Research on Path Planning for Fire Evacuation Using the Enhanced Hiking Optimization Algorithm.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Emergency Response Planning

Background:

• Fire evacuation path planning faces challenges like local optima convergence, inefficient computation, and suboptimal path quality.
• Existing algorithms often struggle with high-dimensional and multimodal optimization problems.

Purpose of the Study:

• To propose an enhanced optimization algorithm, the Weighted Dynamic Hiking Optimization Algorithm (WDHOA), to address fire evacuation path planning challenges.
• To improve convergence accuracy, computational efficiency, and path quality in complex optimization scenarios.

Main Methods:

• Development of WDHOA with a three-phase cooperative framework: dynamic grouping, hybrid search, and angle generation.
• Evaluation using CEC 2017 and CEC 2022 benchmark suites against eight established algorithms.
• Application of WDHOA to fire evacuation path planning problems with nonlinear constraints.

Main Results:

• WDHOA significantly outperformed LSHADE, RIME, and SCA in convergence accuracy, stability, and robustness on benchmark functions.
• Statistical tests (Wilcoxon rank-sum, Friedman) confirmed WDHOA's significant superiority.
• WDHOA achieved optimal solutions for fire evacuation path planning, satisfying all constraints.

Conclusions:

• WDHOA effectively balances optimization accuracy and practical applicability for fire evacuation.
• The enhanced strategies within WDHOA contribute to its improved performance in complex optimization tasks.
• WDHOA offers a robust solution for efficient and safe fire evacuation path planning.