Genetic ordinal optimization for stochastic traveling salesman problem

Genetic algorithm (GA) is a class of intelligent optimization algorithms, but traditional GA often suffers from prematurity and dependence on parameters, while very few ideas have been improved, the performance of GA has been proposed regarding stochastic optimization problems. In this paper, ordinal optimization (OO) and optimal computing budget allocation (OCBA) are incorporated reasonably within the search framework of GA to propose a novel and effective genetic ordinal optimization (GOO) approach. To test the performance of the proposed algorithm, traveling salesman problem (TSP) with stochastic traveling time is used and simulation results based on benchmarks demonstrate the effectiveness of the GOO by comparison with the traditional method.