Solving Constrained Engineering Design Optimization Problems Using the Hippopotamus Optimization Algorithm with Novel Multiple Predator Strategy Safrizal Ardana Ardiyansa, Mohamad Muslikh, and Syaiful Anam
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Brawijaya University
Abstract
This research presents a novel enhancement to the Hippopotamus Optimization Algorithm (HOA) through the introduction of a Multiple Predator Strategy (MPS) aimed at improving exploratory capability in solving constrained engineering design problems. The proposed MPS-HOA was evaluated on eight classical benchmark problems. Quantitative analysis demonstrates that MPS-HOA outperforms the conventional HOA in terms of both solution quality and robustness. Notably, in the pressure vessel design problem, the MPS-HOA achieved an average best fitness of 2.6138 x 10^3 with a standard deviation of 3.3852, representing a 2.97% improvement in accuracy and a 96.85% reduction in performance variability compared to the HOA. For the cantilever beam problem, optimal performance was obtained using 6 predators, yielding a fitness value of 1.344 x 10^0 with lower variance and a computation time of 13.8 seconds. Although computational cost increases linearly with predator count, from 9.1 seconds (1 predator) to 29.6 seconds (20 predators), the trade-off is justified by enhanced optimization outcomes. The results confirm that the proposed MPS-HOA offers a robust, accurate, and efficient approach for solving complex, nonlinear, and highly constrained design optimization problems.
