基于改进遗传算法的继电器优化设计

Relay Optimized Design Based on Improved Genetic Algorithm

为解决二进制编码遗传算法(GAs)处理连续优化问题时需解码、编码操作,算法计算精度不高等问题,提出了一种基于实数编码的改进遗传算法(IFGA),采用非均匀交叉和非均匀变异算子进化产生下一代种群。针对继电器产品体积最小优化设计受机、电、磁、热、几何尺寸等制约条件下的非线性约束优化问题,其目标函数和约束函数均呈高度非线性,应用改进的遗传算法结合自适应约束处理技术求解该优化问题。通过数值试验与传统设计、已有文献设计的对比,该算法满足所有约束条件,且目标函数值下降51.6%,验证了该算法的有效性。

To overcome the binary-encoding genetic algorithms （ GAs ） to deal with continuous optimization problems low accuracy and need to encoding and decoding operation, this paper proposed a improved genetic algorithms based on real-number encoding with the non-uniform crossover and non-uniform mutation operator to generation the offspring population. The relay optimization design of the smallest volume is subject to mechanical, electrical, magnetic, thermal, geometric dimensions and other constraints under nonlinear constrained optimization problems, thus the objective function and constraint functions a~ highly nonlinear. The optimization problem is solved by improved genetic algorithms combined with adaptive constraint handling techniques. It is verification effectively by numerical experiments compared with the traditional design and the reference literature application IFGA, the results satisfies all the constraints and objective function value decreased 51.6% .