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Sub-Population Genetic Algorithm II for Multi-Objective Parallel Machine Scheduling Problems

Excerpt

In recent years, industrial manufacturing usually faces the tradeoff of multi-objective decision problems. Many researchers have become more aware of the efficiency of heuristics for solving multi-objective problems. In this paper, we improve the previous SPGA approach proposed by Chang et al. [19] and present a Sub-population Genetic Algorithm II (SPGA2). SPGA2 takes advantage of the Tchebycheff Decomposition and effective Pareto Fronts and Reference Points generated during the evolutionary process to enhance the performance of the proposed approach. Our experimental results show that SPGA2 is able to improve the performance of SPGA in solving Parallel Machine Scheduling Problems.

  • Abstract
  • Key Words
  • 1 Introduction
  • 2. Literature Review
  • 3 Tchebycheff Decomposition Genetic Algorithm
  • 4. Experimental Results
  • 5. Conclusion
  • 6. Acknowledgement
  • References

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