Improved Harris Hawks Optimization algorithm based on quantum correction and Nelder-Mead simplex method.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Swarm Intelligence

Background:

• The Harris Hawks Optimization (HHO) algorithm, while inspired by hawk predation, faces limitations in calculation accuracy, susceptibility to local optima, and balancing exploration and exploitation.
• These shortcomings hinder its effectiveness in complex optimization problems.

Purpose of the Study:

• To address the limitations of the standard HHO algorithm.
• To propose a novel enhanced algorithm, QC-HHO, with improved performance characteristics.
• To validate the effectiveness of QC-HHO on benchmark functions and a practical application in wireless sensor networks.

Main Methods:

• The proposed QC-HHO algorithm incorporates several enhancements: Hénon Chaotic Map for initial population generation, a quantum correction mechanism for local search, the Nelder-Mead simplex method for improved search, group communication factors, and an energy consumption law to balance exploration and exploitation.
• Performance evaluation involved testing on 10 classical and 30 CEC2014 benchmark functions.

Main Results:

• QC-HHO significantly outperformed the original HHO algorithm and other improved HHO variants on the tested benchmark functions.
• The algorithm demonstrated excellent accuracy in gas leakage source localization, including position and gas release rate estimation, when applied to wireless sensor networks.

Conclusions:

• The QC-HHO algorithm effectively overcomes the limitations of the standard HHO, showing enhanced optimization capabilities.
• The practical application in gas leakage source localization confirms the feasibility and effectiveness of QC-HHO in real-world scenarios.