Abstract
The optimization problem of collaboration services scheduling is a major bottleneck restricting the efficiency and cost of collaboration services executing. Correct and efficient handling of scheduling problems contributes to reducing costs and increase efficiency. The traditional GA solves this multi-objective problem more comprehensively than the random algorithm such as stochastic greedy algorithm, but it still has some one-sidedness compared with the actual situation. This paper enhances the flexibility and diversity of the algorithm on the basis of traditional genetic algorithm. In the process of initial population selection, it adopts the method of determining the preliminary internal point internal modification, and optimizes the selection process in the iteration as the selection method based on population exchange to achieve the choice. Mutation factors in the variation based on the individual’s innate quality of adaptive selection enhance the diversity of the population. In the experiments, this algorithm can not only maintain individual diversity, increase the probability of excellent individuals, speed up the convergence rate, but also will not lead to the ultimate result of the local optimal solution.
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Acknowledgment
Research was supported by: (1) the National Key Technologies R&D Program No. 2016YFB1000602, No. 2017YFB1400102; (2) the National Natural Science Foundation of China under Grant, No. 61572295; (3) TaiShan Industrial Experts Programme of Shandong Province, No. tscy20160404.
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Guo, W., Xu, M., Xu, W., Cui, L. (2019). A Collaboration Services Scheduling Method Based on Intelligent Genetic Algorithm. In: Sun, Y., Lu, T., Xie, X., Gao, L., Fan, H. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2018. Communications in Computer and Information Science, vol 917. Springer, Singapore. https://doi.org/10.1007/978-981-13-3044-5_46
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DOI: https://doi.org/10.1007/978-981-13-3044-5_46
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