Classical Engineering Research Articles

A Multimodal Multi-Objective Coati Optimization Algorithm Based on Spectral Clustering

Multimodal multi-objective problems refer to situations where more than one Pareto set in the decision space corresponds to the same Pareto front in the objective space. In this paper, we propose a multimodal multi-objective coati optimization algorithm based on spectral clustering (MMOCOA-SC) for use in multimodal multi-objective problems. The algorithm starts by creating stable subpopulations via a spectral clustering method, grouping similar individuals within each subpopulation. Then, an improved coati optimization algorithm is proposed in order to increase the diversity of the population and continuously search for near-Pareto optimal solutions. Finally, we use an improved crowding distance method combined with a non-dominated sorting method to maintain and retain multiple near-Pareto optimal solutions. The MMOCOA-SC is evaluated alongside five state-of-the-art algorithms using the 2020 CEC test suite, IDMP test problems, and twelve classic engineering application problems. In comparison to the other five state-of-the-art algorithms, the experimental results show that MMOCOA-SC exhibits a superior performance when applied to the problems. This study explores multimodal multi-objective optimization algorithms with a focus on the concept of symmetry, which is crucial for multimodal multi-objective optimization problems in terms of achieving a balanced decision space and a diversity of solutions in the objective space.

Q-learning improved golden jackal optimization algorithm and its application to reliability optimization of hydraulic system

To endow the prey with intelligent movement behavior and improve the performance of Golden Jackal Optimization (GJO), a Q-learning Improved Gold Jackal Optimization (QIGJO) algorithm is proposed. This paper introduces five update mechanisms and proposes double-population Q-learning collaborative mechanism to select appropriate update mechanisms to improve GJO performance. Additionally, a new convergence factor is incorporated to enhance convergence capability of GJO. QIGJO demonstrates excellent performance across 23 benchmark functions, CEC2022, and three classical engineering design problems, indicating high convergence accuracy and significantly enhanced global exploration capability. The reliability optimization model of the hydraulic system for concrete pump trucks was established based on a Continuous-time Multi-dimensional T-S dynamic Fault Tree (CM-TSdFT), considering the two-dimensional factors of operating time and number of impacts. Utilizing QIGJO to optimize this model yielded excellent results, providing valuable methodological support for reliability optimization of hydraulic systems.

Reinforcement learning-based alpha-list iterated greedy for production scheduling

Metaheuristics can benefit from analyzing patterns and regularities in data to perform more effective searches in the solution space. In line with the emerging trend in the optimization literature, this study introduces the Reinforcement-learning-based Alpha-List Iterated Greedy (RAIG) algorithm to contribute to the advances in machine learning-based optimization, notably for solving combinatorial problems. RAIG uses an N-List mechanism for solution initialization and its solution improvement procedure is enhanced by Reinforcement Learning and an Alpha-List mechanism for more effective searches. A classic engineering optimization problem, the Permutation Flowshop Scheduling Problem (PFSP), is considered for numerical experiments to evaluate RAIG's performance. Highly competitive solutions to the classic scheduling problem are identified, with up to 9% improvement compared to the baseline, when solving large-size instances. Experimental results also show that the RAIG algorithm performs more robustly than the baseline algorithm. Statistical tests confirm that RAIG is superior and hence can be introduced as a strong benchmark for future studies.

An enhanced Moth-Flame optimizer with quality enhancement and directional crossover: optimizing classic engineering problems

As a popular meta-heuristic algorithm, the Moth-Flame Optimization (MFO) algorithm has garnered significant interest owing to its high flexibility and straightforward implementation. However, when addressing engineering constraint problems with specific parameters, MFO also exhibits limitations such as fast convergence and a tendency to converge to local optima. In order to address these challenges, this paper introduces an enhanced version of the MFO, EQDXMFO. EQDXMFO integrates a Quality Enhancement (EQ) strategy and a Directional Crossover (DX) mechanism, fortifying the algorithm’s search dynamics. Specifically, the DX mechanism is designed to augment the population’s diversity, enhancing the algorithm’s exploratory potential. Concurrently, the EQ strategy is employed to elevate the solution quality, which in turn refines the convergence precision of the algorithm. To verify the effectiveness of EQDXMFO, experiments are carried out on the test set of the IEEE CEC2017. A total of 5 classical algorithms, five excellent MFO variants, and seven state-of-the-art algorithms are selected for comparison, which confirm the significant advantages of EQDXMFO. Next, EQDXMFO is applied to five complex engineering constraint problems, demonstrating that EQDXMFO can optimize realistic problems. The comprehensive analysis shows that EQDXMFO has strong optimization capabilities and provides methods for research on other complex real-world problems.

PMSOMA: optical microscope algorithm based on piecewise linear chaotic mapping and sparse adaptive exploration

The optical microscope algorithm (OMA) is a metaheuristic algorithm that draws inspiration from the magnifying functionality of optical microscopes. This study introduces an enhanced variant of OMA, termed PMSOMA, designed to mitigate the original version's limitations, notably its slow convergence rates and vulnerability to local optima. PMSOMA integrates a piecewise linear chaotic map to refine population initialization and augment diversity, alongside a sparse adaptive exploration mechanism to bolster search efficacy. The performance of PMSOMA was rigorously tested using a suite of 50 benchmark functions, the CEC2017 test suite, feature selection datasets, and three classical engineering challenges. The empirical findings confirm that PMSOMA surpasses both the original OMA and competing algorithms by delivering superior solutions, accelerating convergence, and demonstrating enhanced robustness in convergence.

Assessment of Multifidelity Surrogate Approaches for Expedient Loads Prediction in High-Speed Flows

Fast and accurate predictions about the flowfield surrounding a hypersonic flight vehicle are necessary for both early design phases and autonomous vehicle control. Data-driven model reduction presents an opportunity to harness the accuracy of high-fidelity techniques for expedient online application. Surrogate modeling is one approach that seeks to emulate the response of a system with evaluation times much faster than the data source. Multifidelity surrogate modeling seeks to reduce the amount of high-fidelity data needed for a sufficiently accurate online model. The objective of this paper is to assesses the tradeoffs between a kriging-based multifidelity model and a neural-network-based multifidelity model, applied to high-speed flow past a canonical flight geometry. All models studied in this work reproduce the results of the benchmark simulations with less than 100 training cases. The kriging models marginally outperform the neural-network-based models in accuracy, but requirements for training and storing the kriging models scale exponentially as the training data gets larger. Kriging models offer built-in uncertainty quantification and require significantly less decision making about hyperparameters and architecture choices. This study also indicates that the inclusion of data from classical engineering methods can improve the prediction accuracy of surrogate models at practically no cost.

Transgressive phenotypes from outbreeding between the Trichoderma reesei hyper producer RutC30 and a natural isolate.

The filamentous fungus Trichoderma reesei produces cellulolytic enzymes that are essential for the hydrolysis of lignocellulosic biomass into monomerics sugars. The filamentous fungus T. reesei produces cellulolytic enzymes that are essential for the hydrolysis of lignocellulosic biomass into monomerics sugars, which can in turn be fermented to produce second-generation biofuels and bioproducts. Production performance improvement, which is essential to reduce production cost, relies on classical mutagenesis and genetic engineering techniques. Although sexual reproduction is a powerful tool for improving domesticated species, it is often difficult to apply to industrial fungi since most of them are considered asexual. In this study, we demonstrated that outbreeding is an efficient strategy to optimize T. reesei. Crossing between a natural isolate and a mutagenized strain generated a biodiverse progeny with some offspring displaying transgressive phenotype for cellulase activities.

An improved multi-island genetic algorithm and its utilization in the optimal design of a micropositioning stage

An improved multi-island genetic algorithm (IMIGA) by integrating various common improvement methods, optimized through D-optimal and analysis of variance (ANOVA) techniques. IMIGA’s performance is validated against various state-of-the-art algorithms across CEC2020 test suite and classic engineering problems, affirming its superiority. IMIGA’s potential in industrial applications is demonstrated by employing it for optimal design in a micropositioning stage, leveraging machine learning (ML) for parameter optimization. With the optimal design parameters, driven by IMIGA and a surrogate ML model, the proposed stage attained a first natural frequency of 55 Hz and maximum strokes of 266.7 μm, 110.9 μm, and 0.7552 mrad in the x-, y-, and θ-directions, respectively. These experimental results closely align with the predicted outcomes from the design phase, highlighting the accuracy and reliability of the employed methodology. Such consistency between experimental measurements and predicted values underscores the efficacy of the design approach in achieving desired performance metrics.

Computer‐Aided Design of Large‐Scale Nanomaterials Synthesis Processes: A Detailed Review

AbstractEfforts from the scientific community and the private sector are required to lower the costs of large‐scale production of nanomaterials (NMs) to enhance their commercialization. In this work, the computer‐aided process design of large‐scale NM synthesis procedures is comprehensively reviewed. Moreover, a generalized process flow diagram for all large‐scale production processes was constructed by surveying numerous scalable experimental procedures reported in the literature. Previous studies reporting simulation cases of large‐scale production processes of NMs and nanocomposites (NCs) are also reviewed based on the type of material produced, e.g., oxides, sulfides, carbonaceous materials, organic materials, metals, and other types of NMs. Finally, technical insights from classical chemical engineering specializations, such as altering process configurations, optimizing process variables, integrating chemical processes, utilizing renewable energy sources, conducting computational calculations, employing machine learning techniques, and studying the process's environmental impact, are reviewed for large‐scale NMs and NCs synthesis.

A two-scale approach for assessing the role of defects in fatigue crack nucleation in metallic structures

Metal structures often exhibit macroscopic defects from which cracks can nucleate during cyclic loading. The current work presents a two-scale approach to enable the prediction of crack nucleation from such defects by taking into account local microstructure features. The geometrical description of the defect and associated non-homogeneous strain fields are modeled using a macroscale model which employs a continuum elastoplastic material model for cyclic deformation. The cyclic deformation of the microstructure near the defect is modeled using a mesoscale model which employs a crystal plasticity material model and uses multiple realizations to address the statistical microstructure variability. The boundary conditions of the mesoscale model are extracted from the macroscale model. By simulating the deformation of the microstructure using the strain fields near the defect and by introducing a fatigue indicator parameter for crack nucleation, along with the weakest-link based upscaling methodology, the developed approach enables the prediction of the distribution of crack nucleation life. The approach is used for analyzing different defects for crack nucleation by considering local grain orientations. The predictions are shown to not only capture phenomena such as scatter, size effects, etc. qualitatively, but also agree with a classical engineering approach and experimentally reported data sets quantitatively.

Analysing All-Optical Random Bit Sequences Using Gap-Based Approaches.

Quantum mechanical phenomena are revolutionizing classical engineering fields such as signal processing or cryptography. When randomness plays an important role, like in cryptography where random bit sequences guarantee certain levels of security, quantum mechanical phenomena allow new ways of generating random bit sequences. Such sequences have a lot of applications in the communication sector, e.g., regarding data transmission, simulation, sensors or radars, and beyond. They can be generated deterministically (e.g., by using polynomials, resulting in pseudo-random sequences) or in a non-deterministic way (e.g., by using physical noise sources like external devices or sensors, resulting in random sequences). Important characteristics of such binary sequences can be modelled by gap processes in conjunction with the probability theory. Recently, all-optical approaches have attracted a lot of research interest. In this work, an adaptation of the quantum key distribution setup is utilized for generating randomised bit sequences. The simulation results show that all-optically generated sequences very well resemble the theoretically ideal probability density characteristic. Additionally, an experimental optical setup is developed that confirms the simulation results. Furthermore, m-sequences show very promising results as well as Gold sequences. Additionally, the level of burstiness, i.e., the distribution of ones and zeros throughout the sequence, is studied for the different sequences. The results enable the finding that generator polynomials with concentrated non-zero coefficients lead to more bursty bit sequences.

A rhinopithecus swarm optimization algorithm for complex optimization problem

This paper introduces a novel meta-heuristic algorithm named Rhinopithecus Swarm Optimization (RSO) to address optimization problems, particularly those involving high dimensions. The proposed algorithm is inspired by the social behaviors of different groups within the rhinopithecus swarm. RSO categorizes the swarm into mature, adolescent, and infancy individuals. Due to this division of labor, each category of individuals employs unique search methods, including vertical migration, concerted search, and mimicry. To evaluate the effectiveness of RSO, we conducted experiments using the CEC2017 test set and three constrained engineering problems. Each function in the test set was independently executed 36 times. Additionally, we used the Wilcoxon signed-rank test and the Friedman test to analyze the performance of RSO compared to eight well-known optimization algorithms: Dung Beetle Optimizer (DBO), Beluga Whale Optimization (BWO), Salp Swarm Algorithm (SSA), African Vultures Optimization Algorithm (AVOA), Whale Optimization Algorithm (WOA), Atomic Retrospective Learning Bare Bone Particle Swarm Optimization (ARBBPSO), Artificial Gorilla Troops Optimizer (GTO), and Harris Hawks Optimization (HHO). The results indicate that RSO exhibited outstanding performance on the CEC2017 test set for both 30 and 100 dimension. Moreover, RSO ranked first in both dimensions, surpassing the mean rank of the second-ranked algorithms by 7.69% and 42.85%, respectively. Across the three classical engineering design problems, RSO consistently achieves the best results. Overall, it can be concluded that RSO is particularly effective for solving high-dimensional optimization problems.

A Classical Modernization of the V‐Model

AbstractThis paper provides new iconography for the Systems Engineering “V‐Model” or “Vee‐Model.” The “Arch Model” resets and refreshes the original iterative intent of the “V‐Model” in a modern context integrated with digital engineering (DE). We will highlight common misperceptions that reduced the efficacy of the “V‐Model” and explore how a “classical Roman engineering” metaphor can inspire a modern view of systems development based on historically successful, foundational engineering.

Chaotic Artificial Algae Algorithm for Solving Global Optimization With Real-World Space Trajectory Design Problems

AbstractThe artificial algae algorithm (AAA) is a recently introduced metaheuristic algorithm inspired by the behavior and characteristics of microalgae. Like other metaheuristic algorithms, AAA faces challenges such as local optima and premature convergence. Various strategies to address these issues and enhance the performance of the algorithm have been proposed in the literature. These include levy flight, local search, variable search, intelligent search, multi-agent systems, and quantum behaviors. This paper introduces chaos theory as a strategy to improve AAA's performance. Chaotic maps are utilized to effectively balance exploration and exploitation, prevent premature convergence, and avoid local minima. Ten popular chaotic maps are employed to enhance AAA's performance, resulting in the chaotic artificial algae algorithm (CAAA). CAAA's performance is evaluated on thirty benchmark test functions, including unimodal, multimodal, and fixed dimension problems. The algorithm is also tested on three classical engineering problems and eight space trajectory design problems at the European Space Agency. A statistical analysis using the Friedman and Wilcoxon tests confirms that CAA demonstrates successful performance in optimization problems.

A reference architecture for quantum computing as a service

Quantum computers (QCs) aim to disrupt the status-quo of computing – replacing traditional systems and platforms that are driven by digital circuits and modular software – with hardware and software that operate on the principle of quantum mechanics. QCs that rely on quantum mechanics can exploit quantum circuits (i.e., quantum bits for manipulating quantum gates) to achieve ‘quantum computational supremacy’ over traditional, i.e., digital computing systems. Currently, the issues that impede mass-scale adoption of quantum systems are rooted in the fact that building, maintaining, and/or programming QCs is a complex and radically distinct engineering paradigm when compared to the challenges of classical computing and software engineering. Quantum service orientation is seen as a solution that synergises the research on service computing and quantum software engineering (QSE) to allow developers and users to build and utilise quantum software services based on pay-per-shot utility computing model. The pay-per-shot model represents a single execution of instruction on quantum processing unit and it allows vendors (e.g., Amazon Braket) to offer their QC platforms, simulators, and software services to end-users. This research contributes by (i) developing a reference architecture for enabling Quantum Computing as a Service (QCaaS), (ii) implementing microservices with the quantum-classic split pattern as an architectural use-case, and (iii) evaluating the architecture based on practitioners’ feedback. The proposed reference architecture follows a layered software pattern to support the three phases of service lifecycle namely development, deployment, and split of quantum software services. In the QSE context, the research focuses on unifying architectural methods and service-orientation patterns to promote reuse knowledge and best practices to tackle emerging and futuristic challenges of architecting QCaaS.

A physiological model for iohexol plasma clearance supporting diagnostics of kidney function

BackgroundThere are several shortcomings in present methods for estimation of GFR from plasma clearance. The aim of the present study was therefore to develop a physiologically based method for calculation of plasma clearance of iohexol. MethodsA mechanistic model founded on classical biochemical engineering principles where in- and outgoing molecular flows of iohexol between plasma and surrounding tissues were balanced over time. After intravenous injections of iohexol, plasma samples were taken from the investigated subjects until complete elimination of iohexol. After tuning of the model parameters, the clearance value was calculated from the injected dose and the integral of the iohexol concentrations over the investigated period. ResultsThe mass balance model was able to predict the time course of iohexol distribution and elimination after parameterization of mass balance and kinetic equations. Four model structures were evaluated, all based on model parameters derived from published data and from internal tests, each complied at varying physiological conditions. Iohexol clearance was assessed through the model and compared with calculations from previously practiced methods. When testing the mass balance model on ten healthy subjects, clearance was estimated accurately. ConclusionsThe physiological and mechanistic character of the mass balance model may suggest that its derived clearance comes closer to actual in vivo conditions than data derived from previously practiced calculation methods. Although here, only verified with the clearance marker iohexol, the mass balance model should be applicable also to other renal clearance markers.

Blood-sucking leech optimizer

In this paper, a new meta-heuristic optimization algorithm motivated by the foraging behaviour of blood-sucking leeches in rice fields is presented, named Blood-Sucking Leech Optimizer (BSLO). BSLO is modelled by five hunting strategies, which are the exploration of directional leeches, exploitation of directional leeches, switching mechanism of directional leeches, search strategy of directionless leeches, and re-tracking strategy. BSLO and ten comparative meta-heuristic optimization algorithms are used for optimizing twenty-three classical benchmark functions, CEC 2017, and CEC 2019. The strong robustness and optimization efficiency of BSLO are confirmed via four qualitative analyses, two statistical tests and convergence curves. Furthermore, the superiority of BSLO for real-world problems under constraints is demonstrated using five classical engineering problems. Finally, a BSLO-based Artificial Neural Network (ANN) predictive model for diameter prediction of melt electrospinning writing fibre is proposed, which further verifies BSLO's applicability for real-world problems. Therefore, BSLO is a potential optimizer for optimizing various problems. Source codes of BSLO are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/163106-blood-sucking-leech-optimizer.

Constrained Bayesian Optimization with Lower Confidence Bound

In this article, we present a hybrid Bayesian optimization (BO) framework to solve constrained optimization problems by adopting a state-of-the-art acquisition function from the unconstrained BO literature, the well-known lower confidence bound acquisition function and propose a novel variant that analyzes the feasible and infeasible regions which ensure the theoretical convergence guarantee. The proposed variant is compared with the existing state-of-the-art approaches in the constrained BO literature via implementing these approaches on six different problems, including black-box, classical engineering, and hyperparameter tuning problems. Further, we demonstrate the effectiveness of our approach through graphical and statistical testing.

Chaotic opposition Golden Sinus Algorithm for global optimization problems

Optimization techniques are required to find the best solutions to challenging problems in many engineering disciplines. Metaheuristic algorithms that effectively increase search performance using various evolutionary strategies have become increasingly popular in recent years. The Golden Sinus Algorithm (GoldSa) is a population-based optimization algorithm that uses the sine function and the golden ratio. In this study, a chaotically enhanced opposition-based Golden Sinus Algorithm (Co-GoldSa) has been proposed to improve the efficiency of the exploitation and exploration ability of the GoldSa method. While designing this approach, it is first necessary to analyze the chaotic behavior of the GoldSa parameters. For this purpose, three chaotic GoldSa methods have been developed using eight chaotic maps to determine the effect of the chaotic maps on the parameters with different behaviors. Secondly, the opposition-based learning strategy is adjusted to the cGoldSa to enhance the searching ability. To investigate the proficiency of the proposed Co-GoldSa method, it has been examined with well-known and newly introduced metaheuristic approaches on benchmark functions and classical engineering design problems. Besides, an efficient framework of the multilevel thresholding image segmentation has been presented based on the Co-GoldSa method since the efficient processing of pathological images is quite important in medical diagnostics. The experimental outcomes reveal the superiority of the proposed method in solving global optimization problems, image segmentation, and engineering problems. Thus, the outcomes of the benchmark functions, image segmentation, and classical engineering problems support that the proposed Co-GoldSa approach can be considered a promising method for resolving challenging optimization problems.

IHHO: an improved Harris Hawks optimization algorithm for solving engineering problems

Harris Hawks optimization (HHO) algorithm was a powerful metaheuristic algorithm for solving complex problems. However, HHO could easily fall within the local minimum. In this paper, we proposed an improved Harris Hawks optimization (IHHO) algorithm for solving different engineering tasks. The proposed algorithm focused on random location-based habitats during the exploration phase and on strategies 1, 3, and 4 during the exploitation phase. The proposed modified Harris hawks in the wild would change their perch strategy and chasing pattern according to updates in both the exploration and exploitation phases. To avoid being stuck in a local solution, random values were generated using logarithms and exponentials to explore new regions more quickly and locations. To evaluate the performance of the proposed algorithm, IHHO was compared to other five recent algorithms [grey wolf optimization, BAT algorithm, teaching–learning-based optimization, moth-flame optimization, and whale optimization algorithm] as well as three other modifications of HHO (BHHO, LogHHO, and MHHO). These optimizers had been applied to different benchmarks, namely standard benchmarks, CEC2017, CEC2019, CEC2020, and other 52 standard benchmark functions. Moreover, six classical real-world engineering problems were tested against the IHHO to prove the efficiency of the proposed algorithm. The numerical results showed the superiority of the proposed algorithm IHHO against other algorithms, which was proved visually using different convergence curves. Friedman's mean rank statistical test was also inducted to calculate the rank of IHHO against other algorithms. The results of the Friedman test indicated that the proposed algorithm was ranked first as compared to the other algorithms as well as three other modifications of HHO.