CMRLCCOA: Multi-Strategy Enhanced Coati Optimization Algorithm for Engineering Designs and Hypersonic Vehicle Path...

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• The Coati Optimization Algorithm (COA) faces challenges with slow search velocity and limited optimization precision.
• Existing metaheuristic algorithms often struggle with premature convergence and insufficient exploration of the search space.

Purpose of the Study:

• To introduce an enhanced Coati Optimization Algorithm (CMRLCCOA) that addresses the limitations of the original COA.
• To improve the search velocity, optimization precision, and stability of the Coati Optimization Algorithm.

Main Methods:

• Initialization using Sine chaotic mapping to enhance population quality and diversity.
• Convex lens imaging reverse learning strategy to expand the search range of candidate solutions.
• Lévy flight strategy to increase search step size and prevent premature convergence.
• Crossover strategy to reduce search blind spots and guide particles toward the global optimum.

Main Results:

• CMRLCCOA demonstrated superior performance on CEC2017 and CEC2019 benchmark datasets compared to existing algorithms.
• Iterative convergence curves, boxplots, and statistical analysis confirmed improved convergence accuracy and avoidance of local optima.
• Application to hypersonic vehicle cruise trajectory optimization yielded shorter path lengths than comparative methods.

Conclusions:

• The proposed CMRLCCOA effectively enhances the efficiency, precision, and steadiness of the Coati Optimization Algorithm.
• CMRLCCOA shows significant potential for solving complex optimization problems, including engineering applications.
• The integration of multiple strategies provides a robust and competitive optimization framework.