Optimization method based on ordinal genetic algorithm under the framework of nested partitions method

A class of order optimization (OO) with optimal computing budgets allocation (OCBA) based genetic algorithm (GA) is designed to perform local search in the framework of nested partitions method (NP). The local searching algorithm borrows from the idea of OO to ensure the quality of the design found with a reduction in computation effort and applies the evolutionary searching mechanism and learning capability of GA to effectively perform exploration and exploitation. Proposed hybrid algorithm retains the property of global search and convergence of NP and the powerful local searching capability of OO with OCBA based GA algorithm. The effectiveness of hybrid algorithm is demonstrated by numerical simulation results based on stochastic job-shop scheduling benchmarks and its optimization quality is much better than pure GA and OO with OCBA algorithm.