Swift Flight Optimizer: a novel bio-inspired optimization algorithm based on swift bird behavior

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Bio-inspired Computing

Background:

• Metaheuristic algorithms are crucial for complex, high-dimensional optimization.
• Existing methods often suffer from premature convergence and poor exploration-exploitation balance.
• Novel algorithms are needed to overcome these limitations.

Purpose of the Study:

• Introduce the Swift Flight Optimizer (SFO), a novel bio-inspired algorithm.
• Address limitations of current metaheuristics in solving challenging optimization problems.
• Evaluate SFO's performance on benchmark functions.

Main Methods:

• Developed SFO based on swift bird adaptive flight dynamics.
• Implemented a multi-mode framework: glide (exploration), target (exploitation), micro (refinement).
• Incorporated a stagnation-aware reinitialization strategy to maintain population diversity.

Main Results:

• SFO achieved the best average fitness on 21/30 (10D) and 11/30 (100D) CEC2017 benchmark functions.
• Demonstrated accelerated convergence and a balanced exploration-exploitation.
• Outperformed 13 state-of-the-art optimizers (PSO, GWO, WOA, EMBGO) in speed, quality, and robustness.

Conclusions:

• SFO is a novel and competitive metaheuristic.
• It shows significant potential for large-scale, multimodal, high-dimensional optimization.
• SFO offers sustained population diversity and alleviates premature convergence effectively.