Enhanced multi-objective evolutionary algorithm for workflow scheduling problem

Chenxi Lin; Bin Tian; Zhen Wang; Zhengbin Zhou

doi:10.1117/12.2660126

22 November 2022 Enhanced multi-objective evolutionary algorithm for workflow scheduling problem

Chenxi Lin, Bin Tian, Zhen Wang, Zhengbin Zhou

Proceedings Volume 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022); 124751C (2022) https://doi.org/10.1117/12.2660126
Event: Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 2022, Hulun Buir, China

Abstract

With the development and application of distributed cloud computing, the problem of assigning workflow tasks to computational resources has become more and more prominent. It involves multiple constraints and optimization objectives, and is a typical NP-hard problem. Existing evolutionary algorithms face local optimum and premature convergence problems. Considering these facts, we proposed a multi-objective evolutionary algorithm with elitism strategy (MOEAES) in this paper. To avoid local optimum, MOEAES uses a new crossover operator called Random Sub-Sequence Exchange Crossover (RSSEX), and it introduces a multi-population-based elitism strategy to accelerate the algorithm. Finally, experimental validation is carried out, which shows that MOEAES achieves performance improvement in terms of solution quality and convergence speed comparing to other methods.

Citation Download Citation

Chenxi Lin, Bin Tian, Zhen Wang, and Zhengbin Zhou "Enhanced multi-objective evolutionary algorithm for workflow scheduling problem", Proc. SPIE 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 124751C (22 November 2022); https://doi.org/10.1117/12.2660126

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