Genetic Simulated Annealing Algorithm Used for PID Parameters Optimization

Author(s):  
Jiajia Wang ◽  
Guoqing Jin ◽  
Yaqun Wang ◽  
Xiaozhu Chen
2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Sheng Lu ◽  
Chenyang Zuo ◽  
Changhao Piao

To solve the problem of parameter selection during the design of magnetically coupled resonant wireless power transmission system (MCR-WPT), this paper proposed an improved genetic simulated annealing algorithm. Firstly, the equivalent circuit of the system is analysis in this study and a nonlinear programming mathematical model is built. Secondly, in place of the penalty function method in the genetic algorithm, the selection strategy based on the distance between individuals is adopted to select individual. In this way, it reduces the excess empirical parameters. Meanwhile, it can improve the convergence rate and the searching ability by calculating crossover probability and mutation probability according to the variance of population’s fitness. At last, the simulated annealing operator is added to increase local search ability of the method. The simulation shows that the improved method can break the limit of the local optimum solution and get the global optimum solution faster. The optimized system can achieve the practical requirements.


2021 ◽  
Vol 28 (2) ◽  
pp. 101-109

Software testing is an important stage in the software development process, which is the key to ensure software quality and improve software reliability. Software fault localization is the most important part of software testing. In this paper, the fault localization problem is modeled as a combinatorial optimization problem, using the function call path as a starting point. A heuristic search algorithm based on hybrid genetic simulated annealing algorithm is used to locate software defects. Experimental results show that the fault localization method, which combines genetic algorithm, simulated annealing algorithm and function correlation analysis method, has a good effect on single fault localization and multi-fault localization. It greatly reduces the requirement of test case coverage and the burden of the testers, and improves the effect of fault localization.


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