An adaptive hybrid evolutionary algorithm and its application in aeroengine maintenance scheduling problem

Multi-objective evolutionary algorithms (MOEAs) have been successfully employed to solve many scientific and engineering problems. However, many algorithms perform ill in maintaining diversity and convergence simultaneously. In this paper, we devised a novel operator selection framework based on two collaborative indicators, generational distance (GD) and maximum spread (MS) to improve the diversity while maintaining a good convergence. By calculating the variation of GDs and MSs over the past 7 iterations, an instruction is conveyed to select a proper operator to execute next 7 iterations. This process is repeated until it reaches the maximum iteration. Two operators are embedded in this algorithm which are differential evolution operator (DE/rand/1) and our proposed crow search operator which is deemed to be efficient in explorating the search space. MOEA/D is utilized as the basis framework of our proposed algorithm. Experiments indicate that our proposed algorithm is valid and outperforms other famous algorithms in terms of diversity and convergence. In the end, a particular aeroengine maintenance scheduling problem is solved by our proposed algorithm.