A Hybrid Butterfly Optimization Algorithm for Numerical Optimization Problems.

Area of Science:

• Computational Intelligence
• Swarm Intelligence
• Optimization Algorithms

Background:

• The butterfly optimization algorithm (BOA) is a metaheuristic inspired by butterfly behavior, widely used for optimization.
• BOA faces challenges like reduced population diversity and local optima entrapment, limiting its effectiveness.

Purpose of the Study:

• To propose a hybrid butterfly optimization algorithm (GDEBOA) integrating a Gaussian distribution estimation strategy.
• To enhance the exploration and exploitation capabilities of the butterfly optimization algorithm.

Main Methods:

• Developed GDEBOA by incorporating a Gaussian distribution estimation strategy to guide population evolution.
• Evaluated GDEBOA against six state-of-the-art algorithms on the CEC2017 benchmark.
• Applied GDEBOA to the Unmanned Aerial Vehicle (UAV) path planning problem.

Main Results:

• GDEBOA demonstrated superior performance compared to existing algorithms on CEC2017 benchmarks.
• The proposed algorithm effectively improved exploration and exploitation capabilities.
• GDEBOA proved highly competitive in solving the UAV path planning problem.

Conclusions:

• GDEBOA offers a significant improvement over the standard BOA by mitigating local optima and enhancing diversity.
• The hybrid approach effectively balances exploration and exploitation for complex optimization tasks.
• GDEBOA shows strong potential for real-world applications like UAV path planning.