Aquila Optimizer (AO) and Harris Hawks (HHO) are recently proposed meta-heuristic optimization algorithms. AO possesses strong global exploration capability but insufficient local exploitation ability. However, the phase of HHO is pretty good, while far from satisfactory. Considering characteristics these two algorithms, an improved hybrid combined with a nonlinear escaping energy parameter random opposition-based learning strategy proposed, namely IHAOHHO, to improve searching performance...

The sand cat swarm optimization algorithm (SCSO) is a recently proposed metaheuristic algorithm. It stimulates the hunting behavior of cat, which attacks or searches for prey according to sound frequency; each aims catch better prey. Therefore, will search location In SCSO algorithm, gradually approach its prey, makes strong exploitation ability. However, in later stage prone fall into local optimum, making it unable find position. order improve mobility and exploration ability this paper,...

Image segmentation is a key stage in image processing because it simplifies the representation of and facilitates subsequent analysis. The multi-level thresholding technique considered one most popular methods efficient straightforward. Many relative works use meta-heuristic algorithms (MAs) to determine threshold values, but they have issues such as poor convergence accuracy stagnation into local optimal solutions. Therefore, alleviate these shortcomings, this paper, we present modified...

In this paper, a new hybrid algorithm based on two meta-heuristic algorithms is presented to improve the optimization capability of original algorithms. This realized by deep ensemble proposed methods, i.e., slime mold (SMA) and arithmetic (AOA), called DESMAOA. To be specific, preliminary method was applied obtain improved SMA, SMAOA. Then, strategies that were extracted from SMA AOA, respectively, embedded into SMAOA boost optimizing speed accuracy solution. The performance DESMAOA...

This paper presents an improved teaching-learning-based optimization (TLBO) algorithm for solving problems, called RLTLBO. First, a new learning mode considering the effect of teacher is presented. Second, Q-Learning method in reinforcement (RL) introduced to build switching mechanism between two different modes learner phase. Finally, ROBL adopted after both and phases improve local optima avoidance ability These strategies effectively enhance convergence speed accuracy proposed algorithm....

The reptile search algorithm (RSA) is a bionic proposed by Abualigah. et al. in 2020. RSA simulates the whole process of crocodiles encircling and catching prey. Specifically, stage includes high walking belly walking, hunting coordination cooperation. However, middle later stages iteration, most agents will move towards optimal solution. if solution falls into local optimum, population fall stagnation. Therefore, cannot converge when solving complex problems. To enable to solve more...

Arithmetic optimization algorithm (AOA) is a newly proposed meta-heuristic method which inspired by the arithmetic operators in mathematics. However, AOA has weaknesses of insufficient exploration capability and likely to fall into local optima. To improve searching quality original AOA, this paper presents an improved (IAOA) integrated with forced switching mechanism (FSM). The enhanced uses random math optimizer probability (RMOP) increase population diversity for better global search. And...

Remora Optimization Algorithm (ROA) is a metaheuristic optimization algorithm, proposed in 2021, which simulates the parasitic attachment, experiential attack, and host feeding behavior of remora ocean. However, performance ROA not very good. Considering habits that rely on to find food, order improve ROA, we designed new host-switching mechanism. By adding mechanism, joint opposite selection, restart strategy, modified algorithm (MROA) proposed. We use 23 standard benchmark CEC2020...

Arithmetic Optimization Algorithm (AOA) is a physically inspired optimization algorithm that mimics arithmetic operators in mathematical calculation. Although the AOA has an acceptable exploration and exploitation ability, it also some shortcomings such as low population diversity, premature convergence, easy stagnation into local optimal solutions. The Golden Sine (Gold-SA) strong searchability fewer coefficients. To alleviate above issues improve performance of AOA, this paper, we present...

Discovery of small-molecule antibiotics with novel chemotypes serves as one the essential strategies to address antibiotic resistance. Although a considerable number computational tools committed molecular design have been reported, there is deficit in holistic and efficient specifically developed for discovery. To this issue, we report AutoMolDesigner, modeling software dedicated design. It generalized framework comprising two functional modules, i.e., generative-deep-learning-enabled...

Based on Salp Swarm Algorithm (SSA) and Slime Mould (SMA), a novel hybrid optimization algorithm, named Hybrid (HSMSSA), is proposed to solve constrained engineering problems. SSA can obtain good results in solving some However, it easy suffer from local minima lower density of population. SMA specializes global exploration robustness, but its convergence rate too slow find satisfactory solutions efficiently. Thus, this paper, considering the characteristics advantages both above algorithms,...

This paper introduces an improved hybrid Aquila Optimizer (AO) and Harris Hawks Optimization (HHO) algorithm, namely IHAOHHO, to enhance the searching performance for global optimization problems. In valuable exploration exploitation capabilities of AO HHO are retained firstly, then representative-based hunting (RH) opposition-based learning (OBL) strategies added in phases effectively improve diversity search space local optima avoidance capability respectively. To verify practicability,...

One of the most popular population-based metaheuristic algorithms is Harris hawks optimization (HHO), which imitates hunting mechanisms in nature. Although HHO can obtain optimal solutions for specific problems, it stagnates local optima solutions. In this paper, an improved named ERHHO proposed solving global problems. Firstly, we introduce tent chaotic map initialization stage to improve diversity population. Secondly, exploration factor optimize parameters improving ability exploration....

The group teaching optimization algorithm (GTOA) is a meta heuristic simulating the mechanism. inspiration of GTOA comes from Each student will learn knowledge obtained in teacher phase, but each student’s autonomy weak. This paper considers that has different learning motivations. Elite students have strong self-learning ability, while ordinary general motivation. To solve this problem, proposes motivation strategy and adds random opposition-based restart to enhance global performance...

The Gorilla Troops Optimizer (GTO) is a novel Metaheuristic Algorithm that was proposed in 2021. Its design inspired by the lifestyle characteristics of gorillas, including migration to known position, an undiscovered moving toward other following silverback gorillas and competing with for females. However, like Algorithms, GTO still suffers from local optimum, low diversity, imbalanced utilization, etc. In order improve performance GTO, this paper proposes modified (MGTO). improvement...

The artificial Gorilla Troop Optimization (GTO) algorithm is a metaheuristic optimization that simulates the social life of gorillas. This paper proposes three innovative strategies considering GTO algorithm’s insufficient convergence accuracy and low speed. First, shrinkage control factor fusion strategy proposed to expand search space reduce blindness by strengthening communication between silverback gorillas other improve global performance. Second, sine cosine interaction based on...

This paper proposes Advanced Fast Recovery OLSR (AFR-OLSR) for Unmanned Aerial Vehicles (UAVs) with sudden link outages, and implements a multipath version of this protocol. AFR-OLSR can maintain high packet delivery ratio hardly bring extra network delay in the scenario nodes suddenly offline or moving at speed UAV swarm. We improve route calculation rule by defining new state using penalty function to reduce priority selecting state. use constant bit rate (CBR) application running on...

Slime mould algorithm (SMA) is a new metaheuristic proposed in 2020, which has attracted extensive attention from scholars. Similar to other optimization algorithms, SMA also the drawbacks of slow convergence rate and being trapped local optimum at times. Therefore, enhanced named as ESMA presented this paper for solving global problems. Two effective methods composed multiple mutation strategy (MMS) restart mechanism (RM) are embedded into original SMA. MMS utilized increase population...

Recently, a new swarm intelligence optimization algorithm called the remora (ROA) was proposed. ROA simulates remora’s behavior of adsorption host and uses some formulas sailfish (SFO) whale (WOA) to update solutions. However, performance is still unsatisfactory. When solving complex problems, ROA’s convergence ability requires further improvement. Moreover, it easy fall into local optimization. Since depends on obtain food optimize performance, this paper introduces mutualistic strategy...

We apply high-resolution angle-resolved photoemission spectroscopy to explore the layered transition-metal dichalcogenide $1T\ensuremath{-}{\mathrm{VSe}}_{2}$ with various photon energies investigate three-dimensional (3D) charge-density wave (CDW) transition. Here, we provide quite comprehensive evidence of 3D Fermi surface nesting in $1T\ensuremath{-}{\mathrm{VSe}}_{2}$. The observed (FS) largely overlaps in-plane vector reported by electron-diffraction results. Comparing CDW gaps below...

In order to extract the purer fetal ECG accurately, a BSS method based on higher-order statistics is contrasted with significant classical technique for FECG extraction. The fast ICA algorithm and infomax are used They were applied multichannel recording obtained from pregnant woman. It was observed that both algorithms able ECG. But experiment outcomes demonstrate better performance fast-ICA in biomedical application. This has been proved be desired heart beat signal composite abdominal signal.

Multilevel thresholding is a widely used method in image segmentation. However, the traditional methods are costly to obtain optimal thresholds through exhaustive search. The Nature-inspired algorithm gradient-free optimizer that overcomes these shortcomings and generates best with high quality efficiency. For this purpose, paper suggests an improved arithmetic optimization federated opposite learning for multilevel segmentation, namely FOL-AOA. In method, strategy incorporated avoid...

Particle swarm optimization (PSO) stands as a prominent and robust meta-heuristic algorithm within intelligence (SI). It originated in 1995 by simulating the foraging behavior of bird flocks. In recent years, numerous PSO variants have been proposed to address various applications. However, overall performance these has not deemed satisfactory. This article introduces novel variant, presenting three key contributions: First, dynamic oscillation inertia weight is introduced strike balance...

Abstract Antibiotic resistance has become a serious threat to public health, thus novel antibiotics are urgently needed combat drug-resistant bacteria including MRSA (methicillin-resistant S. aureus ). The 1,4-dicarbonylthiosemicarbazide is an interesting chemotype that could exhibit antibacterial activity. However, the currently available compounds not as potent clinical antibiotics. Herein, we adopted computer-aided drug design strategy, substructure search, retrieve derivatives, and...