Success-History Based Adaptive Differential Evolution Research Articles

Optimized the locations and sizes of FACTS devices on electrical network involving wind power using a new hybrid stochastic algorithm

This article focuses on utilizing a new hybrid optimization algorithm called the Fitness-Distance Balance-based Archimedes Optimization Algorithm (FDB-AOA) to solve the Optimal Power Flow (OPF) problems within a recently adopted electrical transmission grid, specifically the modified IEEE-30 bus system. This system integrates thermal and wind -based generating units, including various types of Flexible AC Transmission System (FACTS) devices. Several tests are performed where the stochastic wind energy is modeled using probability density functions. The optimization goal takes into account the cost of thermal generation, the direct cost of scheduled wind power, and the penalty cost for underestimating wind power. The locations and sizing of FACTS devices are optimized with aim of reducing several fitness functions. The optimization results achieved by the proposed method in solving single objective functions were more effective in finding the optimal solution compared to several well-known algorithms. The results show the superiority of the proposed method in the majority of case studies, as it achieved a better optimum solution with a total generation cost ( Cgen ) value of 806.9817 $/h, and a real power loss ( Ploss ) value 1.7619 MW, also yields a competitive gross cost ( Cgross ) value of 1104.6652 $/h compared to those obtained by the other algorithms. In contrast, the statistical analysis proven the superiority of this algorithm where the standard deviations (SD) required in solving the single objective problem ( Cgen ) is 0.0996, which are better compared to other techniques. the simulation results demonstrate that the FDB-AOA optimizer is robust than other approaches, like the Success history based-adaptive differential evolution (SHADE) algorithm, MSA (Moth Swarm Algorithm), and ABC (artificial bee colony) integrated with the SF (superiority of feasible solutions) approach, in solving OPF problems involving the integration of both thermal and wind power plants along with FACTS devices.

Large-Scale Exact Epidemiological Modelling with Multi-Dimensional Constrained Optimization Approaches.

Multi-dimensional prediction models of the pandemic diseases should be constructed in a way to reflect their peculiar epidemiological characters. In this paper, a graph theory-based constrained multi-dimensional (CM) mathematical and meta-heuristic algorithms (MA) are formed to learn the unknown parameters of a large-scale epidemiological model. The specified parameter signs and the coupling parameters of the sub-models constitute the constraints of the optimization problem. In addition, magnitude constraints on the unknown parameters are imposed to proportionally weight the input-output data importance. To learn these parameters, a gradient-based CM recursive least square (CM-RLS) algorithm, and three search-based MAs; namely, the CM particle swarm optimization (CM-PSO), the CM success history-based adaptive differential evolution (CM-SHADE), and the CM-SHADEWO enriched with the whale optimization (WO) algorithms are constructed. The traditional SHADE algorithm was the winner of the 2018 IEEE congress on evolutionary computation (CEC) and its versions in this paper are modified to create more certain parameter search spaces. The results obtained under the equal conditions show that the mathematical optimization algorithm CM-RLS outperforms the MA algorithms, which is expected since it uses the available gradient information. However, the search-based CM-SHADEWO algorithm is able to capture the dominant character of the CM optimization solution and produce satisfactory estimates in the presence of the hard constraints, uncertainties and lack of gradient information.

3-D gravity inversion for the basement relief reconstruction through modified success-history-based adaptive differential evolution

SUMMARY A gravity inversion procedure using the success-history-based adaptive differential evolution (SHADE) algorithm is presented to reconstruct the 3-D basement relief geometry in sedimentary basins. We introduced exponential population size (number) reduction (EPSR) to reduce the computational cost and used self-adaptive control parameters to solve this highly nonlinear inverse problem. Model parametrization was carried out by discretizing the sedimentary cover via juxtaposed right prisms, each placed below each observation point. Resolvability characteristics of the 3-D inverse problem were revealed through some cost function topography landscapes. The fine-tuned control parameter namely, population number allowed us to get best benefit from the algorithm. Additionally, a stabilizing function as a relative constraint was used to avoid undesired effects originated from the ill-posedness of the problem. In the synthetic data cases, the strategy we propose outperformed the linear population number reduction strategy which has won various IEEE–CEC competitions so far. Thorough uncertainty assessments via probability density function and principal component analysis demonstrated the solidity of the obtained inverse models. In the real data case, residual gravity anomalies of two well-known major grabens of Aegean Graben System (Türkiye), calculated thanks to the finite element method, were inverted. It was determined that the inverse solutions obtained for these basement reliefs, whose depths are still controversial, are statistically reliable. Moreover, these depths were found to be less than the depths reported in most previous studies. We conclude that the SHADE using EPSR strategy that we propose is a powerful alternative inversion tool for highly nonlinear geophysical problems.

Development of optimal placement and sizing of FACTS devices in power system integrated with wind power using modified krill herd algorithm

PurposeThis study aims to intend and implement the optimal power flow, where tuning the production cost is done with the inclusion of stochastic wind power and different kinds of flexible AC transmission systems (FACTS) devices. Here, the speed with fitness-based krill herd algorithm (SF-KHA) is adopted for deciding the FACTS devices’ optimal sizing and placement integrated with wind power. Here, the modified SF-KHA optimizes the sizing and location of FACTS devices for attaining the minimum average production cost and real power depletions of the system. Especially, the objective includes reserve cost for overestimation, cost of thermal generation of the wind power, direct cost of scheduled wind power and penalty cost for underestimation. The efficiency of the offered method over several popular optimization algorithms has been done, and the comparison over different algorithms establishes proposed KHA algorithm attains the accurate optimal efficiency for all other algorithms.Design/methodology/approachThe proposed FACTS devices-based power system with the integration of wind generators is based on the accurate placement and sizing of FACTS devices for decreasing the actual power loss and total production cost of the power system.FindingsThrough the cost function evaluation of the offered SF-KHA, it was noted that the proposed SF-KHA-based power system had secured 13.04% superior to success history-based adaptive differential evolution, 9.09% enhanced than differential evolution, 11.5% better than artificial bee colony algorithm, 15.2% superior to particle swarm optimization and 9.09% improved than flower pollination algorithm.Originality/valueThe proposed power system with the accurate placement and sizing of FACTS devices and wind generator using the suggested SF-KHA was effective when compared with the conventional algorithm-based power systems.

An Improved Optimally Designed Fuzzy Logic-Based MPPT Method for Maximizing Energy Extraction of PEMFC in Green Buildings

Recently, the concept of green building has become popular, and various renewable energy systems have been integrated into green buildings. In particular, the application range of fuel cells (FCs) has become widespread due to the various government plans regarding green hydrogen energy systems. In particular, proton exchange membrane fuel cells (PEMFCs) have proven superiority over other existing FCs. However, the uniqueness of the operating maximum power point (MPP) of PEMFCs represents a critical issue for the PEMFC control systems. The perturb and observe, incremental conductance/resistance, and fuzzy logic control (FLC) represent the most used MPP tracking (MPPT) algorithms for PEMFC systems, among which the FLC-based MPPT methods have shown improved performance compared to the other methods. Therefore, this paper presents a modified FLC-based MPPT method for PEMFC systems in green building applications. The proposed method employs the rate of change of the power with current (dP/dI) instead of the previously used rate of change of power with voltage (dP/dV) in the literature. The employment of dP/dI in the proposed method enables the fast-tracking of the operating MPP with low transient oscillations and mitigated steady-state fluctuations. Additionally, the design process of the proposed controller is optimized using the enhanced version of the success-history-based adaptive differential evolution (SHADE) algorithm with linear population size reduction, known as the LSHADE algorithm. The design optimization of the proposed method is advantageous for increasing the adaptiveness, robustness, and tracking of the MPP in all the operating scenarios. Moreover, the proposed MPPT controller can be generalized to other renewable energy and/or FCs applications. The proposed method is implemented using C-code with the PEMFC model and tested in various operating cases. The obtained results show the superiority and effectiveness of the proposed controller compared to the classical proportional-integral (PI) based dP/dI-based MPPT controller and the classical FLC-based MPPT controller. Moreover, the proposed controller achieves reduced output waveforms ripple, fast and accurate MPPT operation, and simple and low-cost implementation.

An Accurate Model for Bifacial Photovoltaic Panels

Recently, there has been increasing concerns over bifacial PV (BPV) modules over the conventional monofacial PV (MPV) modules owing to their potential to add extra electrical energy from their rear-side irradiance. However, adding the rear-side irradiance to the front-side irradiance results in the increased nonlinearity of the BPV modules compared to MPV modules. Such nonlinearity makes the conventional methods unable to accurately extract the BPV module parameters. In this context, the precise determination of the BPV module parameters is a crucial issue for establishing energy yield estimations and for the proper planning of BPV installations as well. This paper proposes a new model for the BPV modules based on the MPV modeling, in which a new parameter is added to the MPV model to adjust the value of the model series resistance in order to provide a generic model for BPV modules in both monofacial and bifacial operating regions. Moreover, a new determination method for optimizing BPV model parameters using the recently developed enhanced version of the success-history-based adaptive differential evolution (SHADE) algorithm with linear population size reduction, known as the LSHADE method, is applied. The determination process of the model parameters is adapted using a two-stage optimization scheme to model the full operating range of BPV modules. The accuracy of the obtained parameters using the proposed model is compared with the conventional single-diode and double-diode models of the BPV. The obtained results using the proposed model of the BPV module show the performance superiority and accuracy of the LSHADE method over the existing methods in the literature. Furthermore, the LSHADE method provides the successful and accurate extraction of the global optimized parameters to model MPV and BPV modules. Therefore, the proposed method can provide an accurate model for the whole operating range of BPV that would be beneficial for further studies of their economic and technical feasibility for wide installation plans.

Prediction of rail-wheel contact parameters for a metro coach using machine learning

Dynamics of metro coach is complex with frequent acceleration, braking, sharp turns and gradients. Due to sharp turns and twists in track, metro coaches are more prone to the derailment, frictional energy loss and wear. Since contact parameters are difficult to obtain experimentally and are time consuming to determine even computationally through multibody dynamics simulations, in this work, artificial neural network (ANN) and recurrent neural network (RNN) models are used to predict rail-wheel contact parameters. The determined parameters include contact forces, derailment ratio, frictional power, contact location and contact area. These models can be used for reducing derailment tendency, wheel wear and energy consumption. To tackle local extreme values, four meta-heuristic techniques namely Improved Real-coded Genetic Algorithm (IRGA), Success History based Adaptive Differential Evolution (SHADE), Bonobo Optimizer (BO) and Grey Wolf Optimizer (GWO) algorithms are used to optimize the ANN and RNN models. During training, inputs and outputs to the machine learning techniques are generated from a multibody dynamics model built in commercial software Simpack. The multibody dynamics model used is validated using instrumented field trials. Mean absolute percentage error (MAPE) is used for evaluating the performance of the machine learning techniques. Results demonstrate that SHADE is the best optimizer among the four meta-heuristic techniques considered. MAPE in tests, for SHADE-ANN and SHADE-RNN are found to be less than 7%. The developed machine learning models, particularly SHADE-RNN, can be used for onboard monitoring and can help the pilots in running the trains safely and efficiently.

Optimal Reactive Power Dispatch by Success History Based Adaptive Differential Evolution Salp Swarm Algorithm

In this study, a novel hybrid algorithm success history-based adaptive differential evolution salp swarm algorithm (SHADE-SSA) is proposed to solve two different cases of IEEE 30 bus reactive power dispatch problems integrated with thermal generators, wind farms and solar photovoltaic plants. Real power loss minimization and voltage deviation minimization are considered as main objectives in the present work. The performance and robustness of the proposed hybrid SHADE-SSA algorithm are compared with the results of five different metaheuristic algorithms for the same test system and consider the same control variables and constraints. The results of the simulation of the proposed algorithm conform to the effective choice for the solution of optimal reactive power dispatch problems of power systems.

Optimal Design of Large-scale Dome Truss Structures with Multiple Frequency Constraints Using Success-history Based Adaptive Differential Evolution Algorithm

The success-history based adaptive differential evolution (SHADE) algorithm is an efficient modified version of the differential evolution (DE) algorithm, and it has been successfully applied to solve some real-world optimization problems. However, to the best of our knowledge, it has been rarely applied in the field of structural optimization. The optimal design of structures with frequency constraints is well known as a highly nonlinear and non-convex optimization problem with many local optima. In this paper, the SHADE algorithm is examined in the context of size optimization of large-scale truss structures with multiple frequency constraints. In SHADE, a historical memory of successful control parameter settings is used to guide the generation of new control parameters. In order to demonstrate the effectiveness and efficiency of SHADE, three truss optimization problems with multiple frequency constraints are presented. The three examples considered in this paper include a 600-bar single-layer dome-shaped truss, a 1180-bar single-layer dome-shaped truss, and a 1410-bar double-layer dome-shaped truss. The results obtained by the SHADE algorithm are presented and compared with the best-known results reported in the literature. Numerical results indicate the effectiveness and superior performance of SHADE over other algorithms in terms of solution accuracy and robustness. It is worth mentioning that in all the three cases considered, the optimal designs obtained by SHADE are the best ones reported in the literature so far. However, SHADE often requires fewer structural analyses than those required by the other algorithms.

Aircraft conceptual design using metaheuristic-based reliability optimisation

The reliability optimisation methodology is developed to solve a conceptual design problem of a fixed-wing Unmanned Aerial Vehicle (UAV). The reliability quantification is based on the most probable point (MPP) concept, leading to a double-loop optimisation problem. The design problem is formulated as an outer-loop multiobjective optimisation for the main design problem and an inner-loop optimisation for estimating a reliability index (β) of a design solution. The goal of the outer-loop optimisation is to minimise the aircraft take-off weight, and simultaneously maximise β. The aerodynamic and stability properties of an aircraft solution are calculated by a vortex lattice method (VLM), while various types of empirical weight methods are obtained. The design problem is set up to have uncertainties in calculating aircraft empty weight and aerodynamic coefficients and derivatives. For the inner loop optimisation, the MPP is used for approximating the reliability index and probability of failure (pf). Multiobjective Meta-heuristic with Iterative Parameter Distribution Estimation (MMIPDE) and Success-History based Adaptive Differential Evolution (SHADE) are used for solving the outer- and inner-loop optimisations respectively. Four parameters setting strategies for running metaheuristics are proposed for use with the proposed metaheuristic-based reliability optimisation. The comparative results reveal that the best dynamic parameter setting from this study can reduce runtime by 22.5% compared to the traditional metaheuristic run while maintaining competitive results.

Using spatial neighborhoods for parameter adaptation: An improved success history based differential evolution

Differential Evolution (DE) has been widely appraised as a simple yet robust population-based, non-convex optimization algorithm primarily designed for continuous optimization. Two important control parameters of DE are the scale factor F, which controls the amplitude of a perturbation step on the current solutions and the crossover rate Cr, which limits the mixing of components of the parent and the mutant individuals during recombination. We propose a very simple, yet effective, nearest spatial neighborhood-based modification to the adaptation process of the aforesaid parameters in the Success-History based adaptive DE (SHADE) algorithm. SHADE uses a historical archive of the successful F and Cr values to update these parameters and stands out as a very competitive DE variant of current interest. Our proposed modifications can be extended to any SHADE-based DE algorithm like L-SHADE (SHADE with linear population size reduction), jSO (L-SHADE with modified mutation) etc. The enhanced performance of the modified SHADE algorithm is showcased on the IEEE CEC (Congress on Evolutionary Computation) 2013, 2014, 2015, and 2017 benchmark suites by comparing against the DE-based winners of the corresponding competitions. Furthermore, the effectiveness of the proposed neighborhood-based parameter adaptation strategy is demonstrated by using the real-life problems from the IEEE CEC 2011 competition on testing evolutionary algorithms on real-world numerical optimization problems.

SHADE–WOA: A metaheuristic algorithm for global optimization

Differential evolution and its variants have already proven their worth in the field of evolutionary optimization techniques. This study further enhances the success history-based adaptive differential evolution (SHADE) by hybridizing it with a modified Whale optimization algorithm (WOA). In the new algorithm, the two algorithms, SHADE and modified WOA, carry out the search process independently and share information like the global best solution and whole population and thus guides both the algorithms to explore and exploit new promising areas in the search space. It also reduces the chance of being trapped in local optima and stagnation. The proposed algorithm (SHADE–WOA) is tested, evaluating CEC 2017 functions using dimensions 30, 50, and 100. The results are compared with modified DE algorithms, namely SaDE, SHADE, LSHADE, LSHADE-SPACMA, and LSHADE-cnEpSin, also with modified WOA algorithms, namely ACWOA, AWOA, IWOA, HIWOA, and MCSWOA. The new algorithm’s efficiency in solving real-world problems is examined by solving two unconstrained and four constrained engineering design problems. The performance is verified statistically using non-parametric statistical tests like Friedman’s test and Wilcoxon’s test. Analysis of numerical results, convergence analysis, diversity analysis, and statistical analysis ensures the enhanced performance of the proposed SHADE–WOA.

Equilibrium Optimizer: Insights, Balance, Diversity for Renewable Energy Resources Based Optimal Power Flow with Multiple Scenarios

ABSTRACT Today, along with renewable energy sources such as wind generation units and solar photovoltaic systems, the power grid consists of traditional generating units. An approach for solving single-objective optimal power flow problems with the combination of renewable energy resources (RER-OPF) solar and wind power with conventional coal-based power stations is recommended in the proposed paper. In the proposed work, functions of lognormal and Weibull probability distribution are used, respectively, to forecast solar and wind outcomes. The objective feature includes the underestimation service charge and the standby charge for overestimating unusual non-conventional power generation. The quantitative and comparative results show that Equilibrium optimizer (EO) outperforms compare to Harris Hawks Optimizer (HHO), Grey Wolf Optimizer (GWO), Ions Motion Optimizer (IMO) and Success-History based Adaptive Differential Evolution (SHADE), which are all well-known optimization algorithms for solving RER-OPF problem. The EO optimizer provides the optimum value of each objective function and has merits in solving IEEE-30 bus-based RER-OPF problem, according to several evaluation criteria such as best value statistical criterion.

Success-history based adaptive differential evolution method for optimizing fuel loading pattern of VVER-1000 reactor

A discrete Success-History based Adaptive Differential Evolution (SHADE) method has been developed and applied for fuel loading pattern (LP) optimization of a VVER-1000 reactor. SHADE uses an adaptive mechanism based on a historical record of successful control parameters, i.e., mutant scale F and crossover ratio CR, to improve the original differential evolution (DE) algorithm. Therefore, instead of three control parameters in the original DE, the SHADE method consists of two parameters of population size NP and memory size H. To apply the SHADE method to the fuel LP optimization, a relative position indexing approach was deployed to convert real variables into integer variables. Application of the discrete SHADE method to the problem of fuel LP optimization has been performed based on an VVER-1000 MOX core. A fitness function was chosen to maximize the keff, while flattening the radial power distribution. The results show that the keff of the optimal LP is greater than that of the reference core by about 1580 pcm. Whereas, the radial power peaking factor (PPF) of the optimal LP is about 2.4% smaller than that of the reference core.

A Modified jSO Algorithm for Solving Constrained Engineering Problems

Proposing new strategies to improve the optimization performance of differential evolution (DE) is an important research study. The jSO algorithm was the announced winner of the Congress on Evolutionary Computation (CEC) 2017 competition on numerical optimization, and is the state-of-the-art algorithm in the SHADE (Success-History based Adaptive Differential Evolution) algorithm series. However, the jSO algorithm converges prematurely in the search space with different dimensions and is prone to falling into local optimum during evolution, as well as the problem of decreasing population diversity. In this paper, a modified jSO algorithm (MjSO) is proposed which is based on cosine similarity with parameter adaptation and a novel opposition-based learning restart mechanism incorporated with symmetry to address the above problems, respectively. Moreover, it is well known that parameter setting has a significant impact on the performance of the algorithm and the search process can be divided into two symmetrical parts. Hence, a parameter control strategy based on a symmetric search process is introduced in the MjSO. The effectiveness of these designs is supported by presenting a population clustering analysis, along with a population diversity measure to evaluate the performance of the proposed algorithm, three state-of-the-art DE variant algorithms (EBLSHADE, ELSHADE-SPACMA, and SALSHADE-cnEPSin) and two original algorithms (jSO and LSHADE) are compared with it, for solving 30 CEC’17 benchmark functions and three classical engineering design problems. The experimental results and analysis reveal that the proposed algorithm can outperform other competitions in terms of the convergence speed and the quality of solutions. Promisingly, the proposed method can be treated as an effective and efficient auxiliary tool for more complex optimization models and scenarios.

Optimal placement and sizing of FACTS devices for optimal power flow in a wind power integrated electrical network

Optimal power flow (OPF) is one of the challenging optimization problems in power domain. The complexity of the problem escalates with incorporation of uncertain and intermittent renewable sources into the electrical network. Flexible AC transmission system (FACTS) devices are also becoming more commonplace in modern power system to mitigate growing demand and to relieve congestion from the network. This paper aims to solve the OPF where the generation cost is optimized with incorporation of stochastic wind power and several types of FACTS devices such as static VAR compensator, thyristor-controlled series compensator and thyristor-controlled phase shifter. Case studies with both fixed and uncertain load demands are performed. The stochastic wind energy and load demand are modeled using suitable probability density functions. Optimization objective considers cost of thermal generation, direct cost of scheduled wind power, penalty cost for underestimation and reserve cost for overestimation of the wind power. In addition, both locations and ratings of the FACTS devices are optimized to minimize total generation cost of the system. Success history-based adaptive differential evolution (SHADE), a powerful evolutionary algorithm, is adopted to perform the optimization task. The constraints of OPF problem are handled using superiority of feasible solutions (SF) method. The integration approach of SF method with several popular metaheuristic algorithms has been proposed in this work, and a detailed comparative analysis among various algorithms establishes SHADE algorithm to be the best performer.

Success History-Based Adaptive Differential Evolution Using Turning-Based Mutation

Single objective optimization algorithms are the foundation of establishing more complex methods, like constrained optimization, niching and multi-objective algorithms. Therefore, improvements to single objective optimization algorithms are important because they can impact other domains as well. This paper proposes a method using turning-based mutation that is aimed to solve the problem of premature convergence of algorithms based on SHADE (Success-History based Adaptive Differential Evolution) in high dimensional search space. The proposed method is tested on the Single Objective Bound Constrained Numerical Optimization (CEC2020) benchmark sets in 5, 10, 15, and 20 dimensions for all SHADE, L-SHADE, and jSO algorithms. The effectiveness of the method is verified by population diversity measure and population clustering analysis. In addition, the new versions (Tb-SHADE, TbL-SHADE and Tb-jSO) using the proposed turning-based mutation get apparently better optimization results than the original algorithms (SHADE, L-SHADE, and jSO) as well as the advanced DISH and the jDE100 algorithms in 10, 15, and 20 dimensional functions, but only have advantages compared with the advanced j2020 algorithm in 5 dimensional functions.

A novel local search method for LSGO with golden ratio and dynamic search step

Depending on the developing technology, large-scale problems have emerged in many areas such as business, science, and engineering. Therefore, large-scale optimization problems and solution techniques have become an important research field. One of the most effective methods used in this research field is memetic algorithm which is the combination of evolutionary algorithms and local search methods. The local search method is an important part that greatly affects the memetic algorithm’s performance. In this paper, a novel local search method which can be used in memetic algorithms is proposed. This local search method is named as golden ratio guided local search with dynamic step size (GRGLS). To evaluate the performance of proposed local search method, two different performance evaluations were performed. In the first evaluation, memetic success history-based adaptive differential evolution with linear population size reduction and semi-parameter adaptation (MLSHADE-SPA) was chosen as the main framework and comparison is made between three local search methods which are GRGLS, multiple trajectory search local search (MTS-LS1) and modified multiple trajectory search. In the second evaluation, the improved MLSHADE-SPA (IMLSHADE-SPA) framework which is a combination of MLSHADE-SPA framework and proposed local search method (GRGLS) was compared with some recently proposed nine algorithms. Both of the experiments were performed using CEC’2013 benchmark set designed for large-scale global optimization. In general terms, the proposed method achieves good results in all functions, but it performs superior on overlapping and non-separable functions.

Parameter extraction of solar photovoltaic module by using a novel hybrid marine predators – success history based adaptive differential evolution algorithm

ABSTRACT The manufacturers of photovoltaic (PV) panel give the data of three major points on I–V characteristics. However, this information alone is not sufficient to derive the five parameter and seven parameter models, i.e., single-diode and double-diode models. Hence, several population-based metaheuristic techniques are proposed in the literature. However, there is a need of well-balanced algorithm which is used for extracting the parameters of the diode model of PV panels from the datasheet. In this paper, a novel hybrid algorithm is proposed by combing the best features of a recently developed Marine Predators Algorithm (MPA) and Success History based Adaptive Differential Evolution (SHADE) algorithm. The principal algorithm for obtaining the optimum solution is MPA. However, during the search process to enhance the best solution region, self-adaptive DE based on the successive history of parameters is used. The derived objective function ensures the zero error at three important points of the I–V characteristics. Hence, the parameters extracted by using proposed method results in the I–V curves which are passing through the all three important points. Only three parameters out of five in single-diode model, and five parameters out of seven are optimized with the proposed algorithm and remaining are calculated analytically to reduce the burden on metaheuristic algorithm. MATLAB programming is used to test the proposed parameter extraction by using hybrid Marine Predators – Success History based Adaptive Differential Evolution (MP-SHADE) algorithm, and the results are compared with the other state-of-the-art metaheuristic techniques. The single-diode and double-diode models of three types of panels (monocrystalline, polycrystalline, and thin-film) are derived by using MP-SHADE algorithm.

Comparative Study of Heuristic Algorithms for Electrical Impedance Tomography

Based on electrical measurements from electrodes placed around the boundary of a body, electrical impedance tomography (EIT) is an imaging procedure that recovers the spatial distribution of the conductivities in the interior of a body. Recent studies have shown promising results in reconstructing EIT images using heuristic algorithms. This work presents a study of the applicability of six heuristic algorithms – firefly algorithm (FA), novel bat algorithm (NBA), genetic algorithm with new multi-parent crossover (GA-MPC), success history-based adaptive differential evolution with linear population size reduction with semiparameter adaptation hybrid with covariance matrix adaptation evolutionary strategy (LSHADE-SPACMA), ensemble sinusoidal differential covariance matrix adaptation (LSHADE-cnEpSin), and effective butterfly optimizer with covariance matrix adapted retreat phase (EBOwithCMAR) – for the EIT image reconstruction problem. These algorithms have never been employed to solve the EIT inverse problem. Series of numerical tests were carried out to compare the performance of the selected algorithms.