An Improved Harris Hawks Optimization Algorithm and Its Application in Grid Map Path Planning.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Robotics and Automation

Background:

• The Harris Hawks Optimization (HHO) algorithm faces challenges including out-of-bounds position updates, low search efficiency, slow convergence, and reduced precision.
• These limitations hinder its effectiveness in complex optimization tasks and real-world applications.

Purpose of the Study:

• To propose an Improved Harris Hawks Optimization (IHHO) algorithm designed to overcome the inherent drawbacks of the standard HHO.
• To enhance the algorithm's search efficiency, convergence speed, precision, and reduce out-of-bounds errors.

Main Methods:

• Introduced a circle map for initial population generation and reduced population boundary numbers to optimize location updates.
• Incorporated a random-oriented strategy to boost inter-population information exchange and minimize out-of-bounds updates.
• Implemented an improved sine-trend search strategy and a nonlinear jump mechanism combining escape energy and jump strength to enhance search performance and convergence accuracy.

Main Results:

• The IHHO algorithm demonstrated superior performance compared to the standard HHO on test functions and 2D grid map path planning.
• Achieved higher solving accuracy, faster convergence speeds, and improved efficiency in non-origin symmetric interval searches.
• Significantly reduced the out-of-bounds position update rate.

Conclusions:

• The proposed IHHO algorithm offers a more competitive and effective solution for optimization problems.
• Validated the feasibility and effectiveness of IHHO, particularly in grid map path planning applications.
• The enhancements provide a robust alternative for complex optimization and pathfinding tasks.