In this study, nature-inspired computational intelligence is exploited for active noise control (ANC) systems using variants of particle swarm optimization (PSO) algorithm and its memetic combination with efficient local search technique based on active-set (AS), interior-point (IP), Nelder–Mead (NM) and sequential quadratic programming (SQP) algorithms. In ANC, filtered extended least mean square algorithm is normally used for finding the optimal parameters of the linear finite-impulse response filter, which is more likely to trap in local minima (LM). The issue of LM problem is effectively handled with competence of nature-inspired heuristics by developing four variants of memetic computing approaches based on PSO-NM, PSO-AS, PSO-IP, and PSO-SQP in order to adapt the design variables of ANC with linear and nonlinear primary and secondary paths by taking input noise interferences of pure sinusoidal, random and complex random types. The comparative studies of proposed schemes through statistical performance indices have established the worth of the schemes in terms of accuracy, convergence and complexity parameters.