An Efficient Hybrid Optimization for Skin Cancer Detection Using PNN Classifier

The necessity of on-time cancer detection is extremely high in the recent days as it becomes a threat to human life.The skin cancer is considered as one of the dangerous diseases among other types of cancer since it causes severe health impacts on human beings and hence it is highly mandatory to detect the skin cancer in the early stage for providing adequate treatment.Therefore,an effective image processing approach is employed in this present study for the accurate detection of skin cancer.Initially,the dermoscopy images of skin lesions are retrieved and processed by eliminating the noises with the assistance of Gaborfilter.Then,the pre-processed dermoscopy image is segmented into multiple regions by implementing cascaded Fuzzy C-Means(FCM)algorithm,which involves in improving the reliability of cancer detection.The A Gabor Response Co-occurrence Matrix(GRCM)is used to extract melanoma parameters in an effi-cient manner.A hybrid Particle Swarm Optimization(PSO)-Whale Optimization is then utilized for efficiently optimizing the extracted features.Finally,the fea-tures are significantly classified with the assistance of Probabilistic Neural Net-work(PNN)classifier for classifying the stages of skin lesion in an optimal manner.The whole work is stimulated in MATLAB and the attained outcomes have proved that the introduced approach delivers optimal results with maximal accuracy of 97.83%.

2D multi-scale hybrid optimization method for geophysical inversion and its application

Local and global optimization methods are widely used in geophysical inversion but each has its own advantages and disadvantages. The combination of the two methods will make it possible to overcome their weaknesses. Based on the simulated annealing genetic algorithm （SAGA） and the simplex algorithm, an efficient and robust 2-D nonlinear method for seismic travel-time inversion is presented in this paper. First we do a global search over a large range by SAGA and then do a rapid local search using the simplex method. A multi-scale tomography method is adopted in order to reduce non-uniqueness. The velocity field is divided into different spatial scales and velocities at the grid nodes are taken as unknown parameters. The model is parameterized by a bi-cubic spline function. The finite-difference method is used to solve the forward problem while the hybrid method combining multi-scale SAGA and simplex algorithms is applied to the inverse problem. The algorithm has been applied to a numerical test and a travel-time perturbation test using an anomalous low-velocity body. For a practical example, it is used in the study of upper crustal velocity structure of the A＇nyemaqen suture zone at the north-east edge of the Qinghai-Tibet Plateau. The model test and practical application both prove that the method is effective and robust.

Hybrid optimization algorithm based on chaos,cloud and particle swarm optimization algorithm

As for the drop of particle diversity and the slow convergent speed of particle in the late evolution period when particle swarm optimization（PSO） is applied to solve high-dimensional multi-modal functions,a hybrid optimization algorithm based on the cat mapping,the cloud model and PSO is proposed.While the PSO algorithm evolves a certain of generations,this algorithm applies the cat mapping to implement global disturbance of the poorer individuals,and employs the cloud model to execute local search of the better individuals;accordingly,the obtained best individuals form a new swarm.For this new swarm,the evolution operation is maintained with the PSO algorithm,using the parameter of pop distr to balance the global and local search capacity of the algorithm,as well as,adopting the parameter of mix gen to control mixing times of the algorithm.The comparative analysis is carried out on the basis of 4 functions and other algorithms.It indicates that this algorithm shows faster convergent speed and better solving precision for solving functions particularly those high-dimensional multi-modal functions.Finally,the suggested values are proposed for parameters pop distr and mix gen applied to different dimension functions via the comparative analysis of parameters.

Parameter selection of support vector regression based on hybrid optimization algorithm and its application

Choosing optimal parameters for support vector regression （SVR） is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters on generalization error, a new approach with two steps is proposed for selecting SVR parameters, First the kernel function and SVM parameters are optimized roughly through genetic algorithm, then the kernel parameter is finely adjusted by local linear search, This approach has been successfully applied to the prediction model of the sulfur content in hot metal. The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods,

Development of hybrid optimization algorithm for structures furnished with seismic damper devices using the particle swarm optimization method and gravitational search algorithm

Previous studies about optimizing earthquake structural energy dissipation systems indicated that most existing techniques employ merely one or a few parameters as design variables in the optimization process,and thereby are only applicable only to simple,single,or multiple degree-of-freedom structures.The current approaches to optimization procedures take a specific damper with its properties and observe the effect of applying time history data to the building;however,there are many different dampers and isolators that can be used.Furthermore,there is a lack of studies regarding the optimum location for various viscous and wall dampers.The main aim of this study is hybridization of the particle swarm optimization(PSO) and gravitational search algorithm(GSA) to optimize the performance of earthquake energy dissipation systems(i.e.,damper devices) simultaneously with optimizing the characteristics of the structure.Four types of structural dampers device are considered in this study:(ⅰ) variable stiffness bracing(VSB) system,(ⅱ) rubber wall damper(RWD),(ⅲ) nonlinear conical spring bracing(NCSB) device,(iv) and multi-action stiffener(MAS) device.Since many parameters may affect the design of seismic resistant structures,this study proposes a hybrid of PSO and GSA to develop a hybrid,multi-objective optimization method to resolve the aforementioned problems.The characteristics of the above-mentioned damper devices as well as the section size for structural beams and columns are considered as variables for development of the PSO-GSA optimization algorithm to minimize structural seismic response in terms of nodal displacement(in three directions) as well as plastic hinge formation in structural members simultaneously with the weight of the structure.After that,the optimization algorithm is implemented to identify the best position of the damper device in the structural frame to have the maximum effect and minimize the seismic structure response.To examine the performance of the proposed PSO-GSA optimization method,it has been applied to a three-story reinforced structure equipped with a seismic damper device.The results revealed that the method successfully optimized the earthquake energy dissipation systems and reduced the effects of earthquakes on structures,which significantly increase the building’s stability and safety during seismic excitation.The analysis results showed a reduction in the seismic response of the structure regarding the formation of plastic hinges in structural members as well as the displacement of each story to approximately 99.63%,60.5%,79.13% and 57.42% for the VSB device,RWD,NCSB device,and MAS device,respectively.This shows that using the PSO-GSA optimization algorithm and optimized damper devices in the structure resulted in no structural damage due to earthquake vibration.

Efficient Approach for Resource Allocation in WPCN Using Hybrid Optimization

The recent aggrandizement of radio frequency(RF)signals in wireless power transmission combined with energy harvesting methods have led to the replacement of traditional battery-powered wireless networks since the blooming RF technology provides energy renewal of wireless devices with the quality of service(QoS).In addition,it does not require any unnecessary alterations on the transmission hardware side.A hybridized global optimization technique uniting Global best and Local best(GL)based particle swarm optimization(PSO)and ant colony optimization(ACO)is proposed in this paper to optimally allocate resources in wireless powered communication networks(WPCN)through coordinated operation of communication groups,in which the wireless energy transfer and information sharing take place concomitantly by the aid of a cooperative relay positioned in between the communicating groups.The designed algorithm assists in minimizing power consumption and maximizes the weighted sum rate at the end-user side.Thus the principal target of the system is coordinated optimization of energy beamforming along with time and energy allocation to reduce the total energy consumed combined with assured information rates of the communication groups.Numerical outputs are presented to manifest the proposed system’s performance to verify the analytical results via simulations.

Hybrid optimization model of product concepts

Deficiencies of applying the simple genetic algorithm to generate concepts were specified. Based on analyzing conceptual design and the morphological matrix of an excavator, the hybrid optimization model of generating its concepts was proposed, viz. an improved adaptive genetic algorithm was applied to explore the excavator concepts in the searching space of conceptual design, and a neural network was used to evaluate the fitness of the population. The optimization of generating concepts was finished through the ＂evolution - evaluation＂ iteration. The results show that by using the hybrid optimization model, not only the fitness evaluation and constraint conditions are well processed, but also the search precision and convergence speed of the optimization process are greatly improved. An example is presented to demonstrate the advantages of the orooosed method and associated algorithms.

Hybrid Algorithm-Driven Smart Logistics Optimization in IoT-Based Cyber-Physical Systems

Effectively managing complex logistics data is essential for development sustainability and growth,especially in optimizing distribution routes.This article addresses the limitations of current logistics path optimization methods,such as inefficiencies and high operational costs.To overcome these drawbacks,we introduce the Hybrid Firefly-Spotted Hyena Optimization(HFSHO)algorithm,a novel approach that combines the rapid exploration and global search abilities of the Firefly Algorithm(FO)with the localized search and region-exploitation skills of the Spotted Hyena Optimization Algorithm(SHO).HFSHO aims to improve logistics path optimization and reduce operational costs.The algorithm’s effectiveness is systematically assessed through rigorous comparative analyses with established algorithms like the Ant Colony Algorithm(ACO),Cuckoo Search Algorithm(CSA)and Jaya Algo-rithm(JA).The evaluation also employs benchmarking methodologies using standardized function sets covering diverse objective functions,including Schwefel’s,Rastrigin,Ackley,Sphere and the ZDT and DTLZ Function suite.HFSHO outperforms these algorithms,achieving a minimum path distance of 546 units,highlighting its prowess in logistics path optimization.This comprehensive evaluation authenticates HFSHO’s exceptional performance across various logistic optimization scenarios.These findings emphasize the critical significance of selecting an appropriate algorithm for logistics path navigation,with HFSHO emerging as an efficient choice.Through the synergistic use of FO and SHO,HFSHO achieves a 15%improvement in convergence,heightened operational efficiency and substantial cost reductions in logistics operations.It presents a promising solution for optimizing logistics paths,offering logistics planners and decision-makers valuable insights and contributing substantively to sustainable sectoral growth.

Optimizing wind farm layout for enhanced electricity extraction using a new hybrid PSO-ANN method

With the growing need for renewable energy,wind farms are playing an important role in generating clean power from wind resources.The best wind turbine architecture in a wind farm has a major influence on the energy extraction efficiency.This paper describes a unique strategy for optimizing wind turbine locations on a wind farm that combines the capabilities of particle swarm optimization(PSO)and artificial neural networks(ANNs).The PSO method was used to explore the solution space and develop preliminary turbine layouts,and the ANN model was used to fine-tune the placements based on the predicted energy generation.The proposed hybrid technique seeks to increase energy output while considering site-specific wind patterns and topographical limits.The efficacy and superiority of the hybrid PSO-ANN methodology are proved through comprehensive simulations and comparisons with existing approaches,giving exciting prospects for developing more efficient and sustainable wind farms.The integration of ANNs and PSO in our methodology is of paramount importance because it leverages the complementary strengths of both techniques.Furthermore,this novel methodology harnesses historical data through ANNs to identify optimal turbine positions that align with the wind speed and direction and enhance energy extraction efficiency.A notable increase in power generation is observed across various scenarios.The percentage increase in the power generation ranged from approximately 7.7%to 11.1%.Owing to its versatility and adaptability to site-specific conditions,the hybrid model offers promising prospects for advancing the field of wind farm layout optimization and contributing to a greener and more sustainable energy future.

Hyperparameter Optimization for Capsule Network Based Modified Hybrid Rice Optimization Algorithm

Hyperparameters play a vital impact in the performance of most machine learning algorithms.It is a challenge for traditional methods to con-figure hyperparameters of the capsule network to obtain high-performance manually.Some swarm intelligence or evolutionary computation algorithms have been effectively employed to seek optimal hyperparameters as a com-binatorial optimization problem.However,these algorithms are prone to get trapped in the local optimal solution as random search strategies are adopted.The inspiration for the hybrid rice optimization(HRO)algorithm is from the breeding technology of three-line hybrid rice in China,which has the advantages of easy implementation,less parameters and fast convergence.In the paper,genetic search is combined with the hybrid rice optimization algorithm(GHRO)and employed to obtain the optimal hyperparameter of the capsule network automatically,that is,a probability search technique and a hybridization strategy belong with the primary HRO.Thirteen benchmark functions are used to evaluate the performance of GHRO.Furthermore,the MNIST,Chest X-Ray(pneumonia),and Chest X-Ray(COVID-19&pneumonia)datasets are also utilized to evaluate the capsule network learnt by GHRO.The experimental results show that GHRO is an effective method for optimizing the hyperparameters of the capsule network,which is able to boost the performance of the capsule network on image classification.

Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network

Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monitoring coverage,this research focuses on the power banks’energy supply coverage.The study of 2-D and 3-D spaces is typical in IWSN,with the realistic environment being more complex with obstacles(i.e.,machines).A 3-D surface is the field of interest(FOI)in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN.The hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN system.The model improves the power supply to a more considerable extent with the least number of power bank deployments.The main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic algorithm.An overall probabilistic coverage rate analysis of every point on the FOI is provided,not limiting the scope to target points or areas.Bresenham’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement.A dynamic search strategy(DSS)is proposed to modify the artificial bee colony(ABC)and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment problems.Further,the cellular automata(CA)is utilized to enhance the convergence speed.The case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization process.Comparative experiments are conducted on four benchmark functions to validate the effectiveness of the proposed method.The experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC(GABC)algorithms.The results show that the proposed energy coverage optimization method based on the hybrid power bank deployment model generates more accurate results than the results obtained by similar algorithms(i.e.,ABC,GABC).The proposed model is,therefore,effective and efficient for optimization in the IWSN.

Adaptive modification of turbofan engine nonlinear model based on LSTM neural networks and hybrid optimization method

An accurate and reliable turbofan engine model which can describe its dynamic behavior within the full flight envelop and lifecycle plays a critical role in performance optimization, controller design and fault diagnosis. However, due to the performance differences caused by the tolerance of engine manufacturing and assembly, and performance degradation during continuously stringent environmental regulations, the model accuracy is severely reduced. In this paper, an adaptive modification method of turbofan engine nonlinear Component-Llevel Model(CLM) based on Long Short-Term Memory(LSTM) Neural Network(NN) and hybrid optimization algorithm is pro-posed. First, a dynamic compensator with a combined LSTM NN architecture is constructed to compensate for the initial error between the experimental data and CLM of a turbofan engine under health condition. Then, a sensitivity analysis approach based on the entropy coefficient and technique for order preference by similarity to an ideal solution integrated evaluation is developed to choose the unmeasurable health parameters to be adjusted. Finally, a parallel hybrid optimization algorithm is developed to complete the adaptive model modification when the performance degrades. The proposed method is verified on a military low-bypass twin-spool turbofan engine, and the experimental results show the effectiveness of the proposed method.

Design of Underwater Robot Lines Based on a Hybrid Automatic Optimization Strategy

In this paper, a hybrid automatic optimization strategy is proposed for the design of underwater robot lines. Isight is introduced as an integration platform. The construction of this platform is based on the user programming and several commercial software including UG6.0, GAMBIT2.4.6 and FLUENT12.0. An intelligent parameter optimization method, the particle swarm optimization, is incorporated into the platform. To verify the strategy proposed, a simulation is conducted on the underwater robot model 5470, which originates from the DTRC SUBOFF project. With the automatic optimization platform, the minimal resistance is taken as the optimization goal;the wet surface area as the constraint condition; the length of the fore-body, maximum body radius and after-body＇s minimum radius as the design variables. With the CFD calculation, the RANS equations and the standard turbulence model are used for direct numerical simulation. By analyses of the simulation results, it is concluded that the platform is of high efficiency and feasibility. Through the platform, a variety of schemes for the design of the lines are generated and the optimal solution is achieved. The combination of the intelligent optimization algorithm and the numerical simulation ensures a global optimal solution and improves the efficiency of the searching solutions.

Model parameters estimation of aero-engine based on hybrid optimization algorithm

A hybrid optimization algorithm for the time-domain identification of multivariable,state space model for aero-engine was presented in this paper.The optimization procedure runs particle swarm optimization(PSO) and least squares optimization(LSO) "in series".PSO starts from an initial population and searches for the optimum solution by updating generations.However,it can sometimes run into a suboptimal solution.Then LSO can start from the suboptimal solution of PSO,and get an optimum solution by conjugate gradient algorithm.The algorithm is suitable for the high-order multivariable system which has many parameters to be estimated in wide ranges.Hybrid optimization algorithm is applied to estimate the parameters of a 4-input 4-output state variable model(SVM) for aero-engine.The simulation results demonstrate the effectiveness of the proposed algorithm.

Landing route planning method for micro drones based on hybrid optimization algorithm

Aiming at the obstacle avoidance trajectory planning problem in the landing process of the micro drone,this paper proposes a swarm optimization algorithm that combines the dragonfly optimization method and the differential evolution method.The orthogonal learning mechanism is adopted to realize the adaptive switch between the two algorithms.In the process of landing route planning,the planning plane is first obtained by making the gliding plane tangent to the obstacle.In the planning plane,the projection of obstacle is transformed into multiple unreachable line segments.By designing an optimization model,the 3D landing route planning problem is transformed into a 2D obstacle avoidance route optimization problem.Taking the shortest route as the optimization objective,the penalty factor is introduced into the cost function to avoid the intersection of the landing route and obstacle.During the optimization process,through the orthogonal learning of the intermediate iterative results,the hybrid algorithm can adaptively select the next iterative algorithm,so it can give full play to the respective advantages of the two algorithms.The optimization results show that,compared with the single optimization algorithm,the hybrid optimization algorithm proposed in this paper can better solve the problem of landing route planning for micro-small UAVs.

Smoothness preserving layout for dynamic labels by hybrid optimization

Stable label movement and smooth label trajectory are critical for effective information understanding.Sudden label changes cannot be avoided by whatever forced directed methods due to the unreliability of resultant force or global optimization methods due to the complex trade-off on the different aspects.To solve this problem,we proposed a hybrid optimization method by taking advantages of the merits of both approaches.We first detect the spatial-temporal intersection regions from whole trajectories of the features,and initialize the layout by optimization in decreasing order by the number of the involved features.The label movements between the spatial-temporal intersection regions are determined by force directed methods.To cope with some features with high speed relative to neighbors,we introduced a force from future,called temporal force,so that the labels of related features can elude ahead of time and retain smooth movements.We also proposed a strategy by optimizing the label layout to predict the trajectories of features so that such global optimization method can be applied to streaming data.

Hybrid anti-prematuration optimization algorithm

Heuristic optimization methods provide a robust and efficient approach to solving complex optimization problems.This paper presents a hybrid optimization technique combining two heuristic optimization methods,artificial immune system（AIS） and particle swarm optimization（PSO）,together in searching for the global optima of nonlinear functions.The proposed algorithm,namely hybrid anti-prematuration optimization method,contains four significant operators,i.e.swarm operator,cloning operator,suppression operator,and receptor editing operator.The swarm operator is inspired by the particle swarm intelligence,and the clone operator,suppression operator,and receptor editing operator are gleaned by the artificial immune system.The simulation results of three representative nonlinear test functions demonstrate the superiority of the hybrid optimization algorithm over the conventional methods with regard to both the solution quality and convergence rate.It is also employed to cope with a real-world optimization problem.

A Hybrid TCNN Optimization Approach for the Capacity Vehicle Routing Problem

A novel approximation algorithm was proposed for the problem of finding the minimum total cost of all routes in Capacity Vehicle Routing Problem (CVRP). CVRP can be partitioned into three parts: the selection of vehicles among the available vehicles, the initial routing of the selected fleet and the routing optimization. Fuzzy C-means (FCM) can group the customers with close Euclidean distance into the same vehicle according to the principle of similar feature partition. Transiently chaotic neural network (TCNN) combines local search and global search, possessing high search efficiency. It will solve the routes to near optimality. A simple tabu search (TS) procedure can improve the routes to more optimality. The computations on benchmark problems and comparisons with other results in literatures show that the proposed algorithm is a viable and effective approach for CVRP.

Hybrid Sooty Tern Optimization and Differential Evolution for Feature Selection

In this paper,a hybrid model based on sooty tern optimization algo-rithm(STOA)is proposed to optimize the parameters of the support vector machine(SVM)and identify the best feature sets simultaneously.Feature selec-tion is an essential process of data preprocessing,and it aims to find the most rele-vant subset of features.In recent years,it has been applied in many practical domains of intelligent systems.The application of SVM in many fields has proved its effectiveness in classification tasks of various types.Its performance is mainly determined by the kernel type and its parameters.One of the most challenging process in machine learning is feature selection,intending to select effective and representative features.The main disadvantages of feature selection processes included in classical optimization algorithm are local optimal stagnation and slow convergence.Therefore,the hybrid model proposed in this paper merges the STOA and differential evolution(DE)to improve the search efficiency and con-vergence rate.A series of experiments are conducted on 12 datasets from the UCI repository to comprehensively and objectively evaluate the performance of the proposed method.The superiority of the proposed method is illustrated from dif-ferent aspects,such as the classification accuracy,convergence performance,reduced feature dimensionality,standard deviation(STD),and computation time.

Design of hybrid damping controller based on multi-target gravitational search optimization algorithm in a multi-machine power system with high penetration of PV park

A mathematical approach was proposed to investigate the impact of high penetration of large-scale photovoltaic park(LPP) on small-signal stability of a power network and design of hybrid controller for these units.A systematic procedure was performed to obtain the complete model of a multi-machine power network including LPP.For damping of oscillations focusing on inter-area oscillatory modes,a hybrid controller for LPP was proposed.The performance of the suggested controller was tested using a 16-machine 5-area network.The results indicate that the proposed hybrid controller for LPP provides sufficient damping to the low-frequency modes of power system for a wide range of operating conditions.The method presented in this work effectively indentifies the impact of increased PV penetration and its controller on dynamic performance of multi-machine power network containing LPP.Simulation results demonstrate that the model presented can be used in designing of essential controllers for LPP.