The accelerated particle swarm optimisation (APSO) is an improved variant of the PSO algorithm that guarantees convergence through the use of only global best to update both velocity and position of particles. However, like its predecessor, the APSO is also prone to being trapped in local minima. Therefore, this paper proposes two hybrid algorithms synergizing the social ability of the APSO and the exploitative ability of both spiral dynamic algorithm (SDA) and Adaptive SDA (ASDA). The exploration phase of the proposed algorithms APSO-SDA and APSO-ASDA, will be achieved through the APSO algorithm. The exploration phase solutions of the APSO are then fed to the SDA and ASDA to achieve the exploitation phase. The proposed algorithms have been evaluated with benchmark function and have also been used to tune a filtered predictive proportional-integral (FPPI) controller for WirelessHART networked control systems (WHNCS). The results obtained from Friedman's rank test show that the proposed APSO-SDA and APSO-ASDA outperformed their constituent algorithms. Time domain analysis of the FPPI controller also show that the APSO-SDA and APSO-ASDA outperformed the APSO, SDA and ASDA in terms of settling times and overshoot. © 2019 - IOS Press and the authors. All rights reserved.