In this paper, a recent optimization algorithm called Equilibrium Optimizer (EO) is first improved using a linear reduction diversity technique (LRD) and local minima elimination method (MEM). The improved EO (IEO) reduces the diversity of the population until enabling them to get better solutions. This method is centered around improving the particles with the worst fitness values within the population by moving them toward the best-so-far solution as an attempt to increase the convergence toward the near-optimal solution. As a side effect, LRD increases the probability of entrapment into local minima if it could not find a better solution. Therefore, another method known as local minima elimination method (MEM) is used to take the current solution either within the boundaries of two particles selected randomly or within the search boundaries of the problem itself. The extensive comparative experiments demonstrate that the proposed IEO is competitive and often superior compared to recent algorithms. We applied the proposed IEO algorithm to R.T.C France commercial solar cells using a single diode model (SDM), the double diode model (DDM), and three photovoltaic (PV) modules in addition to two commercial ones.
|Number of pages||15|
|Publication status||Published - Oct 2020|