A Transformation Method of Coupling Matrix Reduction with Hybrid Optimization Algorithm

Wireless Sensor Networks (WSN) utilizes more nodes to forward messages and information from source node to destination node and these nodes are battery-powered devices. Thus, Energy saving is always vital role of Routing Protocols which need to maximize the lifetime of WSN. Mobile ad hoc networks (MANET) are self-starting and self-constructing multichip wireless networks in which, the assembly of the system deviates vigorously. This is mainly due to the mobility of the nodes. The nodes in the network not only act as hosts but also as routers that route data from other nodes in network. MANETs require an efficient routing protocol that achieves the quality of service (QoS) mechanism. They are not considering the mobility as one constraint for those problems. Because, mobility of nodes causes frequent link failures and high error rates, so it makes difficult to maintain the desired QoS in the network. To overcome those problems, we propose mobility aware routing protocol for MANET using hybrid optimization (MARP-HO) algorithm, which maximizes the QoS in data transmission. The proposed routing protocol comprises of two phases. The clustering process consists of cluster formation and cluster head (CH) selection. First, the energy efficient clustering is done by improved animal migration optimization (IAMO) algorithm. Then, we gather multiple constraints are energy consumption, received signal strength, mobility and cooperative rate from each cluster member. The gathered constraints used to compute the node cost of each cluster member, here the highest cost node act as CH of cluster. Second, we compute routing path between source–destination using improved ant colony optimization (IACO) algorithm. The proposed MARP-HO routing protocol is compared with existing state-of-art routing protocols in terms of energy consumption, delivery ratio, loss ratio, link stability, delay, network lifetime, number of dead nodes and throughput.

In this work, optimization methodologies of the kinematics of flapping wings in forward and hover flights are considered. Particularly, local and global optimization algorithms are combined with the unsteady vortex lattice method (UVLM) to determine the most efficient kinematics. In the first problem, the kinematic optimization of a three-dimensional flapping wing in forward flight with active shape morphing is aimed at maximizing the propulsive efficiency under lift and thrust constraints. Results show that due to the quasi-concavity of the objective function associated with the flapping kinematics employed in forward flight, local gradient-based optimizers perform very well in terms of accuracy and computational cost. In the second problem, the objective is to identify the optimized kinematics of a twodimensional hovering wing that minimize the aerodynamic power under lift constraint. The results show that using a hybrid optimization algorithm (combination of local and global schemes) accelerates the convergence to optimal points and avoids being trapped at local minimum points. While the efficiency of using a local optimizer, a global optimizer, or a hybrid of the two depends on the nature of the problem and associated design space, it is determined that hybrid optimization is best suited for determining local minima.

Electromagnetic (EM) parametric modeling of microwave filters is a significant research direction in the microwave filter design and optimization field. The parametric model aims to predict the EM response (such as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$S$</tex>-parameters) based on the input geometrical parameters, so it is crucial to establish an accurate and effective parametric model. In this paper, a parametric modeling method that combines artificial neural network (ANN) with coupling matrix (CM) is applied to the parametric modeling of a fifth-order filter. A center-out optimization algorithm combined with phase de-embedding is proposed to extract CM. Due to the strong continuity of the extracted coupling parameters, the established parametric model can be valid in a wider geometrical neighborhood.

Breast cancer is the second most frequent malignant tumor in the world. Early findings of breast cancer can significantly improve treatment effectiveness. Manual methods of breast cancer diagnosis are prone to human fault and inaccuracy, and they take time. A computer‐aided diagnosis can assist radiologists in making better choices by overcoming the disadvantages of manual methods. One of the significant steps in the breast cancer diagnosis process is feature selection. In recent decades, many studies have proposed numerous hybrid optimization methods to select the optimal features in the breast cancer detection system. However, many hybrid optimization algorithms are trapped in local optima and have slow convergence speed. Thus, it reduces the classification accuracy. For resolving these issues, this work proposes a hybrid optimization algorithm that combines the grasshopper optimization algorithm and the crow search algorithm for feature selection and classification of the breast mass with multilayer perceptron. The simulation is experimented with using MATLAB 2019a. The efficacy of the proposed hybrid grasshopper optimization‐crow search algorithm with multilayer perceptron system is compared to multilayer perceptron based algorithms of enhanced and adaptive genetic algorithm, teaching learning‐based whale optimization algorithm, butterfly optimization algorithm, whale optimization algorithm, and grasshopper optimization algorithm. From the results obtained, the proposed grasshopper optimization‐crow search algorithm with the multilayer perceptron method outperforms the comparative models in terms of classification accuracy (97.1%), sensitivity (98%), and specificity (95.4%) for the mammographic image analysis society dataset.

SummaryMobility prediction and fault tolerance are extremely difficult due to underwater characteristics. Energy drain is one of the major causes for node faults. Hence, in this research article, a hybrid optimization algorithm is developed for fault‐tolerant and accurate localization in UWSN. In this technique, Artificial Butterfly Optimization (ABO) algorithm is applied for finding the distance between the anchors and the sensors. Each non‐localized node runs ABO algorithm for finding the distance amid the anchor or beacon nodes. Then, applying Quaternion‐based Backtracking Search Optimization (QBSA) algorithm, non‐localized sensor nodes are localized and to decrease the localization error based on the Received Signal Strength Indicator (RSSI), battery energy, and distance parameters. Aqua‐Sim a tool kit of NS2 is an open‐source software developed for Underwater Wireless Sensor Network research, and this proposed algorithm will be implemented using this software. By simulation results, it is shown that the proposed optimization algorithm reduces the localization error, latency, and cost and energy consumption.

Mass minimization and sensitivity analysis of high power modular multilevel converter

In this paper a new approach to the synthesis of coupling matrices for microwave filters is presented. The new approach represents an advance on existing direct and optimization methods for coupling matrix synthesis in that it will exhaustively discover all possible coupling matrix solutions for a network if more than one exists. This enables a selection to be made of the set of coupling values, resonator frequency offsets, parasitic coupling tolerance etc that will be best suited to the technology it is intended to realize the microwave filter with. To demonstrate the use of the method, the case of the recently – introduced ‘extended box’ (EB) coupling matrix configuration is taken. The EB represents a new class of filter configuration featuring a number of important advantages, one of which is the existence of multiple coupling matrix solutions for each prototype filtering function, eg 16 for 8th degree cases. This case is taken as an example to demonstrate the use of the synthesis method – yielding one solution suitable for dual-mode realization and one where some couplings are small enough to neglec

In allusion to the deficiencies of the ant colony optimization algorithm for solving the complex problem, the genetic algorithm is introduced into the ant colony optimization algorithm in order to propose a novel hybrid optimization (NHGACO) algorithm in this paper. In the NHGACO algorithm, the genetic algorithm is used to update the global optimal solution and the ant colony optimization algorithm is used to dynamically balance the global search ability and local search ability in order to improve the convergence speed. Finally, some complex benchmark functions are selected to prove the validity of the proposed NHGACO algorithm. The experiment results show that the proposed NHGACO algorithm can obtain the global optimal solution and avoid the phenomena of the stagnation, and take on the fast convergence and the better robustness.

SummaryMelanoma is the most prevalent type of skin cancer, affecting skin surface cells. The risk features of the disease are minimized by finding the disease at time or in the initial stage. Even through, various skin cancer detection methods are developed, detecting the disease using discriminative features still results a challenging task. An efficient detection strategy based on the proposed Lion Cat Swarm Optimization‐based Deep Neuro Fuzzy Network (LCSO‐based DNFN) is created to identify skin cancer in its early stages. The LCSO is the integration of the lion optimization algorithm (LOA) and cat swarm optimization (CSO) algorithm. To make treatment measure at right time increases the probability of survival and so detecting the disease using deep learning classifier is performed with hybrid optimization algorithm. The segmentation result is effective through the fusion model and it ensures to achieve accurate detection process. Rather than extracting the features, the proposed method generates optimal result through data augmentation. The performance of the proposed LCSO‐based DNFN is determined using skin disease dataset, and the introduced approach obtained outstanding performance with the measures of accuracy, sensitivity, and specificity with the values of 93.10%, 92.95%, and 92.87%, respectively.

Lately, the world is looking forward to replacing fossil fuels with renewable energy resources. Photovoltaic (PV) is benefited among renewable energies by its simple design, easy fitting, and fast expansion. For proper selection of controller gains for the PV system's control unit, this paper introduces a new hybrid optimization algorithm. The proposed optimization technique is applied to a PV system to enhance its performance under dynamic irradiance. The introduced hybrid optimization approach includes two optimization algorithms which are Teaching Learning Based Optimization (TLBO) and Equilibrium Optimizer (EO). Moreover, this paper presents a comparison with different optimization algorithms (Harmony Search (HS), Teaching Learning Based Optimization (TLBO), Cuttlefish Algorithm (CFA), and Emperor Penguin Optimizer (EPO)) to ensure the superiority of the newly proposed hybrid technique. The results display that the introduced TLBO-EO reduced the maximum overshoot by 30% and the steady-state error by 93% when compared to other techniques.

At present, the integration of microgrids into power systems presents significant power quality challenges in terms of the rising adoption of nonlinear loads and electric vehicles. Ensuring the stability and efficiency of the electrical network in this evolving landscape is crucial. This paper explores the implementation of cascading Proportional–Integral (PI-PI) and cascading Fractional-Order PI (FOPI-FOPI) controllers for a Distribution Static Compensator (DSTATCOM) in hybrid microgrids that include photovoltaic (PV) systems and fuel cells. A novel hybrid optimization algorithm, WSO-WOA, is introduced to enhance power quality. This algorithm leverages the strengths of the White Shark Optimization (WSO) algorithm and the Whale Optimization Algorithm (WOA), with WSO generating new candidate solutions and WOA exploring alternative search areas when WSO does not converge on optimal results. The proposed approach was rigorously tested through multiple case studies and compared with established metaheuristic algorithms. The findings demonstrate that the WSO-WOA hybrid algorithm significantly outperforms others in optimizing the PI-PI and FOPI-FOPI controllers. The WSO-WOA algorithm showed an improvement in accuracy, surpassing the other algorithms by approximately 7.29% to 14.1% in the tuning of the PI-PI controller and about 8.5% to 21.2% in the tuning of the FOPI-FOPI controller. Additionally, the results confirm the superior performance of the FOPI-FOPI controller over the PI-PI controller in enhancing the effectiveness of the DSTATCOM across various scenarios. The FOPI-FOPI provided controller a reduced settling time by at least 30.5–56.1%, resulting in marked improvements in voltage regulation and overall power quality within the microgrid.

PurposeMetaheuristic algorithms based on biology, evolutionary theory and physical principles, have been widely developed for complex global optimization. This paper aims to present a new hybrid optimization algorithm that combines the characteristics of biogeography-based optimization (BBO), invasive weed optimization (IWO) and genetic algorithms (GAs).Design/methodology/approachThe significant difference between the new algorithm and original optimizers is a periodic selection scheme for offspring. The selection criterion is a function of cyclic discharge and the fitness of populations. It differs from traditional optimization methods where the elite always gains advantages. With this method, fitter populations may still be rejected, while poorer ones might be likely retained. The selection scheme is applied to help escape from local optima and maintain solution diversity.FindingsThe efficiency of the proposed method is tested on 13 high-dimensional, nonlinear benchmark functions and a homogenous slope stability problem. The results of the benchmark function show that the new method performs well in terms of accuracy and solution diversity. The algorithm converges with a magnitude of 10-4, compared to 102 in BBO and 10-2 in IWO. In the slope stability problem, the safety factor acquired by the analogy of slope erosion (ASE) is closer to the recommended value.Originality/valueThis paper introduces a periodic selection strategy and constructs a hybrid optimizer, which enhances the global exploration capacity of metaheuristic algorithms.

SummaryThe discovery of malevolent users in cognitive radio networks (CRNs) is done by using blockchain‐based technique. For diagnosing the availability of frequency resources, the spectrum sensing (SS) plays an essential role in CRN. It is critical to enable cognitive radio (CR) technology in wireless communication systems for the next generation. Due to the increasing demand of the users, some bands are overloaded and others become underutilized. The CR applied models for vigorously assigning unlicensed users. The missed detection problem occurs in the conventional spectrum sensing schemes, which hampers the proper utilization of the spectrum. These motivated the development of a newly devised passive reputation method with an optimized deep model for spectrum sensing in CRN‐based blockchain. The sensing spectrum in CRN is designed using the cooperative spectrum sensing algorithm. The spectrum sensing is done with the decision made using AlexNet where the weights of AlexNet are trained with a hybrid optimization algorithm, named Honey Badger Remora Optimization (HBRO) algorithm. Thus, the output attained from the Alexnet_HBRO algorithm is considered. Meanwhile, the data are recorded in the blockchain, and then, the passive reputation is performed, and the determined output is considered. At last, the spectrum sensing is carried out by fusing AlexNet and passive reputation output where weights are optimally generated using the proposed Snake Honey Badger Remora Optimization (SHBRO) algorithm. The proposed SHBRO offered a high detection probability of 1, false alarm probability of 0.003, sensing time of 551.567s, and computation time of 210.102s.

On the efficiency of chaos optimization algorithms for global optimization

A new gradient based particle swarm optimization algorithm for accurate computation of global minimum