Metaheuristic algorithms are widely known as efficient solutions for solving problems of
optimization. These algorithms supply powerful instruments with significant engineering …

Accurate parameter identification is crucial for a precise PV cell modelling and analysis of
characteristics of PV systems, while high nonlinearity of output IV curve makes this problem …

Abstract The Slime Mould Algorithm (SMA) is a recent metaheuristic inspired by the
oscillation of slime mould. Similar to other original metaheuristic algorithms (MAs), SMA may …

Nature-inspired computing has been a hot topic in scientific and engineering fields in recent
years. Inspired by the shallow water wave theory, the paper presents a novel metaheuristic …

The biogeography-based optimisation (BBO) algorithm is a novel evolutionary algorithm
inspired by biogeography. Similarly, to other evolutionary algorithms, entrapment in local …

Semiconductor final testing scheduling problem (SFTSP) has extensively been studied in
advanced manufacturing and intelligent scheduling fields. This paper presents a Q-learning …

With the development of new energy power systems, the estimation of the parameters of
photovoltaic (PV) models has become increasingly important. Weather changes are random; …

Biogeography-based optimization (BBO) is an evolutionary algorithm which is inspired by
the migration of species between habitats. Almost 10 years have passed since the first BBO …

Precise and reliable modelling of solid oxide fuel cells (SOFC) is critical for simulation
analysis and optimal control of SOFC systems, which typically relies on an accurate …

Distributed assembly permutation flow-shop scheduling problem (DAPFSP) is recognized as
an important class of problems in modern supply chains and manufacturing systems. In this …