A Novel Restart Strategy for Solving Complex Multi-modal Optimization Problems Using Real-Coded Genetic Algorithm

  • Amit Kumar Das
  • Dilip Kumar Pratihar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)


Genetic algorithm (GA) is one of the most popular and robust stochastic optimization tools used in various fields of research and industrial applications. It had been applied for solving many global optimization problems for the last few decades. However, it has a poor theoretical assurance to reach the globally optimal solutions, while solving the complex multi-modal problems. Restart strategy plays an important role in overcoming this limitation of a GA to a certain extent. Although there are a few restart methods available in the literature, these are not adequate. In this paper, a novel restart strategy is proposed for solving complex multi-modal optimization problems using a real-coded genetic algorithm (RCGA). To show the superiority of the proposed scheme, ten complex multi-modal test functions have been selected from the CEC 2005 benchmark functions and its results are compared with that of the other strategies.


Restart strategy Real-coded genetic algorithm  CEC 2005 benchmark test functions Multi-modal optimization problems 


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringIndian Institute of Technology KharagpurKharagpurIndia

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