Modified Harris Hawks Optimization Algorithm with Exploration Factor and Random Walk Strategy

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristic Computing

Background:

• Harris hawks optimization (HHO) is a popular population-based metaheuristic algorithm.
• HHO effectively imitates natural hunting behaviors but often suffers from premature convergence and stagnation in local optima, limiting its global optimization capabilities.

Purpose of the Study:

• To propose an improved Harris hawks optimization algorithm, termed ERHHO, designed to overcome the local optima stagnation issue.
• To enhance the global search capability and reliability of the Harris hawks optimization algorithm for complex optimization problems.

Main Methods:

• Introduced a tent chaotic map during the initialization phase to increase population diversity.
• Developed an exploration factor to optimize algorithm parameters, thereby improving exploration ability.
• Incorporated a random walk strategy to boost exploitation capabilities and facilitate escaping local optima.

Main Results:

• Systematic experiments were conducted on 23 benchmark functions and the CEC2017 test suite.
• The proposed ERHHO algorithm demonstrated superior performance compared to other well-established algorithms.
• ERHHO provided more reliable solutions, effectively addressing the local optima problem inherent in standard HHO.

Conclusions:

• The ERHHO algorithm successfully enhances the Harris hawks optimization by improving population diversity, exploration, and exploitation.
• ERHHO offers a more robust and reliable approach for solving global optimization problems, outperforming existing methods.
• The proposed enhancements effectively mitigate local optima stagnation, paving the way for more effective metaheuristic optimization.