Discussion of Search Strategy for Multi-objective Genetic Algorithm with Consideration of Accuracy and Broadness of Pareto Optimal Solutions

  • Tomoyuki Hiroyasu
  • Masashi Nishioka
  • Mitsunori Miki
  • Hisatake Yokouchi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5361)


In multi-objective optimization, it is important that the obtained solutions are high quality regarding accuracy, uniform distribution, and broadness. Of these qualities, we focused on accuracy and broadness of the solutions and proposed a search strategy. Since it is difficult to improve both convergence and broadness of the solutions at the same time in a multi-objective GA search, we considered to converge the solutions first and then broaden them in the proposed search strategy by dividing the search into two search stages. The first stage is to improve convergence of the solutions, and a reference point specified by a decision maker is adopted in this search. In the second stage, the solutions are broadened using the Distributed Cooperation Scheme. From the results of the numerical experiment, we found that the proposed search strategy is capable of deriving broader solutions than conventional multi-objective GA with equivalent accuracy.


Pareto Front Knapsack Problem Pareto Optimal Solution Pareto Optimal Front Multiobjective Evolutionary Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Tomoyuki Hiroyasu
    • 1
  • Masashi Nishioka
    • 2
  • Mitsunori Miki
    • 3
  • Hisatake Yokouchi
    • 1
  1. 1.Faculty of Life and Medical SciencesDoshisha UniversityKyotoJapan
  2. 2.Graduate School of EngineeringDoshisha UniversityJapan
  3. 3.Faculty of Science and EngineeringDoshisha UniversityJapan

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