An Enhancement of Cuckoo Search Algorithm for Optimal Earthquake Evacuation Space Allocation in Intramuros, Manila City

TL;DR

The paper addresses limitations of the Cuckoo Search Algorithm (CSA)—namely, uneven search due to random initialization and fixed parameter values—by proposing an Enhanced Cuckoo Search Algorithm (ECSA) that uses Sobol sequence initialization and Cosine Annealing with Warm Restarts to adapt the discovery rate and step size. ECSA demonstrates superior performance over CSA on 11 of 13 benchmark functions, with a mean fitness improvement of about 30% (roughly 35% for unimodal and 24% for multimodal problems), and faster convergence. It additionally applies ECSA to a discrete location-allocation problem for earthquake evacuation spaces in Intramuros, Manila, showing practical potential though transferability to discrete settings is not statistically significant. The work advances global optimization techniques with direct implications for disaster management and could extend to other resource-allocation domains with further discrete-oriented refinements.

Abstract

The Cuckoo Search Algorithm (CSA), while effective in solving complex optimization problems, faces limitations in random population initialization and reliance on fixed parameters. Random initialization of the population often results in clustered solutions, resulting in uneven exploration of the search space and hindering effective global optimization. Furthermore, the use of fixed values for discovery rate and step size creates a trade-off between solution accuracy and convergence speed. To address these limitations, an Enhanced Cuckoo Search Algorithm (ECSA) is proposed. This algorithm utilizes the Sobol Sequence to generate a more uniformly distributed initial population and incorporates Cosine Annealing with Warm Restarts to dynamically adjust the parameters. The performance of the algorithms was evaluated on 13 benchmark functions (7 unimodal, 6 multimodal). Statistical analyses were conducted to determine the significance and consistency of the results. The ECSA outperforms the CSA in 11 out of 13 benchmark functions with a mean fitness improvement of 30% across all functions, achieving 35% for unimodal functions and 24% for multimodal functions. The enhanced algorithm demonstrated increased convergence efficiency, indicating its superiority to the CSA in solving a variety of optimization problems. The ECSA is subsequently applied to optimize earthquake evacuation space allocation in Intramuros, Manila.

Figures (6)

• Figure 1: Pseudocode of the Cuckoo Search Algorithm
• Figure 2: Sobol Sequence implementation
• Figure 3: Cosine Annealing with Warm Restarts implementation
• Figure 4: Pseudocode of Enhanced Cuckoo Search Algorithm
• Figure 5: Where $\mathbf{x}$ represents a solution and $d(x_i, x_j)$ represents the distance between block $j$ and evacuation area $i$.