A new multi-objective optimization framework is presented for designing large mode area photonic crystal fibers (LMA-PCFs) with effective single-mode operation in the bent state. For optimizing the structure, we utilize the Multi-Objective Gray Wolf Optimizer (MOGWO) to maximize the effective mode area (EMA) and the bending loss of higher order modes (HOMs), while minimizing the fundamental mode (FM) loss. The simulation results demonstrate that this framework enables us to improve the EMA by a factor of 1.26 and increase (decrease) the bending loss of the HOMs (the FM) by a factor of 7 (17), compared to the non-optimal design. In addition, we investigate the dependence of the optical characteristics of the optimized LMA-PCFs on the wavelength and the bending radius. We found that some optimal structures are highly wavelength dependent and are not suitable for wideband applications. Furthermore, we found that the HOM loss is very sensitive to the bending radius. The proposed framework is comprehensive and can be employed to find a broad range of optimal designs for a wide range of applications.

In this paper, we present a set of optimal graphene-based multilayer surface plasmon resonance (SPR) biosensors for highly sensitive detection of biomolecules. To optimize the biosensor structure, we employed a multi-objective gray wolf optimizer (MOGWO) to maximize the sensitivity and minimize the structure full width at half maximum (FWHM). The main advantages of the optimized structures are high sensitivity, low FWHM, as well as easy implementation. We developed an algorithm that enables us to achieve nine different optimized structures. The best sensitivity, FWHM and FOM are obtained equal to 264.6°/RIU (for the structure #5), 1.905° and 56.6/RIU (for the structure #8), respectively. The results of this paper pave the way for the development of highly-sensitive SPR biosensors.

Because of the existence of irrelevant, redundant, and noisy attributes in large datasets, the accuracy of a classification model has degraded. Hence, feature selection is a necessary pre-processing stage to select the important features that may considerably increase the efficiency of underlying classification algorithms. As a popular metaheuristic algorithm, particle swarm optimisation has successfully applied to various feature selection approaches. Nevertheless, particle swarm optimisation tends to suffer from immature convergence and low convergence rate. Besides, the imbalance between exploration and exploitation is another key issue that can significantly affect the performance of particle swarm optimisation. In this paper, a conditional opposition-based particle swarm optimisation is proposed and used to develop a wrapper feature selection. Two schemes, namely opposition-based learning and conditional strategy are introduced to enhance the performance of the particle swarm optimisation. Twenty-four benchmark datasets are used to validate the performance of the proposed approach. Furthermore, nine metaheuristics are chosen for performance verification. The findings show the supremacy of the proposed approach not only in obtaining high prediction accuracy but also in small feature sizes.

In laser science and industry, considerable effort is directed toward designing fibers for fiber laser and fiber amplifier applications, each of which offers a particular advantage over the others. Evanescently coupled multicore fibers, however, have been studied less extensively due to the relatively small mode area in the single-mode regime. Here, by proposing a new structure with stress-applying parts in a 37-core fiber and optimizing this structure through a comprehensive framework, we present 21 solutions characterized by large-mode-area and high beam quality in the single-mode, single-polarization regime. Different fiber designs are optimal for different output parameters. In one design, the mode area can significantly increase to above 880 $\mathsf{\mu }$m${}^2$, which is comparable with that of photonic-crystal fibers. Moreover, besides the single-mode operation, the beam quality factor ($M^2$ factor) of the fundamental mode is considered an output parameter in the bent state and is improved up to 1.05 in another design. A comprehensive tolerance analysis is then performed to assess the performance of the designs under deviations from normal conditions. Moreover, in spite of the shifts in the loss of modes, the proposed high beam quality LMA fibers maintain single-polarization, single-mode operation across a wide range of core pitches, bending orientation angles, and bending radius deviations. Our results highlight the potential of multicore fibers for the efficient operation of fiber lasers and amplifiers.