Real-world Engineering Problems Research Articles

Optimal Tuning of Power System Stabilizers for a Multi-Machine Power Systems Using Hybrid Gorilla Troops and Gradient-Based Optimizers

This work discusses the production of a novel hybrid algorithm by combining the gorilla troops optimizer (GTO) with the gradient-based optimizers (GBO) approach. The novel approach is called GTO-GBO, it is offered as a useful tool for optimizing the power system stabilizer (PSS) used in the IEEE four-generator, two-area multi-machine power system subjected to a three-phase short-circuit fault. MATLAB/Simulink software was utilized to carry out the assessments. The suggested approach is initially evaluated using multiple benchmark functions with unimodal and multimodal properties. The results are then compared to other competing algorithms (artificial ecosystem optimizer, artificial rabbits optimizer, Coati Optimization Algorithm, and northern goshawk optimization). The comparisons with various algorithms reveal the developed hybrid GTO-GBO algorithm’s considerable promise. This demonstrates the GTO-GBO algorithm’s improved balance of global and local search stages. The proposed GTO-GBO algorithm’s performance is also evaluated by developing an optimum performing PSS for further examination, allowing observation of its capabilities for difficult real-world engineering challenges. To illustrate the applicability and superior performance of the suggested hybrid algorithm for such a complicated real-world engineering problem, the PSS damping controller is formulated as an optimization problem, and the developed GTO-GBO algorithm is used to search for optimal controller parameters. The latter case’s findings are compared to the competitive optimization algorithms where the GTO-GBO demonstrates the efficiency and robustness of this suggested optimization algorithm to enhance power system stability.

Many-Objective Real-World Engineering Problems: A Comparative Study of State-of-the-Art Algorithms

Many-Objective Real-World Engineering Problems: A Comparative Study of State-of-the-Art Algorithms

Chaotic marine predators algorithm for global optimization of real-world engineering problems

A novel metaheuristic called Chaotic Marine Predators Algorithm (CMPA) is proposed and investigated for the optimization of engineering problems. CMPA integrates the exploration merits of the recently proposed Marine Predators Algorithm (MPA) with the chaotic maps exploitation capabilities. Several chaotic maps were applied in the proposed CMPA to govern MPA parameters that eventually led to controlled exploration and exploitation of search. This study makes an initial attempt to explore and employ CMPA in decoding complex and challenging design and manufacturing problems. For performance evaluation of the proposed algorithm, CEC 2020 numerical problems having different dimensions and five widely adopted constrained design problems were solved. For all problems, both qualitative and qualitative results are examined and discussed. Moreover, two case studies of multi-pass turning were examined by the proposed CMPA algorithm to optimize the cutting operation with a minimum cost of production per unit objective. Furthermore, the suggested CMPA algorithm has been investigated for solving a real-world structural topology optimization problem. Statistical analysis is performed, and the results of CMPA are compared with twelve distinguished algorithms. Outcomes of the proposed variant algorithm on the benchmarks demonstrate its significantly improved performance relative to other optimizers including a variant of MPA and two state-of-the-art IEEE CEC competitions winners algorithms. Findings from the manufacturing process exhibit CMPA proficiency in solving arduous real-world design problems.

Effectiveness of in-class excel-based active learning activities for transportation engineering courses

Recently, transportation engineering industry members showed concern that students lacked the skills to solve real-world engineering problems using spreadsheet data analysis. In response to the circumstances shown by industry members, this study investigated how to engage students in a better way by incorporating spreadsheet analysis during class and helping them learn the course topics. Helping students link theoretical knowledge to real-world problems can be a challenge. In this effort, in-class activities and worksheets were redesigned to integrate with Excel to solve example problems using built-in tools, including cell references, equations, data analysis tool pack, solver tool, conditional formatting, charts, etc. The effectiveness of this technique was investigated using students’ evaluations of the course, enrollment data, and students’ comments. Based on the data of those criteria, it is evident that spreadsheet activities may increase student learning.

Improved multi-objective differential evolution algorithm based on a decomposition strategy for multi-objective optimization problems

Many real-world engineering problems need to balance different objectives and can be formatted as multi-objective optimization problem. An effective multi-objective algorithm can achieve a set of optimal solutions that can make a tradeoff between different objectives, which is valuable to further explore and design. In this paper, an improved multi-objective differential evolution algorithm (MOEA/D/DEM) based on a decomposition strategy is proposed to improve the performance of differential evolution algorithm for practical multi-objective nutrition decision problems. Firstly, considering the neighborhood characteristic, a neighbor intimacy factor is designed in the search process for enhancing the diversity of the population, then a new Gaussian mutation strategy with variable step size is proposed to reduce the probability of escaping local optimum area and improve the local search ability. Finally, the proposed algorithm is tested by classic test problems (DTLZ1-7 and WFG1-9) and applied to the multi-objective nutrition decision problems, compared to the other reported multi-objective algorithms, the proposed algorithm has a better search capability and obtained competitive results.

Stability of Finite Difference Schemes to Pseudo-Hyperbolic Telegraph Equation

Hyperbolic partial differential equations are frequently referenced in modeling real-world problems in mathematics and engineering. Therefore, in this study, an initial-boundary value issue is proposed for the pseudo-hyperbolic telegraph equation. By operator method, converting the PDE to an ODE provides an exact answer to this problem. After that, the finite difference method is applied to construct first-order finite difference schemes to calculate approximate numerical solutions. The stability estimations of finite difference schemes are shown, as well as some numerical tests to check the correctness in comparison to the precise solution. The numerical solution is subjected to error analysis. As a result of the error analysis, the maximum norm errors tend to decrease as we increase the grid points. It can be drawn that the established scheme is accurate and effective

Unified space approach-based Dynamic Switched Crowding (DSC): A new method for designing Pareto-based multi/many-objective algorithms

This study proposes a robust method to improve the search performance of multi-objective evolutionary algorithms (MOEAs) using a Pareto-based archiving mechanism and a crowding distance-based archive handling mechanism. The aim of the proposed method is to provide a sustainable diversity in the objective and decision spaces and to establish a stable exploitation-exploration balance in both spaces. To this purpose: 1) reference space combinations are defined, 2) strategies consisting of reference space combinations are developed to improve the performance of the crowding-distance calculation, and 3) a dynamic switching mechanism is proposed to implement these strategies. In the proposed DSC method, non-dominated solutions are represented in three different reference spaces: the decision space, the objective space, and the unified result of these two spaces. Binary combinations of these three spaces were created and strategies using different reference spaces were developed. The switching mechanism was designed to implement these strategies dynamically. Crowding-distance calculation was performed with reference to the space vector selected by this switching mechanism. The proposed DSC method was tested on multimodal multi-objective optimization problems (MMOPs) and real-world engineering problems incorporating both alternating current optimal power flow (AC-OPF) and alternating current/direct current optimal power flow (AC/DC-OPF). According to the experimental study results, the proposed DSC-MOAGDE algorithm has about 30% better success rate on MMOPs compared to its competitors. Similarly, the proposed method was able to optimize cost by 6.66%, 24.15%, 52.13%, 56.72%, and 120.21% better than the MMODE_ICD, MO_RING_PSO_SCD, MOAGDE, NSWOA and SSMOPSO algorithms in AC-OPF and AC/DC-OPF real-world problems.

Local search enhanced Aquila optimization algorithm ameliorated with an ensemble of Wavelet mutation strategies for complex optimization problems

Aquila Optimization Algorithm (AQUILA) is a newly emerged metaheuristic optimizer for solving global optimization problems, which is based on intrinsic hunting behaviors of the foraging aquila individuals. However, this stochastic optimization method suffers from some algorithm-specific drawbacks, such as premature convergence to the local optimum points over the search hyperspace due to the lack of solution diversity in the population. To conquer this algorithmic deficiency, an ensemble of Wavelet mutation operators has been implemented into the standard AQUILA to enhance the explorative capabilities of the algorithm by diversifying the search domain as much as possible. Furthermore, a brand-new local search scheme empowered by the synergetic interactions of elite opposition-based learning and a simple-yet-effective exploitative manipulation equation is introduced into the base AQUILA to intensify on the previously visited promising regions. The proposed learning schemes are stochastically applied to the obtained solutions from the base Aquila algorithm to refine the overall solution quality and amend the premature convergence problem. It is also aimed to investigate whether the collective application of Wavelet mutation operators with different types entails a significant improvement in the general search effectivity of the algorithm rather than their individual efforts. Numerical experiments made on a suite of unconstrained unimodal and multimodal benchmark functions reveal that this hybridization with AQUILA has improved the general solution accuracy and stability to very high standards, outperforming its contemporary counterparts in the comparative statistical analysis. Furthermore, an exhaustive benchmark analysis has been performed on fourteen constrained real-world complex engineering problems.

Fick’s Law Algorithm: A physical law-based algorithm for numerical optimization

Recently, many metaheuristic optimization algorithms have been developed to address real-world issues. In this study, a new physics-based metaheuristic called Fick’s law optimization (FLA) is presented, in which Fick’s first rule of diffusion is utilized. According to Fick’s law of diffusion, molecules tend to diffuse from higher to lower concentration areas. Many experimental series are done to test FLA’s performance and ability in solving different optimization problems. Firstly, FLA is tested using twenty well-known benchmark functions and thirty CEC2017 test functions. Secondly, five real-world engineering problems are utilized to demonstrate the feasibility of the proposed FLA. The findings are compared with 12 well-known and powerful optimizers. A Wilcoxon rank-sum test is carried out to evaluate the comparable statistical performance of competing algorithms. Results prove that FLA achieves competitive and promising findings, a good convergence curve rate, and a good balance between exploration and exploitation. The source code is currently available for public from: https://se.mathworks.com/matlabcentral/fileexchange/121033-fick-s-law-algorithm-fla.

Disturbance inspired equilibrium optimizer with application to constrained engineering design problems

This work proposes a novel adaptive global optimization algorithm called Disturbance Inspired Equilibrium Optimizer. The purpose of this study is to enhance the exploitation ability of the newly developed Equilibrium Optimizer, and to address the issue of getting trapped in local minima. The proposed algorithm is benefited from the novel disturbance-based hybrid initialization strategy, the new form of time factor, and the new update rule of particle's position. In addition, a novel boundary check strategy and an adaptive global position disturbance mechanism are proposed and installed into our algorithm. Based on the disturbance-inspired modifications, the exploration and exploitation ability of the standard Equilibrium Optimizer are significantly improved. The performance of the proposed algorithm is evaluated using representative different benchmark functions, consisting of three well-known mathematical benchmark functions, six complex composite functions, and four challenge functions proposed on 2017 IEEE Congress on Evolutionary Computation. Also, the proposed algorithm is conducted to optimize three engineering designs to examine its applicability in constrained real-world problems. In all experiments, the developed algorithm is compared with six other state-of-the-art metaheuristics. Experimental results and the average rank of Friedman test show that our algorithm provides promising results in solving mathematical problems and constrained real-world engineering optimization problems. Therefore, the proposed algorithm is competitive compared to the other state-of-the-art metaheuristic algorithms and is an effective solution to real-world engineering problems.

A novel automated SuperLearner using a genetic algorithm-based hyperparameter optimization

Industrial revolution 4.0 has pushed industries worldwide to use machine learning (ML) models to address real-world engineering problems. The industry generally faces two main challenges in ML applications: the lack of skilled data scientists and the cost of obtaining large labeled datasets. These challenges need to be addressed to unlock the full potential of ML. In this work, a novel automated SuperLearner (SL) model using a genetic algorithm (AutoSL-GA) based hyperparameter optimization (HPO) is introduced to address the aforementioned challenges and assist scientists and engineers. Detailed comparisons are performed between AutoSL-GA, SuperLearner using Bayesian-based HPO (AutoSL-BO), and another well-known automated machine learning (AutoML) algorithm called Tree-based pipeline optimization tool (TPOT). Six different benchmark datasets were used to compare the performance and computational times of the models. AutoSL-GA resulted in higher performance with lower computational time than other models for all six benchmark datasets. Finally, a sensitivity analysis for dataset size was performed, in which AutoSL-GA also outperformed the other models across the dataset sizes while consuming the least computational resources.

A Review of the Accuracy of Primal Assembly Model Order Reduction Techniques

Model order reduction (MOR) techniques have been used in structural dynamics for over sixty years as a quick and effective way to examine the dynamic behavior of complex finite element (FE) models. Various macroscopic phenomena arise from the physics and mechanics of the underlying microstructure, and MOR approaches can be employed in conjunction with the multiscale computational homogenization approach, which is deriving local macroscopic constitutive responses from underlying microstructure; to predict the dynamic behavior of heterogeneous macrostructures. The accuracy and efficiency of the most representative approaches from dynamic condensation, component mode synthesis (CMS), and general model reduction (GMR) methods are compared in this paper. The techniques considered are Guyan and the improved reduced system (IRS) from classical dynamic condensation, Craig-Bampton (CB) and enhanced Craig-Bampton (ECB) from CMS, IRS-based substructuring, GMR, and GMRPlus. All the substructuring methods employ primal assembly at the boundary interface of the substructures. The accuracy and computing efficiency of the techniques are compared in-depth using real-world engineering problems, and the effect of the number of substructures on the accuracy and computational cost is also examined.

Fast-Dynamic Grey Wolf Optimizer for solving model order reduction of bilinear systems based on multi-moment matching technique

Grey Wolf Optimizer (GWO) has been known as one of the most popular and powerful metaheuristic search algorithms. It has great advantages such as simplicity, accuracy, and good convergence rate, but its performance degrades when the global optimum is not zero. To address these issues, in this paper, a new formulation is introduced to change its searching and hunting mechanisms, while the updating structure of wolves is also modified. With these changes, a proper compromise is achieved between exploitation and exploration. Therefore, the proposed algorithm, called Fast-Dynamic GWO (FDGWO), is more accurate with a faster convergence rate compared to GWO. Thirty-eight typical benchmark functions are optimized with FDGWO and compared with a set of efficient metaheuristic search algorithms. The Wilcoxon rank-sum test is used to statistically assess the results. With an overall efficacy of 84.2%, FDGWO is the most effective among competing algorithms. Then, the performance of FDGWO for ten real-world constrained engineering problems is compared with some highly accurate recent algorithms such as BP-ϵMAg-ES and COLSHADE. The results show that while FDGWO performs almost as accurate as these algorithms, but its computational complexity is quite less. Next, a new hybrid method based on FDGWO is proposed for model order reduction (MOR) of bilinear systems. First, the order of reduced system is determined via an iterative approach based on H2 norm of the error system. The unknown parameters of reduced model are then determined by minimizing a multi-objective fitness function aiming to match the multi-moments of original and reduced bilinear systems. Some stability constraints based on the Volterra series expansion of the states of the bilinear system are added to the optimization problem, which is solved by FDGWO algorithm. Finally, two bilinear test systems are reduced by the proposed hybrid method and compared with the most common bilinear MOR methods.

An explicit predictor/multicorrector time marching with automatic adaptivity for finite-strain elastodynamics

We propose a time-adaptive predictor/multi-corrector method to solve hyperbolic partial differential equations, based on the generalized-α scheme that provides user-control on the numerical dissipation and second-order accuracy in time. Our time adaptivity uses an error estimation that exploits the recursive structure of the variable updates. The predictor/multicorrector method explicitly updates the equation system but computes the residual of the system implicitly. We analyze the method's stability and describe how to determine the parameters that ensure high-frequency dissipation and accurate low-frequency approximation. Subsequently, we solve a linear wave equation, followed by non-linear finite strain deformation problems with different boundary conditions. Thus, our method is a straightforward, stable and computationally efficient approach to simulate real-world engineering problems. Finally, to show the performance of our method, we provide several numerical examples in two and three dimensions. These challenging tests demonstrate that our predictor/multicorrector scheme dynamically adapts to sudden energy releases in the system, capturing impacts and boundary shocks. The method efficiently and stably solves dynamic equations with consistent and under-integrated mass matrices conserving the linear and angular momenta as well as the system's energy for long-integration times.

Modified teaching-learning-based optimization and applications in multi-response machining processes

Many real-world engineering problems such as machining processes are multi-objective optimization problems (MOPs) because multiple performance characteristics are considered to satisfy their contradictory goals. An improved multi-objective teaching–learning-based optimization with refined knowledge sharing mechanisms (IMTLBO-RKSM) is proposed to tackle these MOPs effectively. Pareto dominance concept is first incorporated into IMTLBO-RKSM to handle the tradeoffs of multiple contradictory objectives. Appropriate modifications are incorporated into both teacher and learner phases of IMTLBO-RKSM to emulate to emulate the knowledge sharing processes of classroom more accurately, hence achieving better balancing of exploration and exploitation searches. Particularly, both concepts of Euclidean-distance based teacher assignment scheme and social learning are incorporated into the IMTLBO-RKSM’s teacher phase to derive the unique directional information that can provide better guidance for each learner. The learner phase of IMTLBO-RKSM is also modified by designing two new learning mechanisms known as independent learning and adaptive peer learning, aiming to facilitate different preferences of learners in acquiring new knowledge. The performance of IMTLBO-RKSM is evaluated and compared with six multi-objective optimization methods by using five case studies of multi-response machining problems and twelve MOP benchmark functions. Extensive simulation studies show that IMTLBO-RKSM have more competitive performance than other methods by generating Pareto fronts with better quality in terms of accuracy and diversity of solution members for most tested problems.

An Extreme Gradient Boosting approach to estimate the shear strength of FRP reinforced concrete beams

Although fiber-reinforced polymer (FRP) bars have gained much attention recently, their material characteristics, particularly the elastic modulus, are markedly different from conventional reinforcement. This explains why classical calculation models cannot correctly capture the behaviors of FRP-reinforced concrete (RC) beams in structural engineering. As a novel approach, this study aims to construct a machine learning (ML) model for predicting the shear strength (SS) of FRP-RC beams with and without stirrups. An Extreme Gradient Boosting (XGB) model is selected thanks to its effectiveness and robustness in tackling many complicated problems. An experimental database encompassing 453 examples is utilized, with input variables covering the FRP-RC beams’ geometry, the mechanical properties of concrete, and two reinforcement components. The selection of hyperparameters of XGB model is first conducted, followed by a prediction performance assessment using common statistical measures. Additionally, this study conducts feature importance analysis and employs SHAP values technique to discover the inputs versus output relationship, as well as to identify the most significant features affecting the SS of FRP-RC beams. The outcome of this research, which lies in developing a reliable and accurate ML model that can handle both FRP-RC beams with and without stirrups, may favor the application of ML models to various real-world engineering problems.

Real-world engineering problems: Two surrogate methods for robust vibration control of moving mass-beam coupling systems with epistemic uncertainty

The dynamic responses and the vibration control of the moving mass-beam coupling systems are one of the real-world problems in engineering fields including vehicle-bridge coupling systems and missile-gun systems. There are many uncertainty factors in the real-world engineering applications. Analyzing the effects of uncertainty on the state trajectory of the system is significant to realize the safe operation of the system. This paper develops two surrogate methods to research the evidence-theory-based robust adaptive fuzzy sliding mode control (ET-RAFSMC) of the moving mass-beam coupling systems. For the first approach, the active learning Kriging (ALK) is combined with the ET-RAFSMC to perform the evidence response analysis through an interval Monte Carlo simulation, and then a Karush-Kuhn-Tucker condition is utilized to alleviate the burden of searching the extreme responses. The other surrogate method is the Lobatto polynomial function, which integrates with a spare sampling method for improving the computational efficiency and accuracy when running the extreme value analysis of the ET-RAFSMC. The accuracy and the efficiency of the proposed methods are studied by comparing with Monte Carlo simulations as well as Legendre expansion model. Finally, the performance of the ET-RAFSMC is studied through comparing with the traditional sliding mode control.

WOASCALF: A new hybrid whale optimization algorithm based on sine cosine algorithm and levy flight to solve global optimization problems

In recent years, researchers have been focused on solving optimization problems in order to determine the global optimum. Increasing the dimension of a problem increases its computational cost and complexity as well. In order to solve these types of problems, metaheuristic algorithms are used. The whale optimization algorithm (WOA) is one of the most well-known algorithms based on whale hunting behavior. In this paper, the WOA algorithm is combined with the Sine Cosine Algorithm (SCA), which is based on the principle of trigonometric sine-cosine. The WOA algorithm has superior performance in the exploration phase in contrast with the exploitation phase, whereas the SCA algorithm has weaknesses in the exploitation phase. The levy flight distribution has been used in the hybrid WOA and SCA algorithm to improve these deficiencies. This study introduced a novel hybrid algorithm named WOASCALF. In this algorithm, the search agents' position updates are based on a hybridization of the WOA, SCA, and levy flight. Each of these metaheuristic algorithms has reasonable performance, however, the Levy distribution caused small and large distance leaps in each phase of the algorithm. Thus, it is possible for the appropriate search agent to move in different directions of the search space. The performance of the WOASCALF has been evaluated by the 23 well-known benchmark functions and three real-world engineering problems. The result analysis demonstrates that the exploration ability of WOASCALF has strong superiority over other compared algorithms.

An improved beetle antennae search algorithm with Lévy flight and its application in micro-laser assisted turning

The beetle antennae search (BAS) algorithm is a single-solution metaheuristic optimizer that imitates the foraging behavior of beetles. Due to its easy implementation and fast convergence, it has been applied in various engineering fields. To enhance the local search ability and preclude the blindness of position updates performed in BAS, an improved beetle antennae search algorithm with Lévy flight (IBAS) is proposed in this paper. Additionally, to further enhance the algorithm performance, the features of inertia, normalization, and elitist selection are adopted into this proposed hybrid algorithm. In a comparison with other algorithms based on 15 well-known benchmark functions and 4 classic engineering problems, IBAS maintains good performance under the premise of a trade-off between efficacy and efficiency. The detailed results suggest that the improvements adapted in IBAS alleviate the shortcomings of BAS. In addition, a real-world engineering problem, laser energy prediction in micro-laser assisted turning, is used to further validate the potential of the proposed algorithm for industrial applications. The results indicate the feasibility of the new solution based on the IBAS algorithm. Two further applications of IBAS in the reconstruction of the temperature field and online laser energy detection are also discussed.

A new firefly algorithm with improved global exploration and convergence with application to engineering optimization

Firefly algorithm (FA) is a powerful and efficient meta-heuristic algorithm which has shown effective performance in the recent literature when applied to solving engineering optimization problems. FA imitates the flashing behavior of fireflies. FA generates solutions randomly and assumes them as fireflies. However, these algorithms may suffer from premature convergence and poor global exploration when used to optimize complex and high dimension engineering problems. Therefore, this study has proposed a novel FA, called firefly algorithm 1 to 3 (FA1→3), via different types of movements of fireflies in an attempt to improve the global exploration and convergence characteristics of FA. A comprehensive study has been carried out on the CEC2014 test functions to compare FA1→3 with the standard FA and several modern improved FA algorithms to validate its performance. The experimental results demonstrate that FA1→3 has achieved acceptable performance. In addition, it has been applied to six real-world engineering problems to show the optimization capability, robustness, and efficacy of FA1→3 in comparison with modern algorithms As per simulations, FA1→3 has provided suitable performance and higher accuracy than traditional and modified algorithms introduced in the last years. According to simulations, FA1→3 is significantly powerful and robust when dealing with various complex engineering problems and finds the design variables straightforwardly. Note that the source code of the proposed FA1→3 algorithm is publicly available at https://www.optim-app.com/projects/FA.