COPs) have been at the origin of the design of
many optimal and heuristic solution frameworks such as branch-and-bound
algorithms, branch-and-cut algorithms, classical local search methods, metaheuristics,
and hyperheuristics.
This thesis proposes a refined generic and parametrised infeasible local search
(GPILS) algorithm for solving COPs and customises it to solve the traveling salesman
problem (TSP), for illustration purposes. In addition, a rule-based heuristic is proposed
to initialise infeasible local search, referred to as the parameterised infeasible heuristic
(PIH), which allows the analyst to have some control over the features of the infeasible
solution he/she might want to start the infeasible search with. A recursive infeasible
neighbourhood search (RINS) as well as a generic patching procedure to search the
infeasible space are also proposed. These procedures are designed in a generic manner,
so they can be adapted to any choice of parameters of the GPILS, where the set of
parameters, in fact for simplicity, refers to set of parameters, components, criteria and
rules.
Furthermore, a hyperheuristic framework is proposed for optimizing the parameters of
GPILS referred to as HH-GPILS. Experiments have been run for both sequential (i.e.
simulated annealing, variable neighbourhood search, and tabu search) and parallel
hyperheuristics (i.e., genetic algorithms / GAs) to empirically assess the performance
of the proposed HH-GPILS in solving TSP using instances from the TSPLIB.
Empirical results suggest that HH-GPILS delivers an outstanding performance.
Finally, an offline learning mechanism is proposed as a seeding technique to improve
the performance and speed of the proposed parallel HH-GPILS. The proposed offline
learning mechanism makes use of a knowledge-base to keep track of the best
performing chromosomes and their scores. Empirical results suggest that this learning
mechanism is a promising technique to initialise the GA’s population