一种布谷鸟-交叉熵混合优化算法及其性能仿真

为了提高布谷鸟搜索算法在求解复杂优化问题时的收敛速度和搜索精度,基于交叉熵方法,构建了一种新的布谷鸟-交叉熵混合优化算法.该算法将基于模型的交叉熵随机优化算法和基于种群的布谷鸟搜索进行有机融合,采用协同演化策略,既提升了混合算法收敛速度,又改善了其全局优化能力.对经典测试函数和PID控制器整定问题的仿真结果表明,新算法具有全局搜索能力强、求解精度高和鲁棒性好等特性,是一种求解复杂优化问题的可行和有效算法.

Hybrid Optimization Algorithm Based on Cuckoo Search and Cross Entropy and Its Performance

In order to improve the rate of convergence and obtain high optimization precision of cuckoo search, this paper proposed a hybrid optimization algorithm involving cross-entropy method for solving complicated optimization problems. The proposed algorithm combines model-based cross-entropy method with population-based cuckoo search. The hybrid algorithm not only improves the rate of convergence but also enhances the global search ability by co-evolution. Simulated experiments were conducted on classical benchmarks and PID controller tuning problem. The results show that the proposed algorithm possesses more powerful global search capacity, higher optimization precision and robustness, and is feasible and effective for solving complicated optimization problems.