Local Optimal Value Research Articles

A hybrid-strategy-improved butterfly optimization algorithm applied to the node coverage problem of wireless sensor networks.

To increase the node coverage of wireless sensor networks (WSN) more effectively, in this paper, we propose a hybrid-strategy-improved butterfly optimization algorithm (H-BOA). First, we introduce Kent chaotic map to initialize the population to ensure a more uniform search space. Second, a new inertial weight modified from the Sigmoid function is introduced to balance the global and local search capacities. Third, we comprehensively use elite-fusion and elite-oriented local mutation strategies to raise the population diversity. Then, we introduce a perturbation based on the standard normal distribution to reduce the possibility of the algorithm falling into premature. Finally, the simulated annealing process is introduced to evaluate the solution's quality and improve the algorithm's ability, which is helpful to jump out of the local optimal value. Through numerous experiments of the international benchmark functions, the results show the performance of H-BOA has been significantly raised. We apply it to the WSN nodes coverage problem. The results show that H-BOA improves the WSN maximum coverage and it is far more than other optimization algorithms.

Novel process parameters based approach for reducing residual stresses in WAAM

Residual stresses are unfavourable phenomena in wire arc additive manufacturing, which needs to be critically analysed and mitigated. Process parameters play a crucial role in mitigating stress, the work here attempts to analyse strategies to find the best trend of varying the process parameters rather than selecting the optimal process parameters in order to have the least residual stresses. The initial part of work deals with optimising the input parameters assuming uniform value of parameters to build the entire structure wherein local optimal values of current, voltage and welding speed are obtained. However, critically analysing for global optimas, the parameters needed further reduction, which would create nodus of arc stabilities. To address this an innovative procedure is applied wherein a variation trend of parameters along the length of the build structure are used and trend analysis of the output is carried out to find the best and worst case. The study's outputs indicate that the difference in the effective stress formed via designed ideology and the one with uniform parameters on an average scale is 210 MPa.

Energy-Efficient Scheduling for a Cognitive IoT-Based Early Warning System

Flash floods are deemed the most fatal and disastrous natural hazards globally due to their prompt onset that requires a short prime time for emergency response. Cognitive Internet of things (CIoT) technologies including inherent characteristics of cognitive radio (CR) are potential candidates to develop a monitoring and early warning system (MEWS) that helps in efficiently utilizing the short response time to save lives during flash floods. However, most CIoT devices are battery-limited and thus, it reduces the lifetime of the MEWS. To tackle these problems, we propose a CIoT-based MEWS to slash the fatalities of flash floods. To extend the lifetime of the MEWS by conserving the limited battery energy of CIoT sensors, we formulate a resource assignment problem for maximizing energy efficiency. To solve the problem, at first, we devise a polynomial-time heuristic energy-efficient scheduler (EES-1). However, its performance can be unsatisfactory since it requires an exhaustive search to find local optimum values without consideration of the overall network energy efficiency. To enhance the energy efficiency of the proposed EES-1 scheme, we additionally formulate an optimization problem based on a maximum weight matching bipartite graph. Then, we additionally propose a Hungarian algorithm-based energy-efficient scheduler (EES-2), solvable in polynomial time. The simulation results show that the proposed EES-2 scheme achieves considerably high energy efficiency in the CIoT-based MEWS, leading to the extended lifetime of the MEWS without loss of throughput performance.

Fractional-Order PID and Active Disturbance Rejection Control with Tuning Parameter Optimization for Induction Heating

Induction heating systems are characterized by model uncertainty, nonlinearity, and external disturbances, and the control accuracy of the system directly affects the performance of the heated workpiece. In order to improve the temperature control accuracy and anti-interference performance of induction heating systems, this paper proposes a composite control strategy combining fractional-order PID (FOPID) and active disturbance rejection control (ADRC). Meanwhile, for the problem of too many controller tuning parameters, an improved quantum behavior particle swarm optimization (QPSO) algorithm is used to transform the nine parameters to be tuned in fractional-order PID active disturbance rejection control (FOPID-ADRC) into a minimization value optimization problem for solving. The simulation results show that the FOPID-ADRC controller improves the anti-interference capability and control accuracy of the temperature control system, and the improved QPSO algorithm has better global search capability and local optimal adaptation value.

An efficient multi-thresholding based COVID-19 CT images segmentation approach using an improved equilibrium optimizer

Optimization is the process of searching for the optimal (best-so-far) solution among a wide range of solutions. Besides, in the last two decades, a family of algorithms known as metaheuristic algorithms (MHs) has been widely used. MHs have attracted researchers’ interest due to their efficiency, easy implementation, and understanding. The equilibrium optimizer (EO) is a recent MH that has been used to tackle several real world problems. Despite the robustness of the EO algorithm, it suffers of the unbalance between the exploration and exploitation phases, this situation causes that the search process be trapped in local optimal values. In this study, an improved version of the EO that combines the standard operators with the dimension learning hunting (DLH) is introduced. The proposed method called I-EO is tested over the CEC’2020 benchmark functions. Quantitative and qualitative results confirmed the robustness and superiority of the proposed algorithm compared to a set of well-known optimization methods. Besides, I-EO is proposed to tackle a real-world application; the multi-level thresholding segmentation for a set of CT images of COVID-19 by maximizing the fuzzy entropy. The segmentation results show the excellent performance in all experiments and confirmed that the proposed I-EO could be an efficient tool for image segmentation. The different elements of the CT are properly segmented by the I-EO based approach. Moreover, the statistical analysis, quality metrics, comparisons and non-parametric tests validates the performance of the I-EO to segment CT images of COVID-19.

Pilot allocation optimization using enhanced salp swarm algorithm for sparse channel estimation

Pilot pattern has a significant effect on the performance of channel estimation based on compressed sensing. However, because of the influence of the number of subcarriers and pilots, the complexity of the enumeration method is computationally impractical. The meta-heuristic algorithm of the salp swarm algorithm (SSA) is employed to address this issue. Like most meta-heuristic algorithms, the SSA algorithm is prone to problems such as local optimal values and slow convergence. In this paper, we proposed the CWSSA to enhance the optimization efficiency and robustness by chaotic opposition-based learning strategy, adaptive weight factor, and increasing local search. Experiments show that the test results of the CWSSA on most benchmark functions are better than those of other meta-heuristic algorithms. Besides, the CWSSA algorithm is applied to pilot pattern optimization, and its results are better than other methods in terms of BER and MSE.

Accurate and stable two-step LED position calibration method for Fourier ptychographic microscopy.

.Significance: Fourier ptychography microscopy (FPM) is a computational optical imaging technology that employs angularly varying illuminations and a phase retrieval algorithm to achieve a wide field of view and high-resolution imaging simultaneously. In the FPM, LED position error will reduce the quality of the reconstructed high-resolution image. To correct the LED positions, current methods consider each of the LED positions as independent and use an optimization algorithm to get each of the positions. When the positional misalignment is large or the search position falls into a local optimal value, the current methods may lack stability and accuracy.Aim: We improve the model of the LED position and propose an accurate and stable two-step correction scheme (tcFPM) to calibrate the LED position error.Approach: The improved LED positions model combines the overall offset, which represents the relative deviation of the LED array and the optical axis, with the slight deviation of each LED’s independent position. In the tcFPM, the overall offset of the LED array is corrected at first, which obtains an approximate value of the overall offset of the LED array. Then the position of each LED is precisely adjusted, which obtains the slight offset of each LED.Results: This LED position error model is more in line with the actual situation. The simulation and experimental results show that the method has high accuracy in correcting the LED position. Furthermore, the reconstruction process of tcFPM is more stable and significantly improves the quality of the reconstruction results, which is compared with some LED position error correction methods.Conclusions: An LED position error correction technology is proposed, which has a stable iterative process and improves the reconstruction accuracy of complex amplitude.

An improved firefly algorithm with dynamic self-adaptive adjustment.

The firefly algorithm (FA) is proposed as a heuristic algorithm, inspired by natural phenomena. The FA has attracted a lot of attention due to its effectiveness in dealing with various global optimization problems. However, it could easily fall into a local optimal value or suffer from low accuracy when solving high-dimensional optimization problems. To improve the performance of the FA, this paper adds the self-adaptive logarithmic inertia weight to the updating formula of the FA, and proposes the introduction of a minimum attractiveness of a firefly, which greatly improves the convergence speed and balances the global exploration and local exploitation capabilities of FA. Additionally, a step-size decreasing factor is introduced to dynamically adjust the random step-size term. When the dimension of a search is high, the random step-size becomes very small. This strategy enables the FA to explore solution more accurately. This improved FA (LWFA) was evaluated with ten benchmark test functions under different dimensions (D = 10, 30, and 100) and with standard IEEE CEC 2010 benchmark functions. Simulation results show that the performance of improved FA is superior comparing to the standard FA and other algorithms, i.e., particle swarm optimization, the cuckoo search algorithm, the flower pollination algorithm, the sine cosine algorithm, and other modified FA. The LWFA also has high performance and optimal efficiency for a number of optimization problems.

Accurate residual capacity estimation of retired LiFePO4 batteries based on mechanism and data-driven model

Accurate residual capacity estimation of retired LiFePO4 batteries is critically important for second-use applications but is challenging with multiple aging pathways and nonlinear degradation mechanisms. In this study, a fast and accurate residual capacity estimation method based on the mechanism and data-driven model is developed with two main contributions. First, as the basis of the residual capacity estimation model, three new health indicators directly related to the capacity loss mechanism are derived from the prognostic and mechanism model using the Levenberg-Marquardt method and Spearman correlation. Second, residual capacity tests were conducted on 1000 retired batteries to establish a data-driven model for residual capacity estimation based on the proposed health indicators, guaranteeing better universality and estimation accuracy for different types of retired LiFePO4 batteries. To establish a data-driven model for the residual capacity estimation, an improved moth–flame optimization and support vector regression method is used; the adaptive weight and Levy flight are introduced in the moth–flame optimization algorithm to prevent the local optimal value. The residual capacity estimation results are compared with the results from three other typical methods and input health indicators. The results show that the root mean square error of the proposed method is within 2.18% using only the first 10% of the data, a smaller error than with the other methods. A fast and accurate residual capacity estimation method for retired batteries can reduce the cost and improve the development for second-use applications.

Adaptive Cuckoo Search Algorithm Based on Dynamic Adjustment Mechanism

Cuckoo Search (CS) algorithm, a simple and effective global optimization algorithm, has been widely used to deal with practical optimization problems. So as to improvethe standard cuckoo search algorithm, such as slow convergence and easy convergence to local optimal value, an Adaptive Cuckoo Search algorithm on the basis of Dynamic Adjustment Mechanism (ACSDAM) has been proposed. Based on exponential function and logarithmic function, the dynamic adjustment is made for updating step size and discovering probability. During the optimization process, updating step size and discovering probability of each nest are adjusted according to the number of iterations of each nest, so as to equilibrate the global detection and local capacity of the algorithm. Then 23 standard test functions will be selected for a simulation experiment, and compared with other CS variant algorithms, ACS-DAM effectively improved the rate of convergence and the algorithmic precision. ACS-DAM algorithm was employed to optimize the Support Vector Machine (SVM). The experiment proves that the convergence rate with ACSDAM is better than that with CS obviously and ACS-DAM has stronger optimization ability and higher efficiency than CS.

A reliable approach for heat exchanger networks synthesis with stream splitting by coupling genetic algorithm with modified quasi-linear programming method

In this paper, an efficient as well as reliable approach to deal with heat exchanger networks (HENs) synthesis problems, which is inherently known as a mixed-integer non-linear programming model, is presented. The structural variables as the discrete variables are optimized by a genetic algorithm (GA), whereas continuous variables are handled by a modified quasi-linear programming (MQLP) model. Each HEN is considered as a chromosome consisting of a sequence of genes. Each gene also contains the address of the exchanger(s) in the network. The HENs generated by the GA are sent to the MQLP to calculate their overall objective function (OOF) (i.e. minimum total annual cost (TAC)). The MQLP model includes two inner and outer surfaces. On the outer surface, the local optimal values of the continuous variables are found according to the maximum energy recovery of HEN, while on the inner surface, the globally optimal values of them are found to reach the minimum TAC of HEN. Due to the relatively linear behavior of the proposed method, a comparison of results with references showed that this method can reduce TAC of HENs compared to the studied references by about (0.51% to 2.37%).

An Enhancing Particle Swarm Optimization Algorithm (EHVPSO) for damage identification in 3D transmission tower

In this paper, a novel enhanced Particle Swarm Optimization (PSO) algorithm is introduced for solving damage identification problems. For the first time, the algorithm is applied to a complex structure, namely an Electric Power Transmission with 44.05 m height. The process of structural damage assessment is implemented using SAP2000 commercial software combined with MATLAB. Using the Open Application Programming Interface (OAPI) source code, which is available in SAP2000, a strong MATLAB environment program has been developed in this research. This program can allow the user to adjust the initial model's parameters in SAP2000 to create a continuous two ways data exchange between SAP2000 and MATLAB. Thus, the process of detecting the location and level of damage in the structure is performed by applying a new version of PSO, namely Enhancing Particle Swarm Optimization Algorithm (EHVPSO), using stochastic parameters. The key factor in the EHVPSO algorithm is to introduce two novel equations. The first equation is used to control the convergence rate in each movement of particleith, and the second equation is used to control the balance between local optimal value and global optimal value. The results demonstrate that the proposed algorithm can detect damage with very high accuracy and reliability.

IFFO: An Improved Fruit Fly Optimization Algorithm for Multiple Workflow Scheduling Minimizing Cost and Makespan in Cloud Computing Environments

Cloud computing platforms have been extensively using scientific workflows to execute large-scale applications. However, multiobjective workflow scheduling with scientific standards to optimize QoS parameters is a challenging task. Various metaheuristic scheduling techniques have been proposed to satisfy the QoS parameters like makespan, cost, and resource utilization. Still, traditional metaheuristic approaches are incompetent to maintain agreeable equilibrium between exploration and exploitation of the search space because of their limitations like getting trapped in local optimum value at later evolution stages and higher-dimensional nonlinear optimization problem. This paper proposes an improved Fruit Fly Optimization (IFFO) algorithm to minimize makespan and cost for scheduling multiple workflows in the cloud computing environment. The proposed algorithm is evaluated using CloudSim for scheduling multiple workflows. The comparative results depict that the proposed algorithm IFFO outperforms FFO, PSO, and GA.

Heat Transfer by Natural Convection in a Square Enclosure Containing PCM Suspensions

Research on using phase change material (PCM) suspension to improve the heat transfer and energy storage capabilities of thermal systems is booming; however, there are limited studies on the application of PCM suspension in transient natural convection. In this paper, the implicit finite difference method was used to numerically investigate the transient and steady-state natural convection heat transfer in a square enclosure containing a PCM suspension. The following parameters were included in the simulation: aspect ratio of the physical model = 1, ratio of the buoyancies caused by temperature and concentration gradients = 1, Raleigh number (RaT) = 103–105, Stefan number (Ste) = 0.005–0.1, subcooling factor (Sb) = 0–1.0, and initial mass fraction (or concentration) of PCM particles (ci) = 0–0.1. The results showed that the use of a PCM suspension can effectively enhance heat transfer by natural convection. For example, when RaT = 103, Ste = 0.01, ci = 0.1, and Sb = 1, the steady-state natural convection heat transfer rate inside the square enclosure can be improved by 70% compared with that of pure water. With increasing Sb, the Nusselt number can change nonlinearly, resulting in a local optimal value.

A new selection operator for differential evolution algorithm

Most research on improving differential evolution algorithms has focused on mutation operator and parameter control. In this paper, a new selection operator is proposed to improve differential evolution algorithm performance. When the individual is not in a state of stagnation, the proposed selection operator is the same as the classical selection operator, meaning that it chooses the best vector from the trial vector and parent vector to survive to the next generation. When the individual is in a state of stagnation, the three other candidate vectors may survive to the next generation. The first candidate vector is the best vector of all the discarded trial vectors of the parent vector. The second candidate vector is the second-best vector of all the discarded trial vectors of the parent vector. The third candidate vector is randomly chosen from all the successfully updated solutions. The proposed selection operator will improve the differential evolution algorithm’s ability to escape the local optimal value. 58 benchmark functions are used for verification of the proposed selection operator’s performance. Experiments were conducted in order to compare six differential evolution algorithms’ performances using the proposed selection operator and not using the proposed selection operator. Simulation results showed that the proposed selection operator significantly improved the differential evolution algorithm’s performance.

GPS Position Prediction Method Based on Chaotic Map-Based Flower Pollination Algorithm

GPS position data prediction can effectively alleviate urban traffic, population flow, route planning, etc. It has very important research significance. Using swarm intelligence optimization algorithm to predict geographic location has important research strategies. Flower pollination algorithm (FPA) is a new swarm intelligence optimization algorithm (SIOA) and easy to implement and has other characteristics; more and more scholars have continuously improved it and applied it to more fields. Aiming at the fact that FPA leads to the local optimal value in cross-pollination, the chaotic mapping strategy is proposed to optimize related issues that the population is not rich enough in the self-pollination process. The improved flower pollination algorithm has better advantages in testing function convergence and geographic location prediction effect.

Modelling and optimisation of hardness in citrate stabilised electroless nickel boron (ENi-B) coatings using back propagation neural network – Box Behnken design and simulated annealing – genetic algorithm

ABSTRACT In this work, a novel citrate stabilised electroless bath was developed and the process parameters (concentrations of nickel, reducing agent, and stabiliser) were optimised to achieve the maximum hardness in the ENi-B deposit on 7075-T6 aluminium alloy, using the back-propagation neural network (BPNN), Box–Behnken design (BBD), simulated annealing (SA) and genetic algorithm (GA). The effect of independent variables on dependent variable was modelled using the BPNN and BBD. The models were assessed for their significance using the coefficient of determination (R2) and mean squared error (MSE). The MSE and R2 of 34.18 and 0.9852 were obtained for BPNN model against 20.48 and 0.9911 for BBD, which proved that the BBD fits well to the experimental data. The optimum nickel ion, reducing agent and stabiliser concentrations of 29.86, 0.77 and 30.92 g L−1 were obtained from BBD for the maximum hardness of 592 HV. The local optimum values obtained from BBD were compared with global optimisation techniques, SA and GA, and the values were validated through experiments carried out in triplicate. The maximum hardness from local and global optimisation techniques was identical, with negligible change in the values of optimised process parameters. X-ray diffraction and scanning electron microscopy methods were used to examine the elemental composition and surface morphology, respectively, before and after heat treatment.

Research on transformer fault diagnosis based on genetic algorithm optimized neural network

Aiming at the problem of transformer fault diagnosis in power system, a fault diagnosis method based on genetic algorithm optimization BP neural network is proposed. The BP neural network structure was established based on the traditional three-ratio fault diagnosis method. The concentration of dissolved gas in transformer oil was taken as the input value of the neural network, and the fault working state of transformer was taken as the output value. The established neural network is used to diagnose transformer faults. In order to avoid BP neural network falling into the problem of local optimal value, the weights and thresholds of the neural network are optimized by genetic algorithm. By using the genetic algorithm toolbox in Matlab to establish the genetic algorithm network structure, the transformer fault data as input for the performance of the neural network test. The results show that the neural network optimized by genetic algorithm has a high classification effect on transformer fault types, and the neural network optimized by genetic algorithm has a higher diagnostic efficiency and accuracy than BP neural network.

Insights into instability of friction-induced vibration of multi-degree-of-freedom models

This study addresses a fundamental problem of linear friction-induced vibration (FiV), the intriguing role of damping on the instability of FiV in the form of mode-coupling. Two well-known classic 2-degree-of-freedom (DoF) slider-belt models are revisited, and a 4-DoF slider-belt model considering different damping configurations in the slider and the belt is investigated. Since proportional damping is very rare in reality, non-proportional damping is given much attention in this paper.Firstly, the analytical solutions of bifurcation boundary of the critical coefficients of friction of the classic 2-DoF models are derived, which show the subtle role of damping clearly and theoretically explain and clear misunderstandings and confusions on the phenomenon about the drop of the stability boundary from that of the undamped system caused by adding non-proportional damping. Secondly, a further increase of non-proportional damping is found to improve the stability and even bring higher degree of stability than increasing proportional damping (which is a new finding). Thirdly, the influences of the different damping distributions among the system components on the stability of the 4-DoF slider-belt model are explored. The evolution of the bifurcation boundary indicates the importance of the ratio of the horizontal to the vertical damping in the slider and in the belt. For some non-proportional damping values, the bifurcation boundary fluctuates, which could exhibit local optimal values of the ratio of damping in different directions of a system component and can be exploited in design against FiV. These findings are valid for general linear second-order dynamic systems with an asymmetric stiffness matrix.

Scheduling Model of Power System Based on Forecasting Error of Wind Power Plant Output

Aiming at the problem of unnecessary waste of power resources in the power system caused by the uncertainty of wind farm output, and then affecting the economics of the system. This paper proposes a power system scheduling model based on wind power output forecasting errors. In the forecasting stage, in view of the defect that the Fruit Fly Optimization Algorithm (FOA) is easy to fall into the local optimal value, an Adaptive Mutation Fruit Fly Optimization Algorithm (AMFOA) based on the flavor concentration variance is proposed. And then, the parameters of Extreme Learning Machine (ELM) are optimized by AMFOA to predict the wind power output. In the scheduling phase, a limit scenario is established based on the obtained prediction error to reduce the fluctuation of the wind farm output. When modeling, the influence of the prediction error is considered in the power balance equation, and it is constructed to maximize the probability of its establishment. And then, incorporate it into the objective function. Based on this, a scheduling model is established and solved by AMFOA. Finally, an example is used to verify the accuracy of the proposed forecasting model and the economics of the scheduling model. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.