MATLAB Implementation of Enhanced Genetic Algorithm for Passive Filter Optimization

In this article, we explore the implementation of an enhanced genetic algorithm for parameter optimization of passive filters, addressing three critical objectives: cost reduction, reactive power compensation, and filtering performance. The enhanced genetic algorithm is an optimization technique inspired by genetic principles and evolutionary theory, which identifies optimal solutions by simulating biological evolution processes under specified objective functions. This paper demonstrates how to implement this algorithm in MATLAB to solve passive filter optimization problems, including detailed discussions of key implementation aspects such as chromosome encoding for filter parameters (component values, configurations), fitness function design combining multiple objectives, and specialized genetic operators like adaptive mutation rates. We provide insights into algorithmic advantages including global search capability and multi-objective handling, while addressing limitations such as computational complexity. Through practical MATLAB code examples covering population initialization, selection mechanisms, and convergence criteria, readers will gain applicable knowledge for solving real-world engineering problems, significantly benefiting their research and professional applications.