Engineering Design Optimization Research Articles

Automatic Identification of Rock Discontinuity Sets by a Fuzzy C-Means Clustering Method Based on Artificial Bee Colony Algorithm

The identification and classification of rock discontinuities are crucial for studying rock mechanical properties and rock engineering optimization design and safety assessment. An improved artificial bee colony (ABC) algorithm is proposed and combined with the fuzzy C-means (FCM) clustering method to develop an FCM clustering method for automatically identifying rock discontinuity sets based on the ABC algorithm (FCM-ABC method). All the equations of the method are fully developed, and the methodology is presented in its entirety. Moreover, the rock structural planes are investigated in a gold mine in China using a ShapeMetriX 3D system. Based on the measured structural plane data, the specific calculation process, selection of parameters, effectiveness of grouping, and the dominant orientation of the proposed method for structural plane occurrence classification are analyzed and discussed, and satisfactory clustering results are achieved. This validates the validity and reliability of the method. Furthermore, multiple aspects of the excellent performance of this method for the identification of structural plane sets compared to traditional clustering methods are demonstrated. In addition, the significance of structural plane identification in the prevention and control of rock engineering disasters is discussed. This new method theoretically expands the technology of rock mass structural plane identification and has important application value in practical engineering.

Multi-Objective Hybrid Sailfish Optimization Algorithm for Planetary Gearbox and Mechanical Engineering Design Optimization Problems

Multi-Objective Hybrid Sailfish Optimization Algorithm for Planetary Gearbox and Mechanical Engineering Design Optimization Problems

Fuzzy Neural Network Learning for Practical Intelligent Powertrain Exhaust Gas Temperature Prediction

The exhaust gas temperature plays a leading role in the thermal efficiency of the automotive powertrain system. It also determines the performance of catalytic converters for removing toxic gases. When the exhaust temperature is excessively high, it will give rise to extra heat energy loss in the exhaust systems. Meanwhile overheating leads to engine component damage, engine knock, fuel pre-ignition and emission after-treatment system failure. The inherent formation mechanism of exhaust temperature is highly complicated, which depends on multiple factors in the combustion process as well as heat and mass transfer. The maximum combustion efficiency itself requires the stoichiometric mixture (air to fuel ratio equal to 14.7). Two parameters with dominant impact on exhaust gas temperature are engine speed and engine load. Some typical delays occur in engine operations as well due to fuel atomization, air and fuel mixing, vaporization, heat transfer, and so on. It is essential to operate at the optimal exhaust temperature to maximize the performance to cost ratio and to avoid severe damage. Ambiguity and uncertainty are inevitable, which gives rise to high complexity in modeling and prediction of the exhaust gas temperature. The goal of this research instead is to design a feasible and applicable simple exhaust gas temperature model for potential optimal engine design. The intelligent hybrid fuzzy neural network learning is proposed to model the exhaust gas temperature in the powertrain system with high nonlinearity, high complexity and high uncertainty. The fuzzy system is introduced to deal with the uncertainty and ambiguity through fuzzy sets, covering fuzzification, inference engine and defuzzification subsystems. Hybridization is made when artificial neural networks involve in together with the fuzzy system. In this case, training, learning, predication and validation of the hybrid fuzzy neural network powertrain exhaust gas temperature model can be implemented. This simple model can be accomplished with ease, which can also be extended to the exhaust gas temperature model prediction across arbitrary types of automotive engines.

Improving Fuel Consumption Prediction for Marine Diesel Engines Using Hierarchical Neural Networks and Pulsating Exhaust Models

Predicting end-of-process variables in internal combustion engines, such as brake-specific fuel consumption or pollutant emissions, is crucial for engine design decisions. However, errors in common multi-layer-perceptron-based artificial neural network models often match the magnitude of the expected fuel consumption improvements, potentially leading to incorrect decisions. This study introduces a hybrid model where artificial neural networks replace engine block elements, while the 1D gas circuit and turbocharger models are retained. To enhance metamodel accuracy, two modifications are proposed: incorporating a pulsating mass flow rate in the exhaust line to capture pulsating effects missing in mean-value engine models and using a hierarchical arrangement of several multi-layer perceptrons instead of a parallel arrangement. The pulsating mass flow rate approach improves the accuracy of all tested configurations by replicating pulsating effects from a detailed 1D engine model. Meanwhile, the hierarchical arrangement refines predictions of end-of-process variables, such as fuel consumption, by increasing the total layers, with a minimal trade-off in the accuracy of other variables. These findings are validated using a metamodel derived from a calibrated 1D engine model in GT-Suite. The proposed methods are expected to enhance the accuracy of data-driven artificial neural network approaches, contributing to more reliable engine design optimization.

A Novel Slime Mould Multiverse Algorithm for Global Optimization and Mechanical Engineering Design Problems

The slime mould optimization algorithm (SMA) is one of the well-established optimization algorithms with a superior performance in a variety of real-life optimization problems. The SMA has certain limitations that reduce the diversity and accuracy of solutions, raising the risk of premature convergence with an inadequate balance between its exploitation and exploration phases. In this study, a novel hybrid slime mould multi-verse algorithm (SMMVA) is proposed to improve the performance of SMA algorithm. The SMA and multi-verse optimization (MVO) algorithm hybrid is introduced while updating variation parameter through novel nonlinear convergence factor. The proposed algorithm balances the ability of the SMA algorithm to explore and exploit, boosts the global exploration capability and improves the accuracy, stability, and convergence speed. The performance of SMMVA algorithm is compared with 16 well-established and recently-published metaheuristic algorithms on 23 standard benchmark functions, CEC2017, CEC2022 test functions, five engineering design problems, and five UCI repository datasets. The statistical tests such as Friedman’s test, box plot comparison and Wilcoxon rank sum test are employed to verify the SMMVA’s stability and statistical superiority. The algorithm was tested on total 64 benchmark functions, achieving an overall success rate of 68.75% across 30 runs compared to the other counterparts. The results for the feature selection problem show that the proposed algorithm with k-nearest neighbour (KNN) classifier obtained more informative features with higher accuracy values. Thus, the proposed SMMVA algorithm is proven to perform excellent performance in solving optimization problems with better solution accuracy and promising prospect.

A multi-strategy enhanced reptile search algorithm for global optimization and engineering optimization design problems

The reptile search algorithm (RSA) is a well-known swarm-based metaheuristic algorithm inspired by the hunting behaviors of crocodiles. To overcome the problems of falling into local optima and premature convergence, this paper proposes a multi-strategy enhanced reptile search algorithm (MRSA), which integrates a novel dynamic evolutionary sense, prey approaching strategy and Cauchy mutation strategy. The prey approaching strategy comes from the secretary bird optimization algorithm and is applied to strengthen the exploration capability of RSA. A comparative performance analysis is conducted using the CEC2005, CEC2017 and CEC2022 benchmark functions. And fifteen algorithms are employed for the performance comparison. The results of numerical, convergence curves, boxplots, Wilcoxon rank-sum test and Friedman ranking confirm the efficacy and stability of proposed MRSA, indicating its superior performance compared to other algorithms. Moreover, seven practical engineering design tasks are used to test the performance of MRSA in real-world optimization problems. The results also show that MRSA can efficiently obtain better optimal solution compared to existing methods.

Exploring Topology Optimization in Geotechnical Engineering

Despite recent advancements in utilizing topology optimization in geotechnical engineering design, practical applications primarily focus on simple foundation design. In the view of this, this study aims to explore the application of topology optimization methodology to various geotechnical design problems. The optimization takes place in a finite portion of the soil, modeled via Finite Element Method (FEM) which is coupled with the rest of the semi-infinite soil, modeled via Indirect Boundary Element Method (IBEM). The optimization is carried out considering a linear bi-material interpolation through the three field Floating Point Topology Optimization (FPTO) method. Numerical results for the optimized foundations of plates, bridges and linings are presented and discussed.

Integrating metaheuristic and machine learning for optimization of a full-scale transmission line tower

In long transmission lines (TLs), the design of a transmission line tower (TLT) can be replicated multiple times, a contrast to most structures that feature a unique design. Various optimization methods have been developed to reduce the overall mass of these structures. Historically, most research has relied on metaheuristic algorithms to identify optimal solutions. More recently, machine learning (ML) techniques have begun to be integrated with metaheuristics to speed up the optimization process, although ML has primarily been applied to smaller TLTs, in academic settings, or to simplify structural analysis, potentially compromising accuracy. This paper introduces a new approach that combines the Backtracking Search Algorithm (BSA), known for its efficacy in similar real-world TLT optimization challenges, with Kriging-based Efficient Global Optimization (EGO). This methodology starts by optimizing the size using BSA and subsequently employs EGO to refine the shape. This dual-step optimization effectively significantly reduces the mass of the TLT with only a few hundred additional objective function evaluations (OFEs). In comparison, simultaneous size and shape optimization using BSA alone requires over one hundred thousand additional OFEs to achieve comparable results, showing the potential of the proposed approach in dealing with expensive engineering design optimization problems.

Employing topology optimization method to create optimum telecommunication tower design structure

Recently, the employment of topology optimization (TO) in structural engineering design has gained a significant structural performance, also, TO is employed by designers for developing aesthetically and efficient buildings. In this work, TO is employed to design novel and rigged structures of communication towers. The way that the TO algorithm works is to intelligently create the 3D model by preserving architectural features with tradeoffs between the stiffness and weight ratio. The present work focuses on the investigation of creating optimal self-supported communication towers. Results concerning the prime observation of optimization analyses and the potential benefits of TO in designing telecommunication tower lattices are drawn.

The critical role of c and φ in ensuring stability: A study on rockfill dams

Abstract Slope stability analysis is an important part of rockfill dam design, and the uncertainty of rock and soil physical and mechanical parameters has a significant impact on slope stability. In this article, based on physical and mechanical parameters c and φ, simplified Bishop’s method and mean clustering method are adopted to study the influence of parameter cohesion c and internal friction Angle φ on slope safety factor k of composite geomembrane rockfill dam, and the key and non-critical areas of the dam are preliminarily differentiated according to the different influences of the changes in different sections of the dam body on slope safety factor c and φ. The research results show that whether c and φ change ±5 and ±10% simultaneously with single parameter or double parameter, it shows that cohesion force c has little influence on slope stability, while internal friction Angle φ is the most sensitive factor in slope stability calculation, and its numerical accuracy has a great influence on the calculation result of safety factor. In addition, the influence of c and φ in key areas on the stability of the dam body is more significant. Therefore, in the construction of a composite geofilm rockfill dam, a relatively accurate φ value is required when selecting parameters, especially in key areas. This study not only has a certain guiding significance for the property requirements of the selected soil, engineering safety, and engineering optimization design, but also puts forward some new methods and ideas for optimizing the design scheme and improving the safety of the dam.

Evolving Novel Gene Regulatory Networks for Structural Engineering Designs.

Engineering design optimization poses a significant challenge, usually requiring human expertise to discover superior solutions. Although various search techniques have been employed to generate diverse designs, their effectiveness is often limited by problem-specific parameter tuning, making them less generalizable and scalable. This article introduces a framework inspired by evolutionary and developmental (evo-devo) concepts, aiming to automate the evolution of structural engineering designs. In biological systems, evo-devo governs the growth of single-cell organisms into multicellular organisms through the use of gene regulatory networks (GRNs). GRNs are inherently complex and highly nonlinear, and this article explores the use of neural networks and genetic programming as artificial representations of GRNs to emulate such behaviors. To evolve a wide range of Pareto fronts for artificial GRNs, this article introduces a new technique, a real value-encoded neuroevolutionary method termed real-encoded NEAT (RNEAT). The performance of RNEAT is compared with that of two well-known evolutionary search techniques across different 2-D and 3-D problems. The experimental results demonstrate two key findings. First, the proposed framework effectively generates a population of GRNs that can produce diverse structures for both 2-D and 3-D problems. Second, the proposed RNEAT algorithm outperforms its competitors on more than 50% of the problems examined. These results validate the proof of concept underlying the proposed evo-devo-based engineering design evolution.

Nature-inspired metaheuristic optimization algorithms for FDTD dispersion modeling

Optimization algorithms have been employed for a variety of applications such as engineering design optimization, machine learning, control systems, computer science and software engineering. Among various optimization approaches, nature-inspired metaheuristic optimization algorithms excel in addressing complex optimization problems by considering various constraints and optimizing a wide array of variables and target functions. In finite-difference time-domain (FDTD) methods for complex dispersive media, it is crucial to derive accurate dispersion model parameters that satisfy the numerical stability conditions by applying an optimization algorithm. In this work, we apply five representative nature-inspired metaheuristic optimization algorithms to extract accurate and numerically stable dispersion modeling parameters: continuous genetic algorithm, particle swarm optimization (PSO), artificial bee colony, grey wolf optimization, and coyote optimization algorithm. To achieve a comprehensive analysis, this study examines the FDTD dispersion modeling for various materials across different frequency ranges. The numerical examples illustrate that PSO excels at extracting numerically stable and highly accurate parameters for the FDTD dispersion model.

Boosting Engineering Optimization With a Novel Recursive Transfer Bifidelity Surrogate Modeling

Abstract In the engineering optimization, there often exist the multiple sources of information with different fidelity levels. In general, low-fidelity (LF) information is usually more accessible than high-fidelity (HF) information, while the latter is usually more accurate than the former. Thus, to capitalize on the advantages of this information, this study proposes a novel recursive transfer bifidelity surrogate modeling to fuse information from HF and LF levels. First, the selection method of optimal scale factor is proposed for constructing bifidelity surrogate model. Then, a recursive method is developed to further improve its performance. The efficacy of the proposed model is comprehensively evaluated using numerical problems and an engineering example. Comparative analysis with some surrogate models (five multifidelity and a single-fidelity surrogate models) demonstrates the superior prediction accuracy and robustness of the proposed model. Additionally, the impact of varying cost ratios and combinations of HF and LF samples on the performance of the proposed model is also investigated, yielding consistent results. Overall, the proposed model has superior performance and holds potential for practical applications in engineering design optimization problems.

An enhanced slime mould algorithm with triple strategy for engineering design optimization

Abstract This paper introduces an enhanced slime mould algorithm (EESMA) designed to address critical challenges in engineering design optimization. The EESMA integrates three novel strategies: the Laplace logistic sine map technique, the adaptive t-distribution elite mutation mechanism, and the ranking-based dynamic learning strategy. These enhancements collectively improve the algorithm’s search efficiency, mitigate convergence to local optima, and bolster robustness in complex optimization tasks. The proposed EESMA demonstrates significant advantages over many conventional optimization algorithms and performs on par with, or even surpasses, several advanced algorithms in benchmark tests. Its superior performance is validated through extensive evaluations on diverse test sets, including IEEE CEC2014, IEEE CEC2020, and IEEE CEC2022, and its successful application in six distinct engineering problems. Notably, EESMA excels in solving economic load dispatch problems, highlighting its capability to tackle challenging optimization scenarios. The results affirm that EESMA is a competitive and effective tool for addressing complex optimization issues, showcasing its potential for widespread application in engineering and beyond.

Multi-Objective Majority–Minority Cellular Automata Algorithm for Global and Engineering Design Optimization

When dealing with complex models in real situations, many optimization problems require the use of more than one objective function to adequately represent the relevant characteristics of the system under consideration. Multi-objective optimization algorithms that can deal with several objective functions are necessary in order to obtain reasonable results within an adequate processing time. This paper presents the multi-objective version of a recent metaheuristic algorithm that optimizes a single objective function, known as the Majority–minority Cellular Automata Algorithm (MmCAA), inspired by cellular automata operations. The algorithm presented here is known as the Multi-objective Majority–minority Cellular Automata Algorithm (MOMmCAA). The MOMmCAA adds repository management and multi-objective search space density control to complement the performance of the MmCAA and make it capable of optimizing multi-objective problems. To evaluate the performance of the MOMmCAA, results on benchmark test sets (DTLZ, quadratic, and CEC-2020) and real-world engineering design problems were compared against other multi-objective algorithms recognized for their performance (MOLAPO, GS, MOPSO, NSGA-II, and MNMA). The results obtained in this work show that the MOMmCA achieves comparable performance with the other metaheuristic methods, demonstrating its competitiveness for use in multi-objective problems. The MOMmCAA was implemented in MATLAB and its source code can be consulted in GitHub.

Two linear energy stable lumped mass finite element schemes for the viscous Cahn–Hilliard equation on curved surfaces in 3D

The evolution of a dynamic system on complex curved 3D surfaces is essential for the understanding of natural phenomena, the development of new materials, and engineering design optimization. In this work, we study the viscous Cahn–Hilliard equation on curved surfaces and develop two linear energy stable finite element schemes based on the lumped mass method. Two stabilizing terms are added to ensure both the unique solvability and unconditional energy stability. We prove rigorously that two schemes are unconditionally energy stable . Numerical experiments are presented to verify theoretical results and to show the robustness and accuracy of the proposed method.

An improved Coati Optimization Algorithm with multiple strategies for engineering design optimization problems

Aiming at the problems of insufficient ability of artificial COA in the late optimization search period, loss of population diversity, easy to fall into local extreme value, resulting in slow convergence and lack of exploration ability; In this paper, an improved COA algorithm based on chaotic sequence, nonlinear inertia weight, adaptive T-distribution variation strategy and alert updating strategy is proposed to enhance the performance of COA (shorted as TNTWCOA). The algorithm introduces chaotic sequence mechanism to initialize the position. The position distribution of the initial solution is more uniform, the high quality initial solution is generated, the population richness is increased, and the problem of poor quality and uneven initial solution of the Coati Optimization Algorithm is solved. In exploration phase, the nonlinear inertial weight factor is introduced to coordinate the local optimization ability and global search ability of the algorithm. In the exploitation phase, adaptive T-distribution variation is introduced to increase the diversity of individual population under low fitness value and improve the ability of the algorithm to jump out of the local optimal value. At the same time, the alert update mechanism is proposed to improve the alert ability of COA algorithm, so that it can search within the optional range. When Coati is aware of the danger, Coati on the edge of the population will quickly move to the safe area to obtain a better position, while Coati in the middle of the population will randomly move to get closer to other Coatis. IEEE CEC2017 with 29 classic test functions were used to evaluate the convergence speed, convergence accuracy and other indicators of TNTWCOA algorithm. Meanwhile, TNTWCOA was used to verify 4 engineering design optimization problems, such as pressure vessel optimization design and welding beam design. The results of IEEE CEC2017 and engineering design Optimization problems are compared with Improved Coati Optimization Algorithm (ICOA), Coati Optimization Algorithm (COA), Golden Jackal Optimization Algorithm (GJO), Osprey Optimization Algorithm (OOA), Sand Cat Swarm Optimization Algorithm (SCSO), Subtraction-Average-Based Optimizer (SABO). The experimental results show that the improved TNTWCOA algorithm significantly improves the convergence speed and optimization accuracy, and has good robustness. Three‑bar truss design problem, The Gear Train Design Problem, Speed reducer design problem shows a strong solution advantage. The superior optimization ability and engineering practicability of TNTWCOA algorithm are verified.

Recent Advances in Reconfigurable Electromagnetic Surfaces: Engineering Design, Full-Wave Analysis, and Large-Scale Optimization

Recent Advances in Reconfigurable Electromagnetic Surfaces: Engineering Design, Full-Wave Analysis, and Large-Scale Optimization

Dynamic modeling and analysis of compressed air energy storage for multi-scenario regulation requirements

Compressed air energy storage (CAES) technology has received widespread attention due to its advantages of large scale, low cost and less pollution. However, only mechanical and thermal dynamics are considered in the current dynamic models of the CAES system. The modeling approaches are relatively homogeneous. CAES power stations have gradually increased the demand for auxiliary services such as frequency modulation mode and voltage regulation mode in addition to the peak regulation mode. Therefore, it is urgent to pay attention to the dynamic response characteristics at shorter time scales. Due to the lack of an accurate and complete dynamic model of the CAES system for multi-scenario adaptation requirements, the development of system optimization design and control technology has been severely limited. The paper establishes a dynamic model of advanced adiabatic compressed air energy storage (AA-CAES) considering multi-timescale dynamic characteristics, interaction of variable operating conditions and multivariate coordinated control. The simulation data is compared with the measured data of the peak regulation, frequency regulation and voltage regulation scenarios of the Jintan Salt Cave CAES (JTSC-CAES). The results show that the deviation of the dynamic model is <7 %, which verifies the validity and accuracy of the proposed AA-CAES dynamic model. The established model and research results provide the theoretical model foundation for dynamic characteristic research, system design and optimization control of the JTSC-CAES.

Optical engineering perspectives on fractional analysis: A comprehensive study of the conformable Gross–Pitaevskii equation in the Bose–Einstein condensation

In this study, we examine the dynamic behavior of optical solitons in the nonlinear conformable Gross–Pitaevskii equation (GPE) within Bose–Einstein condensates, which refer to the phenomenon of many ultra-cold bosonic particles occupying a single quantum state. The GPE is a cornerstone of advanced statistical physics, has application in various engineering disciplines, particularly in the realm of quantum mechanics and optical engineering. By precisely encoding the GPE’s steady-state solutions, we make it possible to compute nonlinear models pertinent to engineering systems and clarify solutions within particular parameter regimes that are essential for engineering applications. Our study encompasses repulsive and attractive nonlinearities in GPE, including quadratic and double-well potentials. We focus on discovering solutions, employing the generalized Riccati equation mapping method and modified extended tanh-function method to extract soliton solutions. The study yields a variety of soliton including combined dark–bright, singular, combo dark-singular, periodic-singular, and rational solutions with physical perspectives. Through 2D and 3D representations, the internal structure of these phenomena is effectively illustrated, offering insights into their behavior and dynamics, which is significant for optimization process and engineering design.