ENHANCED BONOBO OPTIMIZER FOR OPTIMIZING DYNAMIC PHOTOVOLTAIC MODELS

Abstract

Bonobo optimizer (BO) is a novel metaheuristic algorithm motivated by
the social behaviour of the bonobos. This paper presents a quantum behaved bonobo optimization algorithm (QBOA) employing an innovative metaheuristic based on the reproductive strategies and social behavior of bonobos.
Whereby, the quantum mechanics are embedded into the bonobo optimizer
to direct the search agents through the search space. Accordingly, under this
quantum-behaved movement, the proposed QBOA’s exploitation capability is
promoted. The performance of the proposed QBOA is exhibited on CEC2005
and CEC2019 benchmarks. Moreover, the QBOA algorithm was adapted to
optimize the dynamic photovoltaic models parameters. QBOA exhibits the
efficiency and adequacy to solve various optimization problems based on experimental and comparison findings, as well as its ability to implement competitive
and promising results optimizing dynamic photovoltaic models