An improved multi-strategy equilibrium optimizer for surface marine vehicle path planning.

Area of Science:

• Optimization Algorithms
• Heuristic Optimization
• Computational Intelligence

Background:

• Standard equilibrium optimizer (EO) faces limitations in optimization capability.
• Need for enhanced global search and exploration in optimization algorithms.

Purpose of the Study:

• To improve the performance of the equilibrium optimizer (EO).
• Introduce novel strategies to enhance EO's optimization capability and global search.
• Validate the effectiveness of the improved algorithm in benchmark functions and real-world applications.

Main Methods:

• Implementation of a reverse equilibrium state pool for wider search space exploration.
• Introduction of a non-uniform equilibrium state selection strategy for focused exploration.
• Integration of an equilibrium state mutation strategy for enhanced global optimum seeking.
• Performance evaluation using 29 benchmark functions and application in marine vehicle path planning.

Main Results:

• The enhanced EO, termed reverse equilibrium states EO (R[Formula: see text]O), shows significant performance improvements over the standard EO.
• R[Formula: see text]O demonstrates superior results compared to other frequently-used heuristic optimization algorithms.
• Successful application of R[Formula: see text]O in path planning for surface marine vehicles with static and dynamic obstacles.

Conclusions:

• The proposed enhancements effectively improve the equilibrium optimizer's performance.
• R[Formula: see text]O offers a robust and efficient solution for complex optimization problems.
• The algorithm shows practical utility in autonomous systems like marine vehicle path planning.