Improved Measurement of the Differential Polarizability Using Co-Trapped Ions.

We present a novel approach for measuring the differential static scalar polarizability of a target ion utilizing a "polarizability scale" scheme with a reference ion co-trapped in a linear Paul trap. The differential static scalar polarizability of the target ion can be precisely extracted by measuring the ratio of the ac Stark shifts induced by an add-on infrared laser shed on both ions. This method circumvents the need for the calibration of the intensity of the add-on laser, which is usually the bottleneck for measurements of the polarizability of trapped ions. As a demonstration, ^{27}Al^{+} (the target ion) and ^{40}Ca^{+} (the reference ion) are used in this work, with an add-on laser at 1068 nm injected into the ion trap along the trap axis. The differential static scalar polarizability of ^{27}Al^{+} is extracted to be 0.416(14) a.u. by measuring the ratio of the ac Stark shifts of both ions. Compared to the most recent result [Phys. Rev. Lett. 123, 033201 (2019)PRLTAO0031-900710.1103/PhysRevLett.123.033201], the relative uncertainty of the differential static scalar polarizability of ^{27}Al^{+} is reduced by approximately a factor of 4, to 3.4%. This improvement is expected to be further enhanced by using an add-on laser with a longer wavelength.

[Study on the Supercontinuum Generation with Femtosecond Pulse in Photonic Crystal Fiber].

Physical mechanism of supercontinuum generation in photonic crystal fiber by femtosecond laser pulse has been investigated experimentally. In this study, we used the tunable output wavelength Ti: sapphire optical parametric amplifier as the pump source and the fiber spectrometer acquired the spectrogram of supercontinuum generation in photonic crystal fiber under different power and wavelength conditions, then we normalized the spectrograms and make a comparison of them. PCF supercontinuum differences affected by physical mechanisms were analyzed. We found that when increasing the incident pump pulse power, the spectral width will be gradually widened, there are more peaks, part of the energy will transfer in to the short-wave- length region; as long as it reaches a certain intensity, width of supercontinuum finally saturated, the shape of supercontinuum was also stabilized. As the incident power was settled at 300 milliwatt and the length of PCF was settled at 105 millimeter, experimental results show that width and shape of supercontinuum are affected by the wavelength of pump pulse, in the range of 760 to 840 nm, there appears more and more peaks with the increase of incident wavelength; at anomalous dispersion the spectrogram of supercontinuum generation will be more flat and more wider as the wavelength of pump pulse closer to zero point.

A multi-objective African vultures optimization algorithm with binary hierarchical structure and tree topology for big data optimization.

INTRODUCTION: Big data optimization (Big-Opt) problems present unique challenges in effectively managing and optimizing the analytical properties inherent in large-scale datasets. The complexity and size of these problems render traditional data processing methods insufficient. OBJECTIVES: In this study, we propose a new multi-objective optimization algorithm called the multi-objective African vulture optimization algorithm with binary hierarchical structure and tree topology (MO_Tree_BHSAVOA) to solve Big-Opt problem. METHODS: In MO_Tree_BHSAVOA, a binary hierarchical structure (BHS) is incorporated to effectively balance exploration and exploitation capabilities within the algorithm; shift density estimation is introduced as a mechanism for providing selection pressure for population evolution; and a tree topology is employed to reinforce the algorithm's ability to escape local optima and preserve optimal non-dominated solutions. The performance of the proposed algorithm is evaluated using CEC 2020 multi-modal multi-objective benchmark functions and CEC 2021 real-world constrained multi-objective optimization problems and is applied to Big-Opt problems. RESULTS: The performance is analyzed by comparing the results obtained with other multi-objective optimization algorithms and using Friedman's statistical test. The results show that the proposed MO_Tree_BHSAVOA not only provides very competitive results, but also outperforms other algorithms. CONCLUSION: These findings validate the effectiveness and potential applicability of MO_Tree_BHSAVOA in addressing the optimization challenges associated with big data.

Hybrid Whale Optimization with a Firefly Algorithm for Function Optimization and Mobile Robot Path Planning.

With the wide application of mobile robots, mobile robot path planning (MRPP) has attracted the attention of scholars, and many metaheuristic algorithms have been used to solve MRPP. Swarm-based algorithms are suitable for solving MRPP due to their population-based computational approach. Hence, this paper utilizes the Whale Optimization Algorithm (WOA) to address the problem, aiming to improve the solution accuracy. Whale optimization algorithm (WOA) is an algorithm that imitates whale foraging behavior, and the firefly algorithm (FA) is an algorithm that imitates firefly behavior. This paper proposes a hybrid firefly-whale optimization algorithm (FWOA) based on multi-population and opposite-based learning using the above algorithms. This algorithm can quickly find the optimal path in the complex mobile robot working environment and can balance exploitation and exploration. In order to verify the FWOA's performance, 23 benchmark functions have been used to test the FWOA, and they are used to optimize the MRPP. The FWOA is compared with ten other classical metaheuristic algorithms. The results clearly highlight the remarkable performance of the Whale Optimization Algorithm (WOA) in terms of convergence speed and exploration capability, surpassing other algorithms. Consequently, when compared to the most advanced metaheuristic algorithm, FWOA proves to be a strong competitor.

Advances in Slime Mould Algorithm: A Comprehensive Survey.

The slime mould algorithm (SMA) is a new swarm intelligence algorithm inspired by the oscillatory behavior of slime moulds during foraging. Numerous researchers have widely applied the SMA and its variants in various domains in the field and proved its value by conducting various literatures. In this paper, a comprehensive review of the SMA is introduced, which is based on 130 articles obtained from Google Scholar between 2022 and 2023. In this study, firstly, the SMA theory is described. Secondly, the improved SMA variants are provided and categorized according to the approach used to apply them. Finally, we also discuss the main applications domains of the SMA, such as engineering optimization, energy optimization, machine learning, network, scheduling optimization, and image segmentation. This review presents some research suggestions for researchers interested in this algorithm, such as conducting additional research on multi-objective and discrete SMAs and extending this to neural networks and extreme learning machining.

Optimal stochastic power flow using enhanced multi-objective mayfly algorithm.

For the classical multi-objective optimal power flow (MOOPF) problem, only traditional thermal power generators are used in power systems. However, there is an increasing interest in renewable energy sources and the MOOPF problem using wind and solar energy has been raised to replace part of the thermal generators in the system with wind turbines and solar photovoltaics (PV) generators. The optimization objectives of MOOPF with renewable energy sources vary with the study case. They are mainly a combination of 2-4 objectives from fuel cost, emissions, power loss and voltage deviation (VD). In addition, reasonable prediction of renewable power is a major difficulty due to the discontinuous, disordered and unstable nature of renewable energy. In this paper, the Weibull probability distribution function (PDF) and lognormal PDF are applied to evaluate the available wind and available solar power, respectively. In this paper, an enhanced multi-objective mayfly algorithm (NSMA-SF) based on non-dominated sorting and the superiority of feasible solutions is implemented to tackle the MOOPF problem with wind and solar energy. The algorithm NSMA-SF is applied to the modified IEEE-30 and standard IEEE-57 bus test systems. The simulation results are analyzed and compared with the recently reported MOOPF results.

Hybrid Manta Ray Foraging Algorithm with Cuckoo Search for Global Optimization and Three-Dimensional Wireless Sensor Network Deployment Problem.

In this paper, a new hybrid Manta Ray Foraging Optimization (MRFO) with Cuckoo Search (CS) algorithm (AMRFOCS) is proposed. Firstly, quantum bit Bloch spherical coordinate coding is used for the initialization of the population, which improves the diversity of the expansion of the traversal ability of the search space. Secondly, the dynamic disturbance factor is introduced to balance the exploratory and exploitative search ability of the algorithm. Finally, the unique nesting strategy of the cuckoo and Levy flight is introduced to enhance the search ability. AMRFOCS is tested on CEC2017 and CEC2020 benchmark functions, which is also compared and tested by using different dimensions and other state-of-the-art metaheuristic algorithms. Experimental results reveal that the AMRFOCS algorithm has a superior convergence rate and optimization precision. At the same time, the nonparametric Wilcoxon signed-rank test and Friedman test show that the AMRFOCS has good stability and superiority. In addition, the proposed AMRFOCS is applied to the three-dimensional WSN coverage problem. Compared with the other four 3D deployment methods optimized by metaheuristic algorithms, the AMRFOCS effectively reduces the redundancy of sensor nodes, possesses a faster convergence speed and higher coverage and then provides a more effective and practical deployment scheme.

Efficiently loading 40Ca+-27Al+ ion crystal using sympathetic cooling and pulsed laser ablation.

We demonstrate a method to efficiently load a pair of 40Ca+-27Al+ ion crystals with sympathetic cooling and pulsed laser ablation, serving as a starting step for the 27Al+ clock. We achieved a technique to rapidly detect the loading of hot ions by monitoring the 2S1/2 → 2D5/2 narrow transition of 40Ca+ that couples to the shared motional modes between the two ions. The sympathetic cooling time of the 40Ca+-27Al+ ion pair is measured. Two traps are employed to compare the loading time from two directions and it was found that the loading from the axial direction takes much shorter time than loading from the radial direction of the trap. With the help of adaptively controlled trap potential, our method reduced the average loading time of a 40Ca+-27Al+ pair from 26 to 1 min. This research is an important step for increasing the uptime ratio of the 27Al+ optical clock and is useful for other mixed-species ion crystals based on sympathetic cooling.

An efficient binary Gradient-based optimizer for feature selection.

Feature selection (FS) is a classic and challenging optimization task in the field of machine learning and data mining. Gradient-based optimizer (GBO) is a recently developed metaheuristic with population-based characteristics inspired by gradient-based Newton's method that uses two main operators: the gradient search rule (GSR), the local escape operator (LEO) and a set of vectors to explore the search space for solving continuous problems. This article presents a binary GBO (BGBO) algorithm and for feature selecting problems. The eight independent GBO variants are proposed, and eight transfer functions divided into two families of S-shaped and V-shaped are evaluated to map the search space to a discrete space of research. To verify the performance of the proposed binary GBO algorithm, 18 well-known UCI datasets and 10 high-dimensional datasets are tested and compared with other advanced FS methods. The experimental results show that among the proposed binary GBO algorithms has the best comprehensive performance and has better performance than other well known metaheuristic algorithms in terms of the performance measures.