Meta-heuristic optimization algorithms for design of gain constrained state variable filter

In conventional design method, the parameters of an active filter cannot be selected based on manufactured parameters such as E series. However, this can be feasibly obtained using optimization technique through an exhaustive search on all possible combination of preferred values of E series by providing the coding system. The exhaustive search for low pass second order filter is carried out through Meta-heuristic optimization Algorithms. The cost function for error minimization including compatible to E series and gain adaptive are used here as objective function for finding out minima through well known algorithms. The inclusion of gain constrained concept eliminates the extra op-amp cascading to state variable filter circuit which reduces the noise and dimension of cost function; in addition, improves the stability of the system. The performances of Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA) and Sine-Cosine Algorithm (SCA), nature and physics-inspired artificial intelligence techniques are evaluated for state variable filter design considering gain constrained. Each of above algorithms is applied to filter structure and performances of them are also evaluated when filter design is realized with components selected from different manufactured E series.