Hybrid Multiobjective Evolutionary Algorithm with Differential Evolution for Process Planning and Scheduling Problem
In an intelligent manufacturing environment, process planning and scheduling (PPS) plays a very important role as a most complex and practical scheduling problem, which processes a set of prismatic parts into completed products by determining the optimal process plans and moments to execute each operation with competitive manufacturing resources. Many research works use multiobjective evolutionary algorithm (MOEA) to solve PPS problems with consideration of multiple complicated objectives to be optimized. This paper proposes a hybrid multiobjective evolutionary algorithm with differential evolution (HMOEA-DE) to solve the PPS problem. HMOEA-DE uses a special designed fitness function to evaluate the dominated and nondominated individuals and divides the population and elitism into two parts, which close to the center and edge areas of Pareto frontier. Moreover, differential evolution applied on elitism tries to improve the convergence and distribution performances much more by guiding the search directions through different individuals with different fitness function. Numerical comparisons indicate that the efficacy of HMOEA-DE outperforms the traditional HMOEA without DE in convergence and distribution performances.
KeywordsProcess planning and scheduling Multiobjective evolutionary algorithm Differential evolution
This research work is supported by the National Natural Science Foundation of China (U1304609), Foundation for Science & Technology Research Project of Henan Province (162102210044, 152102210068 152102110076), Program for Innovative Research Team (in Science and Technology) in University of Henan Province (17IRTSTHN011), Program for Key Project of Science and Technology in University of Education Department of Henan Province (17A520030), Fundamental Research Funds for the Henan Provincial Colleges and Universities (2014YWQQ12, 2015XTCX03, 2015XTCX04), Research Funds for Key Laboratory of Grain Information Processing and Control (Henan University of Technology) (KFJJ-2015-106), Ministry of Education and the Grant-in-Aid for Scientific Research (C) of Japan Society of Promotion of Science (JSPS): No. 15K00357.
- 4.Babu BV, Jehan MML (2003) Differential evolution for multi-objective optimization. In: The 2003 Congress on Evolutionary Computation, 2003, CEC 2003, vol 4, pp 2696–2703Google Scholar
- 6.Gen M, Cheng R, Lin L (2008) Network models and optimization: Multiobjective genetic algorithm approachGoogle Scholar
- 7.Guo W, Yu X (2014) Non-dominated sorting differential evolution with improved directional convergence and spread for multiobjective optimization. In: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, pp 87–88Google Scholar
- 8.Iorio AW, Li X (2006) Incorporating directional information within a differential evolution algorithm for multi-objective optimization. In: Conference on Genetic and Evolutionary Computation, pp 691–698Google Scholar
- 9.Li H, Zhang Q (2006) A multiobjective differential evolution based on decomposition for multiobjective optimization with variable linkages. In: Parallel Problem Solving From Nature - PPSN Ix, International Conference, Reykjavik, Iceland, 9–13 September 2006, Proceedings, pp 583–592Google Scholar
- 14.Schaffer JD (1985) Multiple objective optimization with vector evaluated genetic algorithms. In: International Conference on Genetic Algorithms, pp 93–100Google Scholar
- 18.Yu X (2010) Introduction to evolutionary algorithms. In: International Conference on Computers and Industrial Engineering, p 1Google Scholar
- 20.Zitzler E, Laumanns M, Thiele L (2001) SPEA2: Improving the strength pareto evolutionary algorithmGoogle Scholar