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This study introduces an improved neural network algorithm (NNA) using quasi-oppositional learning and chaotic sine-cosine strategies to enhance global optimization. The novel approach effectively avoids local optima and accelerates convergence for complex engineering problems.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Artificial Neural Networks

Background:

  • Global optimization problems are crucial in engineering, with neural network algorithms (NNA) being widely applied.
  • NNAs often suffer from poor local optima and slow convergence on complex problems.
  • Addressing these limitations is essential for advancing engineering applications.

Purpose of the Study:

  • To propose an improved neural network algorithm (NNA) that enhances global optimization capabilities.
  • To accelerate convergence and prevent the algorithm from getting stuck in local optima.
  • To validate the effectiveness of the proposed algorithm on benchmark functions and engineering problems.

Main Methods:

  • Integration of quasi-oppositional-based learning to improve search space exploration and exploitation.
  • Introduction of a novel logistic chaotic sine-cosine learning strategy to enhance escape from local optima.
  • Utilization of a dynamic tuning factor with piecewise linear chaotic mapping to adjust the exploration space and improve convergence.

Main Results:

  • The improved NNA demonstrated superior performance in avoiding local optima and achieving faster convergence.
  • Comparative analysis using CEC 2017 functions and engineering problems showed significant improvements.
  • Statistical tests confirmed the algorithm's excellent global optimality and convergence speed.

Conclusions:

  • The proposed improved neural network algorithm effectively overcomes the limitations of traditional NNAs in global optimization.
  • The combination of quasi-oppositional learning and chaotic sine-cosine strategies offers a robust solution for complex optimization tasks.
  • The algorithm shows strong potential for practical application in various engineering fields requiring efficient global optimization.