Abstract
In this paper, a multiagent system based cuckoo search optimization (MASCSO) algorithm is developed by combining a multiagent system (MAS) and cuckoo search optimization (CSO) to exploit the complementary nature of the MAS and CSO. The existing behavioral rules in MAS are modified to get improved convergence. The MASCSO algorithm is tested on benchmark single objective bounded constrained functions. Nonparametric statistical analysis is performed to validate the MASCSO algorithm against benchmark algorithms. The proposed MASCSO algorithm is applied to estimate parameters of photovoltaic (PV) cell and module using Lambert W-function (MASCSO(L)) and Direct (MASCSO(D)) current estimation approaches, respectively. The relative power error percentage at the maximum power point (%ΔPMPP) is proposed to justify the effectiveness of these parameter estimation techniques. The results indicated that parameters estimated from MASCSO(L) technique have lowered %ΔPMPP by 54.46% and 38.88%, respectively, for PV cell and module.
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