一种改进的混合蛙跳算法求解有约束优化问题

提出一种适用于求解有约束优化问题的改进混合蛙跳算法(improved shuffled frog leaping algorithm, Im-SFLA)。该算法针对混合蛙跳算法(shuffled frog leaping algorithm, SFLA)在进化后期搜索速度变慢且容易陷入局部极值的缺陷,将模拟退火和免疫接种思想引入到具有高斯变异和混沌扰动的SFLA中。标准测试函数仿真结果表明Im-SFLA能显著提高收敛速度和精度,并能有效克服局部极值,全局寻优能力明显优于SFLA。使用静态罚函数法将有约束优化转化为无约束优化,对12个有约束优化测试函数的实验结果表明Im-SFLA寻优精度高、鲁棒性强,是一种十分有效的求解有约束优化问题的算法。

An improved shuffled frog leaping algorithm for solving constrained optimization problems

An improved shuffled frog leaping algorithm (Im-SFLA) was proposed for solving constrained optimization problems. In view of overcoming the defects of shuffled frog leaping algorithm (SFLA) such as slow searching speed in the late evolution and local minimum, the ideas of simulated annealing and immune vaccination were involved into basic SFLA with Guassian mutation and chaotic disturbance in the improved algorithm. The test results on standard test functions indicated that Im-SFLA could outstandingly enhance the convergence velocity and precision, effectively averted the local extreme values and the global searching performance was superior to SFLA. The static penalty function was used to transform a constrained optimization problem into an unconstrained optimization problem, and the test results on 12 constrained optimization benchmark functions showed that Im-SFLA could obtain a high solution quality and had strong robust, which was an effective algorithm for solving constrained optimization problems.