A Gaussian convolutional optimization algorithm with tent chaotic mapping.

Area of Science:

• Computational intelligence and optimization algorithms.
• Evolutionary computation and metaheuristics.

Background:

• Traditional convolution optimization algorithms (COA) suffer from slow convergence and premature local optima.
• Need for robust optimization techniques in complex search spaces.

Purpose of the Study:

• To introduce a novel Gaussian mutation convolution optimization algorithm based on tent chaotic mapping (TCOA).
• To address the limitations of traditional COA, specifically convergence speed and local optima.
• To enhance the exploration and exploitation balance in optimization.

Main Methods:

• Initialization using tent chaotic strategy for uniform population distribution.
• Gaussian convolution kernel for in-depth search and local optima avoidance.
• Validation using 23 benchmark functions and comparison with six evolutionary algorithms.

Main Results:

• TCOA demonstrates superior performance in low-dimensional optimization tasks.
• The algorithm effectively solves practical, spring-related industrial design problems.
• Significant improvements in convergence speed and solution accuracy compared to existing methods.

Conclusions:

• TCOA offers a robust and efficient solution for complex optimization challenges.
• The proposed algorithm has broad applicability in engineering and scientific problem-solving.
• Gaussian mutation and tent chaotic mapping effectively enhance optimization capabilities.