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Enhanced crested porcupine optimizer for global optimization and constrained engineering problems
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PRE
AI
J
D
杨
C
DOI：10.1007/s10586-026-06399-w.png)
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Abstract
En 中文
Metaheuristic algorithms have been extensively applied across diverse domains owing to their efficiency and versatility in addressing complex optimization problems. Among them, crested porcupine optimizer (CPO) has shown promise in large-scale optimization tasks. However, its limited convergence speed and tendency to become trapped in local optima restrict its performance in high-dimensional and complex scenarios. To overcome these limitations, this study proposes an enhanced crested porcupine optimizer (ECPO) for global and constrained engineering optimization problems. First, the good point set method is employed for population initialization, which improves diversity and distribution uniformity. Second, a novel threat-aware defense relocation mechanism with adaptive parameter adjustment is proposed to dynamically balance exploration and exploitation. Third, a competitive crossover strategy that combines horizontal and vertical crossover operations is developed to broaden the global search range and refine local accuracy. Finally, the performance of ECPO is rigorously evaluated against eleven classical and state-of-the-art algorithms using the CEC2017, CEC2019, and CEC2022 test suites across multiple dimensions. Experimental results and statistical analyses demonstrate that ECPO consistently achieves the best performance, with an average ranking of 1.76. Furthermore, ECPO ranks first in terms of mean performance over 30 independent runs for twelve constrained engineering optimization problems covering diverse practical scenarios. These findings highlight the efficiency, robustness, and reliability of ECPO in solving real-world optimization challenges. Overall, ECPO offers a powerful, accurate, and versatile solution for complex optimization tasks in both theoretical benchmarks and engineering applications.
Keywords:
Crested porcupine optimizer
Good point set
Threat-aware defense relocation mechanism
Crisscross strategy
Engineering optimization problem
Journal
C
IF:
4.1
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4.8K
Citations:
7.5K
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