The whale optimization algorithm (WOA) is a new biological meta‐heuristic algorithm based on the social hunting behaviors of humpback whales. However, it can easily fall into a local optimum when solving complex problems and exhibits slow convergence speed and poor exploration. This study proposed three improved versions of the WOA based on the concepts of chaos initialization, nonlinear convergence factor, and chaotic inertial weight to enhance its exploration abilities. These properties were employed to improve the population diversity and maintain the balance between exploration and exploitation. The performance of the best version was compared with those of moth‐flame optimization, firefly algorithm, particle swarm optimization, gray wolf optimizer, flower pollination algorithm, original WOA, and two recently proposed hybrid WOA through 19 benchmark functions. Experimental results indicated that the proposed algorithms exhibit better performance in terms of complexity and convergence speed.

Whale optimization algorithm based on nonlinear convergence factor and chaotic inertial weight

The paper shows the simulation of the behavior of a swarm of underwater drones (AUV) diffused in a closed section of the sea and inserted from a single starting point: Based on a few essential rules, we will see how their behavior evolves and how they manage to spread throughout the area assigned to them. In the first part of this work, after defining the design of the vehicle, we introduce our vision of the swarm, its problems, and its strengths. Later, we show how to spread a series of underwater drones with “diffused intelligence” (swarm) and its microscopic diffusion model. In the last part, we present the simulation that supports our approach to the swarm.

https://www.mdpi.com/1424-8220/20/17/4950/pdf

Simulation of Autonomous Underwater Vehicles (AUVs) Swarm Diffusion.

The real-life signals captured by different measurement systems (such as modern maritime transport characterized by challenging and varying operating conditions) are often subject to various types of noise and other external factors in the data collection and transmission processes. Therefore, the filtering algorithms are required to reduce the noise level in measured signals, thus enabling more efficient extraction of useful information. This paper proposes a locally-adaptive filtering algorithm based on the radial basis function (RBF) kernel smoother with variable width. The kernel width is calculated using the asymmetrical combined-window relative intersection of confidence intervals (RICI) algorithm, whose parameters are adjusted by applying the particle swarm optimization (PSO) based procedure. The proposed RBF-RICI algorithm’s filtering performances are analyzed on several simulated, synthetic noisy signals, showing its efficiency in noise suppression and filtering error reduction. Moreover, compared to the competing filtering algorithms, the proposed algorithm provides better or competitive filtering performance in most considered test cases. Finally, the proposed algorithm is applied to the noisy measured maritime data, proving to be a possible solution for a successful practical application in data filtering in maritime transport and other sectors.

https://www.mdpi.com/2077-1312/9/4/439/pdf

Particle-Swarm-Optimization-Enhanced Radial-Basis-Function-Kernel-Based Adaptive Filtering Applied to Maritime Data

The efficiency of direct methanol fuel cell (DMFC) is largely determined by the activity and durability of methanol oxidation reaction (MOR) catalysts. Herein, we present a CO-resilient MOR catalyst of palladium-tin nano-alloy anchored on Se-doped MXene (PdSn 0.5 /Se-Ti 3 C 2 ) via a progressive one-step electrochemical deposition strategy. MOR mass activity resulting from Pd/Se-Ti 3 C 2 catalyst (1046.2 mA mg -1 ) is over 2-fold larger than that of Pd/Ti 3 C 2 , suggesting that the introduction of Se atoms on MXene might accelerate the reaction kinetics. PdSn 0.5 /Se-Ti 3 C 2 with Se-doping progress of MXene and the cooperated Pd-Sn sites has a superior MOR mass activity (4762.8 mA mg -1 ), outperforming many other reported Pd-based catalysts. Both experimental results and theoretical calculation reveal that boosted electron interaction of metal crystals with Se-doped MXene and optimized distribution of Pd-Sn sites can modulate the d band center, reduce adsorption energies of CO* at Pd site and enhance OH* generation at Sn site, resulting in highly efficient removal of CO intermediates by reaction with neighboring OH species on adjacent Sn sites.

Engineering Support and Distribution of Palladium and Tin on MXene with the Modulation d-Band Center for CO-resilient Methanol Oxidation.

As a new assistant navigation technology using geophysical field for navigation, geomagnetic matching navigation can effectively alleviate the problems such as the unavailability of satellite and the easy divergence of position data of inertial navigation system in the process of navigation. It can also carry out real-time assistant navigation with high concealment, all-around area and all-weather. According to the principle of geomagnetic matching and the geomagnetic affine model, considering that the basic particle swarm optimization algorithm is easy to fall into local extremum, this paper introduces particle swarm optimization geomagnetic matching algorithm based on simulated annealing(SAPSO) for limitations of traditional matching algorithm. What’s more, the SAPSO is improved from three parts: constraints, parameters and function of fitness. Finally, the simulation analysis is carried out from five aspects to verify the effectiveness and accuracy of the improved SAPSO.

/pdf/improved-particle-swarm-optimization-geomagnetic-matching-4x3trxl1vl.pdf

Improved Particle Swarm Optimization Geomagnetic Matching Algorithm Based on Simulated Annealing

Heteroatom doping can endow MXenes with various new or improved electromagnetic, physicochemical, optical, and structural properties. This greatly extends the arsenal of MXenes materials and their potential for a spectrum of applications. This article comprehensively and critically discusses the syntheses, properties, and emerging applications of the growing family of heteroatom-doped MXenes materials. First, the doping strategies, synthesis methods, and theoretical simulations of high-performance MXenes materials are summarized. In order to achieve high-performance MXenes materials, the mechanism of atomic element doping from three aspects of lattice optimization, functional substitution, and interface modification is analyzed and summarized, aiming to provide clues for developing new and controllable synthetic routes. The mechanisms underlying their advantageous uses for energy storage, catalysis, sensors, environmental purification and biomedicine are highlighted. Finally, future opportunities and challenges for the study and application of multifunctional high-performance MXenes are presented. This work could open up new prospects for the development of high-performance MXenes.

Element-Doped Mxenes: Mechanism, Synthesis, and Applications.

Particle swarm optimization (PSO) is an algorithm widely used to solve optimization problems. Multi-swarm particle swarm optimization (MSPSO) is a form of particle swarm optimization (PSO). The swarm size of the multi-swarm particle swarm optimization (MSPSO) algorithm is an important characteristic of the algorithm. The number of particles and the number of swarms will affect the performance of the algorithm to varying degrees. The algorithm performance of multi-swarm particle swarm optimization algorithm is studied from the perspective of swarm size.

Research on Swarm Size of Multi-swarm Particle Swarm Optimization Algorithm

Synthesis of Luminol and Chemiluminescence Oscillation

A multi-swarm particle swarm optimization algorithm based on clustering dynamic grouping(DGMSPSO) is presented for the problem of easy to getting struck at local optima in optimization process and low precision of the particle swarm optimization algorithm. The clustering grouping strategy is introduced to enhance the location information of the particles to each other in the search process to improve the diversity of the algorithm. Local search ability and global ability of the algorithm are balanced with the dynamic adjustment strategy. The results of simulations on the test functions show that the dynamic grouping process is stable, feasible and effective, and has fast convergence and high accuracy compared with several other PSO-based algorithms in terms of global searching ability and convergent speed.

Yuhao Zhang

Papers

Element-Doped Mxenes: Mechanism, Synthesis, and Applications.

Research on Swarm Size of Multi-swarm Particle Swarm Optimization Algorithm

Synthesis of Luminol and Chemiluminescence Oscillation

Multi-swarm Particle Swarm Optimization Algorithm Based on Clustering Dynamic Grouping

Improving the Semi-Synthesis and Antibacterial Study of Penicillin V Potassium as a Chemical Biology Comprehensive Experiment