Abstract
In this paper, a novel hybrid multi-objective particle swarm algorithm Mopsocd_BL is proposed to solve the flow shop scheduling problem with two objectives of minimizing makespan and the total idle time of machines. This algorithm bases on Baldwinian learning mechanism to improve local search ability of particle swarm optimization, and uses the Pareto dominance and crowding distance to update the solutions. Experimental results show that this algorithm can maintain the diversity of solutions and find more uniformly distributed Pareto optimal solutions.
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Wang, W., Chen, L., Jie, J., Zhao, Y., Zhang, J. (2012). A Novel Multi-objective Particle Swarm Optimization Algorithm for Flow Shop Scheduling Problems. In: Huang, DS., Gan, Y., Gupta, P., Gromiha, M.M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2011. Lecture Notes in Computer Science(), vol 6839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25944-9_4
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DOI: https://doi.org/10.1007/978-3-642-25944-9_4
Publisher Name: Springer, Berlin, Heidelberg
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