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An Improved Real-Coded Genetic Algorithm Using the Heuristical Normal Distribution and Direction-Based Crossover.

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A novel multi-offspring improved real-coded genetic algorithm (MOIRCGA) enhances optimization by using a new crossover operator and mutation method. This approach accelerates convergence and achieves superior results in constrained optimization problems.

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Engineering mathematics

Background:

  • Constrained optimization problems are prevalent in engineering and science.
  • Existing real-coded genetic algorithms face challenges with convergence speed and population diversity.
  • Developing efficient algorithms is crucial for solving complex optimization tasks.

Purpose of the Study:

  • To propose a Multi-Offspring Improved Real-Coded Genetic Algorithm (MOIRCGA) for constrained optimization.
  • To enhance convergence speed and solution quality.
  • To address limitations in existing genetic algorithm operators.

Main Methods:

  • Development of a novel Heuristical Normal Distribution and Direction-Based Crossover (HNDDBX) operator.
  • Introduction of a substitution operation to prevent population duplication.
  • Implementation of a Combinational Mutation method for simultaneous local and global search.

Main Results:

  • MOIRCGA demonstrated fast convergence speed across sixteen test examples.
  • The algorithm effectively avoids population duplication, improving computational efficiency.
  • Optimization of a cantilevered beam structure showed MOIRCGA outperformed the standard RCGA.

Conclusions:

  • The proposed MOIRCGA, with its enhanced operators, offers significant improvements in solving constrained optimization problems.
  • The algorithm achieves faster convergence and superior objective function values compared to existing methods.
  • MOIRCGA shows promise for practical engineering optimization applications.