Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA.

Multi-Strategy Dynamic Spectrum Access in Cognitive Radio Networks: Modeling, Analysis and Optimization

Dynamic spectrum access(DSA) based on cognitive radios(CR) technique is an effective approach to address the "spectrum scarcity" issue. However, traditional CR-enabled DSA system employs only single DSA strategy, which might not be suited to the dynamic network environment. In this paper, we propose a multi-strategy DSA(MS-DSA) system, where the primary and the secondary system share spectrum resources with multiple DSA strategies simultaneously. To analyze the performance of the proposed MS-DSA system, we model it as a continuous-time Markov chain(CTMC) and derive the expressions to compute the corresponding performance metrics. Based on this, we define a utility function involving the concerns of effective throughput, interference quantity on primary users, and spectrum leasing cost. Two optimization schemes, named as spectrum allocation and false alarm probability selection, are proposed to maximize the utility function. Finally, numerical simulations are provided to validate our analysis and demonstrate that the performance can be significantly improved caused by virtues of the proposed MS-DSA system.

An Improved Honey Badger Algorithm through Fusing Multi-Strategies

TheHoney Badger Algorithm(HBA)is a novelmeta-heuristic algorithm proposed recently inspired by the foraging behavior of honey badgers.The dynamic search behavior of honey badgers with sniffing and wandering is divided into exploration and exploitation in HBA,which has been applied in photovoltaic systems and optimization problems effectively.However,HBA tends to suffer from the local optimum and low convergence.To alleviate these challenges,an improved HBA(IHBA)through fusing multi-strategies is presented in the paper.It introduces Tent chaotic mapping and composite mutation factors to HBA,meanwhile,the random control parameter is improved,moreover,a diversified updating strategy of position is put forward to enhance the advantage between exploration and exploitation.IHBA is compared with 7 meta-heuristic algorithms in 10 benchmark functions and 5 engineering problems.The Wilcoxon Rank-sum Test,Friedman Test and Mann-WhitneyU Test are conducted after emulation.The results indicate the competitiveness and merits of the IHBA,which has better solution quality and convergence traits.The source code is currently available from:https://github.com/zhaotao789/IHBA.

Element doping induced microstructural engineering enhancing the lithium storage performance of high-nickel layered cathodes

The high-nickel layered cathodes Li[Ni_(x)Co_(y)Mn_(1-x-y)]O_(2)(x≥0.8)with high specific capacity and long cycle life are considered as prospective cathodes for lithium-ion batteries.However,the microcrack formation and poor structural stability give rise to inferior rate performance and undesirable cycling life.Herein,we propose a dual modification strategy combining primary particle structure design and element doping to modify Li[Ni_(0.95)Co_(0.025)Mn_(0.025)]O_(2) cathode by tungsten and fluorine co-doped(W-F-NCM95).The doping of W can convert the microstructure of primary particles to the unique rod-like shape,which is beneficial to enhance the reversibility of phase transition and alleviate the generation of microcracks.F doping is conducive to alleviating the surface side reactions.Thus,due to the synergistic effect of W,F codoping,the obtained W-F-NCM95 cathodes deliver a high initial capacity of 236.1 mA h g^(-1) at 0.1 C and superior capacity retention of 88.7%over 100 cycles at 0.5 C.Moreover,the capacity still maintains73.8%after 500 cycles at 0.5 C and the texture of primary particle is intact.This work provides an available strategy by W and F co-doping to enhance the electrochemistry performance of high-nickel cathodes for practical application.

Ontology Mapping Based on Bayesian Network

Ontology mapping is a key interoperability enabler for the semantic web. In this paper,a new ontology mapping approach called ontology mapping based on Bayesian network( OM-BN) is proposed. OM-BN combines the models of ontology and Bayesian Network,and applies the method of Multi-strategy to computing similarity. In OM-BN,the characteristics of ontology,such as tree structure and semantic inclusion relations among concepts,are used during the process of translation from ontology to ontology Bayesian network( OBN). Then the method of Multi-strategy is used to create similarity table( ST) for each concept-node in OBN. Finally,the iterative process of mapping reasoning is used to deduce new mappings from STs,repeatedly.

Whirligig‑Inspired Hybrid Nanogenerator for Multi‑strategy Energy Harvesting

Portable energy solutions are highly desired in the era of the Internet of Things for powering various distributed micro-electronic devices.At the same time,the energy crisis and catastrophic global warming are becoming serious problems in the world,emphasizing the urgent need for clean and renewable energy.Here,we report a low-cost,high-performance,and portable hand-driven whirligig structured triboelectric–electromagnetic hybrid nanogenerator(whirligig-HNG)for multi-strategy energy harvesting.The whirligig-HNG comprises a dynamic supercoiling TENG via the pulling-strings and inner-distributed EMGs(variable number)in the rotator.The whirligig structure can readily convert linear displacement in low frequency into rotary motion in extremely high frequency.Based on this ingenious design,the whirligig-HNG is capable to harvest the triboelectric energy from the supercoiling/uncoiling process from the pulling strings and simultaneously utilize the high-frequency rotation energy via electromagnetic induction.We have systematically investigated the working mecha-nism of the whirligig-HNG for coupled energy harvesting and compared the individual characteristics of TENG and EMG.The whirligig-HNG is successfully demonstrated to light up more than 100 commercial light-emitting diodes(LEDs)and drive portable electronics.This research presents the enormous potential of whirligig-HNG as a manual and portable power supply for powering various portable electronics.