Efficient design of high pass FIR filter using quantum-behaved particle swarm optimization with weighted mean best position

Quantum-behaved particle swarm optimization (QPSO) algorithm theoretically guarantees global convergence and has been implemented on a wide suite of continuous optimization problems. In this paper, the nonlinear multimodal optimization problem of high pass FIR filter design is investigated using the weighted mean best QPSO algorithm (WQPSO). The results are compared with competitive techniques such as QPSO keeping PSO and PM as references. It is seen that WQPSO statistically outperforms QPSO in terms of convergence characteristics and ripple performance of the designed filter.