Gao Yang, Cheng Liang
School of Petroleum Engineering, Yangtze University, Wuhan 430100, China.
Biomimetics (Basel). 2025 Jan 14;10(1):53. doi: 10.3390/biomimetics10010053.
Optimization algorithms play a crucial role in solving complex problems across various fields, including global optimization and feature selection (FS). This paper presents the enhanced polar lights optimization with cryptobiosis and differential evolution (CPLODE), a novel improvement upon the original polar lights optimization (PLO) algorithm. CPLODE integrates a cryptobiosis mechanism and differential evolution (DE) operators to enhance PLO's search capabilities. The original PLO's particle collision strategy is replaced with DE's mutation and crossover operators, enabling a more effective global exploration and using a dynamic crossover rate to improve convergence. Furthermore, a cryptobiosis mechanism records and reuses historically successful solutions, thereby improving the greedy selection process. The experimental results on 29 CEC 2017 benchmark functions demonstrate CPLODE's superior performance compared to eight classical optimization algorithms, with higher average ranks and faster convergence. Moreover, CPLODE achieved competitive results in feature selection on ten real-world datasets, outperforming several well-known binary metaheuristic algorithms in classification accuracy and feature reduction. These results highlight CPLODE's effectiveness for both global optimization and feature selection.
优化算法在解决包括全局优化和特征选择（FS）在内的各个领域的复杂问题中起着至关重要的作用。本文提出了具有隐生现象和差分进化的增强型极光优化算法（CPLODE），这是对原始极光优化（PLO）算法的一种新颖改进。CPLODE集成了隐生现象机制和差分进化（DE）算子，以增强PLO的搜索能力。原始PLO的粒子碰撞策略被DE的变异和交叉算子所取代，实现了更有效的全局探索，并使用动态交叉率来提高收敛性。此外，隐生现象机制记录并重用历史上成功的解决方案，从而改进贪婪选择过程。在29个CEC 2017基准函数上的实验结果表明，与八种经典优化算法相比，CPLODE具有卓越的性能，平均排名更高且收敛速度更快。此外，CPLODE在十个真实世界数据集的特征选择中取得了有竞争力的结果，在分类准确率和特征约简方面优于几种著名的二元元启发式算法。这些结果突出了CPLODE在全局优化和特征选择方面的有效性。
