Meta-heuristic Optimization Method Research Articles

Parameter estimation of PEMFC model based on Harris Hawks’ optimization and atom search optimization algorithms

Proton exchange membrane fuel cell (PEMFC) is considered as propitious solution for an environmentally friendly energy source. A precise model of PEMFC for accurate identification of its polarization curve and in-depth understanding of all its operating characteristics attracted the interest of many researchers. In this paper, novel meta-heuristic optimization methods have been successfully applied to evaluate the unknown parameters of PEMFC models, particularly Harris Hawks’ optimization (HHO) and atom search optimization (ASO) techniques. The proposed optimization algorithms have been tested on three different commercial PEMFC stacks, namely BCS 500-W PEM, 500W SR-12PEM and 250W stack, under various operating conditions. The sum of square errors (SSE) between the results obtained by the application of the estimated parameters and the experimentally measured results of the fuel cell stacks was considered as the objective function of the optimization problem. In order to validate the effectiveness of the proposed methods, the results are compared with that obtained in studies. Moreover, the I/V curves obtained by the application of HHO and ASO showed a clear matching with data sheet curves for all the studied cases. Finally, PEMFC model based on HHO technique surpasses all compared algorithms in terms of the solution accuracy and the convergence speed.

Multi-criteria decision making monarch butterfly optimization for optimal distributed energy resources mix in distribution networks

The optimal integration of distributed energy resources (DERs) is a multiobjective and complex combinatorial optimization problem that conventional optimization methods cannot solve efficiently. This paper reviews the existing DER integration models, optimization and multi-criteria decision-making approaches. Further to that, a recently developed monarch butterfly optimization method is introduced to solve the problem of DER mix in distribution systems. A new multiobjective DER integration problem is formulated to find the optimal sites, sizes and mix (dispatchable and non-dispatchable) for DERs considering multiple key performance objectives. Besides, a hybrid method that combines the monarch butterfly optimization and the technique for order of preference by similarity to ideal solution (TOPSIS) is proposed to solve the formulated large-scale multi-criteria decision-making problem. Whilst the meta-heuristic optimization method generates non-dominated solutions (creating Pareto-front), the TOPSIS approach selects that with the most promising outcome from a large number of alternatives. The effectiveness of this approach is verified by solving single and multiobjective dispatchable DER integration problems over the benchmark 33-bus distribution system and the performance is compared with the existing optimization methods. The proposed model of DER mix and the optimization technique significantly improve the system performance in terms of average annual energy loss reduction by 78.36%, mean node voltage deviation improvement by 9.59% and average branches loadability limits enhancement by 50%, and minimized the power fluctuation induced by 48.39% renewable penetration. The proposed optimization techniques outperform the existing methods with promising exploration and exploitation abilities to solve engineering optimization problems.

A Modified Shuffled Frog Leaping Algorithm for the Topology Optimization of Electromagnet Devices

The memetic algorithms which employ population information spreading mechanism have shown great potentials in solving complex three-dimensional black-box problems. In this paper, a newly developed memetic meta-heuristic optimization method, known as shuffled frog leaping algorithm (SFLA), is modified and applied to topology optimization of electromagnetic problems. Compared to the conventional SFLA, the proposed algorithm has an extra local search step, which allows it to escape from the local optimum, and hence avoid the problem of premature convergence to continue its search for more accurate results. To validate the performance of the proposed method, it was applied to solving the topology optimization of an interior permanent magnet motor. Two other EAs, namely the conventional SFLA and local-search genetic algorithm, were applied to study the same problem and their performances were compared with that of the proposed algorithm. The results indicate that the proposed algorithm has the best trade-off between the results of objective values and optimization time, and hence is more efficient in topology optimization of electromagnetic devices.

Global performance of metaheuristic optimization tools for water distribution networks

Numerous metaheuristic optimization algorithms are used for optimal design of water distribution networks. Each algorithm shows dissimilar characteristics depending on the network properties and the sensitivity analysis of the algorithm control variables. New performance metrics of metaheuristic optimization methods are proposed using simple but robust refined metrics and were applied to the available literature data for different algorithms which have previously been used for three popular benchmark water distribution networks. In general, recent performance metrics are devoted to measure effectiveness, efficiency, and reliability in a separate manner, which made some confusion, which is the best?. In the present work, the proposed metrics are used to calculate both of best global and average global performance of different optimization algorithms. The results show that the present metrics have a good distinctive performance between different algorithms. The Fittest individual referenced Differential Evolution is found to be the best algorithm.

Approximation of two-variable functions using high-order Takagi\u2013Sugeno fuzzy systems, sparse regressions, and metaheuristic optimization

This paper proposes a new hybrid method for training high-order Takagi–Sugeno fuzzy systems using sparse regressions and metaheuristic optimization. The fuzzy system is considered with Gaussian fuzzy sets in the antecedents and high-order polynomials in the consequents of fuzzy rules. The fuzzy sets can be chosen manually or determined by a metaheuristic optimization method (particle swarm optimization, genetic algorithm or simulated annealing), while the polynomials are obtained using ordinary least squares, ridge regression or sparse regressions (forward selection, least angle regression, least absolute shrinkage and selection operator, and elastic net regression). A quality criterion is proposed that expresses a compromise between the prediction ability of the fuzzy model and its sparsity. The conducted experiments showed that: (a) the use of sparse regressions and/or metaheuristic optimization can reduce the validation error compared with the reference method, and (b) the use of sparse regressions may simplify the fuzzy model by zeroing some of the coefficients.

Member Sizing Optimization of Large Scale Steel Space Trusses Using a Symbiotic Organisms Search Algorithm

A systematic approach of optimization is needed to achieve an optimal design of large and complex truss structures. In the last three decades, several researchers have developed and applied various metaheuristic optimization methods to the design of truss structures. This paper investigates a new metaheuristic algorithm called symbiotic organisms search (SOS) for member sizing optimization of relatively large steel trusses. The case studies include a 120-bar dome truss and a 942-bar tower truss. The structural analyses are carried out using the standard finite element method. The profiles of the truss members are circular hollow structural sections selected from a set of the American Institute of Steel Construction standard profiles. The design results using the SOS are then compared to those obtained using other metaheuristic methods, namely the particle swarm optimization, differential evolution, and teaching-learning-based optimization. The comparison shows the superior performance of the SOS in terms of the optimal solution, consistency, and convergence. Thus, the SOS is a good alternative for optimizing the design of steel truss structures in real engineering practice.

Opposition-based artificial electric field algorithm and its application to FOPID controller design for unstable magnetic ball suspension system

In this study, an improved version of artificial electric field (AEF) algorithm, named as opposition-based AEF (ObAEF), has been proposed for the first time ever to tune a fractional order PID (FOPID) controller used in a magnetic ball suspension system. The basic AEF algorithm is a novel physics-inspired, population-based meta-heuristic optimization method that mathematically mimics Coulomb’s electrostatic force between charged particles. The proposed ObAEF algorithm is the improved version of the AEF which utilizes the opposition-based learning strategy to enhance the AEF algorithm’s exploration capability. To validate the performance, the novel ObAEF algorithm was applied to 6 well-known benchmark optimization problems of Sphere, Rosenbrock, Schwefel, Ackley, Egg Crate and Easom. The results were also compared with other algorithms such as basic AEF, atom search optimization (ASO) and artificial bee colony (ABC). It was also used to tune FOPID controller (ObAEF-FOPID) to improve the transient response of a magnetic levitation (maglev) system by minimizing a new objective function having a simple structure. The latter was proposed to minimize the maximum overshoot, settling and rise times along with steady state error of magnetically suspended ball’s position. The convergence profile and statistical analyzes were conducted to illustrate the success of the proposed algorithm. The effectiveness and superiority of the ObAEF-FOPID controller was further investigated through frequency response analysis and again compared with AEF, ABC and ASO based FOPID controllers as in statistical success, convergence profile and transient analyses. The results showed that the proposed ObAEF-FOPID has better control performance than those tuned by AEF, ASO and ABC algorithms.

Hybrid Elephant Herding and Particle Swarm Optimizations for Optimal DG Integration in Distribution Networks

In this article, the amalgamation of two well-established meta-heuristic optimization methods is presented to solve the multi-objective distributed generation (DG) allocation problem of distribution systems. To overcome some of the shortcomings of newly developed elephant herding optimization (EHO), an improvement is suggested and then, a prominent feature of particle swarm optimization is introduced to the modified version of EHO. The suggested modifications are validated by solving a single objective DG integration problem where various performance parameters of the proposed hybrid method are compared with their individual standard variants. After validation, the proposed technique is exploited to solve a multi-objective DG allocation problem of distribution systems, aiming to minimize power loss and node voltage deviation while simultaneously maximizing the voltage stability index of three benchmark distribution systems namely, 33-bus, 69-bus and 118-bus. The obtained simulation results are further compared with that of the same available in the existing literature. This comparison reveals that the proposed hybrid approach is promising to solve the multi-objective DG integration problem of distribution systems as compared to many existing methods.

An enhanced teaching-learning-based optimization algorithm with self-adaptive and learning operators and its search bias towards origin

Abstract Teaching-learning-based optimization (TLBO) is a recently proposed meta-heuristic optimization method and demonstrates outstanding performance for solving numerous sciences and engineering problems. However, many studies have shown that TLBO has a strong bias towards converging to origin and poorly performs in the problems with shifted solutions. To conquer this problem, a novel self-adaptive hybrid self-learning based TLBO (SHSLTLBO) is proposed in this paper. By constructing a self-adaptive framework, the original TLBO is fused with the Gaussian distribution, and the novel updating law switches in two modes corresponding to the fitness during the searching process. Additionally, in order to avoid local convergence in the initialization, a self-learning phase is introduced, associated to the original teaching and learning phase. The performance of the proposed SHSLTLBO is tested in the numerical benchmark functions, where the comprehensive comparison carries out with the state-of-the-art TLBO variants and other meta-heuristic approaches. The results demonstrate superior advantages of the proposed algorithm at balancing along the evolutionary stages among the TLBO variants. In the experiments, the two well-performed meta-heuristic optimization methods, namely LSHADE and HCLPSO, are used as the comparative methods to SHSLTLBO, where their superior performances of them are shown on the shifted problems. With increasing dimensionality, the performance of TLBO goes down rapidly while LSHADE's relative ranking improves. Apart from these scenarios, the comparative results of different dimensional problems indicate that the proposed SHSLTLBO has the best convergence and stability in solving all 28 benchmarks with low dimension and shifted solutions.

An efficient two-step damage identification method using sunflower optimization algorithm and mode shape curvature (MSDBI–SFO)

Laminated composite structures performance and behavior can be affected by damage that is not always visible on the surface. The need to monitor the health of these structures has continuously increased, which can be achieved in a fast and cost-effective way by numerical simulations. This paper presents an efficient two-step approach for damage identification in laminated composite plates. The first step uses mode shape and its derivatives (mode shape curvature) to locate the damages based on modal data. The proposed indicator utilizes modal analysis information extracted from finite element analysis. Then, a new metaheuristic Sunflower Optimization method (SFO) is employed to assess the correct severity of induced damages. This technique considers the damage detection problem as an inverse problem with minimization of an objective function. Numerical examples considering laminated composite plate with one and two induced damaged sites (delamination) are considered. The results indicate that the proposed method not only successfully identifies the location and severity of multi-damage cases in the composite structures, but also provides a better efficiency in terms of time saving and computational costs.

The study of reducing the cost of investment in wind energy based on the cat swarm optimization with high reliability

Wind and solar are the most important source of renewable energy for power supply in remote locations involves serious consideration of the reliability of these unconventional energy sources. We apply the cat swarm meta-heuristic optimization method to solve the problem of wind power system design optimization. The electrical power components of the system are characterized by their cost, capacity and reliability. This study seeks to optimize the design of parallel power systems in which multiple choices of generators wind, transformers and lines. Our plan has the advantage of allowing electrical components with different parameters to be customized in electrical power systems. The UMGF method is applied to allow rapid reliability estimation. A computer program is developed for the UMGF application and CS algorithm. An example is provided to explain.

Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models

The main objective of the present work is to estimate the load-carrying capacity of concrete-filled steel tubes (CFST) under axial compression using hybrid artificial intelligence (AI) algorithms. In particular, the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic optimization methods, such as the biogeography-based optimization (ANFIS-BBO), the particle swarm optimization (ANFIS-PSO), and the genetic algorithm (ANFIS-GA), have been employed taking into account the variability of input parameters. Commonly used statistical criteria, such as the coefficient of determination (R2), the a20-index, and the root mean squared error (RMSE), were utilized to evaluate and compare the effectiveness of the proposed AI models. The Monte Carlo approach was used to propagate the variability in the input space to the predicted output. The results showed that the ANFIS system, optimized by PSO, was the most effective and robust model with respect to three considered criteria (a20-index = 0.881, R2 = 0.942 and RMSE = 185.631). Sensitivity analysis was performed, indicating that the minor axis length and thickness of the steel tube exhibited the highest contribution to the axial compression load-carrying capacity of the CFST.

Service restoration enhancement by FIs deployment in distribution system considering available AMI system

Service restoration is considered as one of the most staple functions of distribution automation, which executes the fault location, isolation and service restoration (FLISR) function in case of contingency occurrence. The first part of the FLISR function is the fault location that can be improved through fault indicators (FIs) employment in electricity distribution system (EDS). On the other hand, the advanced metering infrastructure (AMI) is widely proliferated in EDSs in order to gather the consumers' consumption data for the operational purposes. In this study, FIs are deployed in the EDS considering available AMI data, using a metaheuristic optimisation method. The economic objective function of the optimisation problem consists of the total customers' interruption cost and the total cost of FI deployment. In addition, the technical objective function of the problem is the system average interruption duration index. In order to investigate the effectiveness of the proposed method, it is implemented on bus number four of the Roy Billinton test system. The results show that the number of deployed FIs reduces when the electric utility uses the proposed method.

Phase space elliptic density feature for epileptic EEG signals classification using metaheuristic optimization method

The electroencephalography (EEG), which is a method for monitoring the brain signals, is a common method used to diagnose the epileptic seizures. In this study, some features are presented for the classification of the brain signals. These features are based on the texture and structure of the brain signals in the phase space representation (PSR). Due to the resonance property, the data are elliptical in the phase space. Therefore, the mentioned features are based on the calculations of the data density in the ellipses.In the first method of feature extraction, the radius values of the ellipses are assumed based on the normal distribution feature. In the two other methods of the presented features, the radius values of the assumed ellipses are calculated by the optimizer. These methods of feature extraction are based on the incremental ellipses and intersecting ellipses, respectively. The density of the data in the assumed ellipses is given to the k-nearest neighbor as a feature to classify the epileptic seizure and seizure-free EEG signals.The intended method was implemented and investigated on two databases of the Bonn university of Germany and the neurology and sleep center of New Delhi. The results indicate that the proposed features are strong tools for separation and diagnosis of this type of signal and have higher accuracy compared to the other classic and updated methods. The extraction speed of the presented features was higher in the test phase compared to the other methods.

The cost-effective and multi-objective optimal positioning of active actuators based on tug of war method in seismically excited benchmark highway bridge

The optimum number and location of control devices not only play an important role in effectiveness of the structural control system but also lead to a cost-effective structural design. In the current paper, based on an innovative meta-heuristic optimization method, tug of war optimization (TWO) firstly, an active control algorithm is designed in order to reduce the structural responses and then the optimum number and location of control devices are found to increase the efficiency of the bridge control system. The presented active control algorithm is a neuro-fuzzy adaptive controller that its output functions parameters are optimized by the tug of war optimization method. The optimum number and location of actuators is determined based on the use of the multi-objective mode of TWO and minimization of three objective functions. The first one is maximum of the bridge base shear, second is maximum of mid-span displacement and the third is maximum of mid-span acceleration. In addition, through choosing a predefined performance level for the dynamic responses of the bridge structure, the designer would be able to decide on increasing or decreasing the control devices and minimizing control costs. The optimization results for different number of actuators are shown in this work. The results show that achieving an ideal control plan requires both an appropriate control algorithm and an actuator optimal placement technique. In this paper, using these two factors, the responses of the structure have been desirably less than the responses provided in Ning et al. (Struct Control Health Monit 16(6):639–656, 2009).

Comparison of metaheuristic optimisation methods for grid-edge technology that leverages heat pumps and thermal energy storage

Grid-edge technology can unlock flexibility from consumers to contribute to meeting the growing need for flexibility in European energy systems. Furthermore, power-to-heat technology such as heat pumps and thermal energy storage has been shown to both decarbonise heat and enable the cost-effective integration of more renewable electricity into the grid. The consumer's reaction to price signals in this context presents the opportunity to simultaneously unlock operational cost reductions for consumers and localised implicit demand-side flexibility to benefit grid operators.In this paper, the prediction accuracy, run-time, and reliability of several (metaheuristic) optimisation algorithms to derive optimal operation schedules for heat pump-based grid-edge technology are investigated. To compare effectiveness, an optimisation effectiveness indicator OEI is defined. Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA) were found to be most effective and robust in yielding quasi-optimal minima for the non-linear, multi-modal, and discontinuous cost function. GA optimisation with binary variables is 5–15 times more effective than with continuous variables. Using continuous variables, PSO is more effective than GA due to smaller optimisation error, shorter run-time, and higher reliability (smaller standard deviation). Simulated Annealing and Direct (Pattern) Search were found to be not very effective.

Application of hybrid multi-objective moth flame optimization technique for optimal performance of hybrid micro-grid system

The research work carried out here deals with the application of a Hybrid Micro-Grid System (HMGS), which includes solar/wind/battery storage/diesel generator applied to three different parts of India. For a better analysis of all the three cases, an efficient and recent metaheuristic optimization method named hybrid multi-objective moth flame optimization (HMOMFO) technique has been used in MATLAB. The aim is to find better candidate solutions for which particle swarm optimization (PSO) technique and levy flight method are integrated, with the moth flame optimization (MFO) algorithm Moreover a new enhanced differential evolution algorithm (EDE) with self-adjustable parameters has been integrated with the second phase of the hybrid algorithm to enhance the searching and exploitation capabilities of the proposed algorithm. Here, for an initial load of 15 households, simulation and statistical results show that HMOMFO proves to be successful in terms of minimizing the price of electrical energy (PEE). Results further assure that minimum values of loss of power supply probability (LPSP) for Durgapur, Hospet, and Tirunelveli, are obtained using HMOMFO, with fewer iterations. The results also contain optimum photovoltaic (PV) power, battery bank performance in terms of autonomy days (AD), the optimum number of wind turbine generators (WT), and diesel generators (DG). The results demonstrate the mastery and effectiveness of the proposed HMOMFO against three area hybrid micro-grid systems.

Investigation of optimal designs for concrete cantilever retaining walls in different soils

In this paper, the investigation of the optimum designs for two types of concrete cantilever retaining walls was performed utilizing the artificial bee colony algorithm. Stability conditions like safety factors sliding, overturning and bearing capacity and some geometric instances due to inherent of the wall were considered as the design constraints. The effect of the existence of the key in wall design on the objective function was probed for changeable properties of foundation and backfill soils. In optimization analysis, wall concrete weight which directly affect parameters such as carbon dioxide emission and the cost was considered as the objective function and analyzes were performed according to different discrete design variables. The optimum concrete cantilever retaining wall designs satisfying constraints of stability conditions and geometric instances were obtained for different soil cases. Optimum designs of concrete cantilever retaining wall with the key were attained in some soil cases which were not found the feasible optimum solution of the concrete cantilever retaining wall. Results illustrate that the artificial bee colony algorithm was a favorable metaheuristic optimization method to gain optimum designs of concrete cantilever retaining wall.

Enhancing Linear Independent Component Analysis: Comparison of Various Metaheuristic Methods

Various methods have been exploited in the blind source separation problems, especially in cocktail party problems. The most commonly used method is the independent component analysis (ICA). Many linear and nonlinear ICA methods, such as the radial basis functions (RBF) and self-organizing map (SOM) methods utilise neural networks and genetic algorithms as optimisation methods. For the contrast function, most of the traditional methods, especially the neural networks, use the gradient descent as an objective function for the ICA method. Most of these methods trap in local minima and consume numerous computation requirements. Three metaheuristic optimisation methods, namely particle, quantum particle, and glowworm swarm optimisation methods are introduced in this study to enhance the existing ICA methods. The proposed methods exhibit better results in separation than those in the traditional methods according to the following separation quality measurements: signal-to-noise ratio, signal-to-interference ratio, log-likelihood ratio, perceptual evaluation speech quality and computation time. These methods effectively achieved an independent identical distribution condition when the sampling frequency of the signals is 8 kHz.

A Review of Optimization Algorithms in Solving Hydro Generation Scheduling Problems

The optimal generation scheduling (OGS) of hydropower units holds an important position in electric power systems, which is significantly investigated as a research issue. Hydropower has a slight social and ecological effect when compared with other types of sustainable power source. The target of long-, mid-, and short-term hydro scheduling (LMSTHS) problems is to optimize the power generation schedule of the accessible hydropower units, which generate maximum energy by utilizing the available potential during a specific period. Numerous traditional optimization procedures are first presented for making a solution to the LMSTHS problem. Lately, various optimization approaches, which have been assigned as a procedure based on experiences, have been executed to get the optimal solution of the generation scheduling of hydro systems. This article offers a complete survey of the implementation of various methods to get the OGS of hydro systems by examining the executed methods from various perspectives. Optimal solutions obtained by a collection of meta-heuristic optimization methods for various experience cases are established, and the presented methods are compared according to the case study, limitation of parameters, optimization techniques, and consideration of the main goal. Previous studies are mostly focused on hydro scheduling that is based on a reservoir of hydropower plants. Future study aspects are also considered, which are presented as the key issue surrounding the LMSTHS problem.