| Abstract: |  This work deals with application of hyperheuristic-based approaches for solving the generally known Vehicle Routing Problem. Hyperheuristics, in contrast to the metaheuristics, work on a higher level of abstraction. That means they are less problem-dependent and can cover a wider range of combinatorial optimization problems. Instead of solving the problem
directly, they suggest and construct methods which after that solve the problem. The primary goal of hyperheuristics is not to overcome existing metaheuristics, but to generalized methods for solving combinatorial and optimization problems.
In this thesis a new hyperheuristic-based algorithm called HyperPOEMS is proposed. This algorithm uses an evolutionary algorithm to develop a sequence of actions which improve the prototype iteration by iteration. The prototype consists of a sequence of units which construct and improve the solution for given problem step by step. In this work the
algorithm is used for solving Capacitated Vehicle Routing Problem (CVRP). This algorithm was tested on some standard state-of-the-art benchmarks and the results was compared with results obtained by the hyperheuristic-based algorithm HHC-VRP and with some specialized
methods for solving CVRP. The results demonstrate that the proposed algorithm outperforms the HHC-VRP algorithm and for some instances can be compared with stateof-the-art algorithms.
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