An improved harris hawks optimization algorithm based on chaotic sequence and opposite elite learning mechanism.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Swarm Intelligence

Background:

• The Harris Hawks Optimization (HHO) algorithm is a nature-inspired swarm intelligence algorithm.
• HHO suffers from premature convergence and local optima due to exploration-exploitation imbalance.

Purpose of the Study:

• To propose a novel HHO variant, HHO-CS-OELM, addressing HHO's limitations.
• To enhance both global and local search capabilities of the HHO algorithm.

Main Methods:

• Incorporated a chaotic sequence to improve population diversity and global search.
• Introduced an opposite elite learning mechanism for enhanced local search and maintaining optimal individuals.
• Balanced exploration and exploitation phases throughout the optimization process.

Main Results:

• The HHO-CS-OELM algorithm demonstrated superior performance.
• Outperformed 14 other optimization algorithms on 23 benchmark functions.
• Showcased effectiveness on a complex engineering problem.

Conclusions:

• HHO-CS-OELM effectively overcomes the premature convergence and local optima issues of the standard HHO algorithm.
• The proposed variant offers a balanced approach to exploration and exploitation.
• HHO-CS-OELM represents a significant advancement over existing state-of-the-art swarm intelligence algorithms.