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In recent years, the application of metaheuristics in training neural network models has gained significance due to the drawbacks of deterministic algorithms. This paper aims to propose the use of a recently developed “memory based hybrid dragonfly algorithm” (MHDA) for training multi-layer perceptron (MLP) model by finding the optimal set of weight and biases.

The efficiency of MHDA in training MLPs is evaluated by applying it to classification and approximation benchmark data sets. Performance comparison between MHDA and other training algorithms is carried out and the significance of results is proved by statistical methods. The computational complexity of MHDA trained MLP is estimated.

Simulation result shows that MHDA can effectively find the near optimum set of weight and biases at a higher convergence rate when compared to other training algorithms.

This paper presents MHDA as an alternative optimization algorithm for training MLP. MHDA can effectively optimize set of weight and biases and can be a potential trainer for MLPs.

This paper aims to provide a technical insight into a selection of recent developments of sensor based on metamaterials.

Following a short introduction, this first part discusses sensors based on acoustic metamaterials. It then briefly considers negative index materials and split ring resonators and provides examples of sensors based on metamaterials which interact with electromagnetic radiation in the microwave, terahertz and infra-red regions. Finally, brief concluding comments are drawn.

Since their discovery at around the turn of the century, metamaterials have been studied widely by the research community. A diverse range of sensors and imaging devices have since been developed which exploit the unique properties of these materials and respond to physical, chemical and biological variables. Many exhibit characteristics and capabilities with the potential to overcome the limitations of conventional devices.

This provides details of a range of recently developed sensors based on the newly discovered families of metamaterials.