Abstract:

The searching ability of traditional evolutionary algorithms decrease srapidly because of the reduced selection pressure in dealing with many-objective optimization problems.Therefore,a many-objective evolutionary algorithm based on a multitasking mechanism is proposed.To increase the selection pressure in optimization processes,an adaptive objective reduction strategy is adopted to construct the low-dimensional task,which is related to the traditional many-objective optimization task.In the construction of low-dimensional tasks,the appropriate dimension reduction technique is chosen according to the evaluation of the current objective subset adaptively.After that,the constructed low-dimensional task and the original many-objective task are optimized simultaneously according to the multitasking mechanism.In this paper,an inter-task interaction strategy is adopted to allocate tasks to individuals and update the individual population,so as to improve the searching ability and avoid information loss because of dimension reduction.Moreover,a differential mutation operator is implemented on the individuals which remain unchanged for several generations from the repository population to avoid converging prematurely.In the experimental part,the proposed algorithm is tested on five groups of benchmark functions with several state-of -the-art many-objective evolutionary algorithms.Statistical results demonstrate the effectiveness of the proposed algorithm in solving many-objective optimization problems.

Key words: multitasking, many-objective optimization, evolutionary algorithms, objective reduction, adaptive algorithms