System identification is the study involving the derivation of a mathematical model from input
and output data to explain dynamical behavior of a system. Such derivation is made using a …

The present work deals with application of recently developed evolutionary computing
based training methods for non-linear system identification problem. Generally, most of the …

The thesis develops five novel hybrid metaheuristic algorithms as per Talbi s taxonomy of
hybrid metaheuristics. Firefly algorithm is integrated with bacterial foraging, flower …

Aim of this research is to explore the strength of evolutionary and swarm intelligence
techniques for parameter identification of control autoregressive moving average (CARMA) …

In this paper, a new nature-inspired Artificial Ecosystem Optimization (AEO) methodology is
presented for reducing the complexity of nonlinear PEMFC SR-12 500 W system. From the …

Focusing on the demerits of AEA (Alopex-based evolutionary algorithm), this paper
proposed a modified AEA algorithm (MAEA), which was fused AEA with differential evolution …

Increased data monitoring enables the energy-efficient operation of air-conditioning systems
via data-mining. The latter is projected to have lesser consumption but more comprehensive …

This study proposes a novel artificial protozoa optimizer (APO) that is inspired by protozoa in
nature. The APO mimics the survival mechanisms of protozoa by simulating their foraging …

The extraction of knowledge corresponds to the process of finding hidden information such
as spatial and context relationships from a given bunch of data. Recently, the extraction of …

Facing the serious concern of global food security, this study focuses on the feasibility of
improvements to food systems and predicts future changes. This study proposes a …