Solution Space Research Articles

Optimization of aerodynamic drag reduction for truck trailer model via machine learning

From advanced fairing designs and spoilers to novel airflow management systems, the quest for drag reduction (DR) in truck trailers is critical for cleaner and more sustainable transport. In this study, a particle swarm optimization (PSO) integrated machine learning approach is developed to optimize the DR performance of a bare truck trailer (BTT) with and without half airfoils and roof fairings. The study aims to reach the optimal truck trailer design with fewer experiments by searching the entire solution space with machine learning methods thanks to the developed approach. For this purpose, the experimental setup was initially designed and the experimental results were obtained. The experimental inputs are Airfoil thickness, Roof Fairing (RF) positions, and Reynolds number, while the experimental output is the drag coefficient. These inputs and outputs were arranged via data preprocessing and then modeled using machine learning methods. Modeling was performed with Artificial Neural Network (ANN) and multiple regression models. In addition to the ANN model setup with appropriate topology, linear and non-linear multiple regression models were established. Comparisons were performed using statistical metrics and it was determined that ANN was the superior model. Using the ANN’s prediction model, the drag coefficients were derived from the whole parameter values of both airfoil thickness and RF positions in the solution space for all Reynolds number and an optimization process was performed by PSO. As a result of the analysis, optimal parameter values were determined as 0.10 and 5.0 mm for the airfoil thickness and RF position. As a result of the experimental validation, it is seen that the BTT_NACA 0010_RF-0.5 model, designed with the optimum parameter values, demonstrated a DR performance of 34.7 %, which is the highest value compared to others. Thus, the heuristic process was experimentally validated and optimized by saving time and cost.

Manifold-assisted coevolutionary algorithm for constrained multi-objective optimization

In constrained multi-objective optimization problems (CMOPs), constraints often fragment the Pareto solution space into multiple feasible and infeasible regions. This fragmentation presents a challenge for evolutionary optimization methods as feasible regions can be discrete and isolated by infeasible areas, making exploration difficult and leading to populations getting trapped in local optima. To address these issues, this paper introduces a manifold assisted coevolutionary algorithm for solving CMOPs. Firstly, a guided feasible search strategy is proposed to explore feasible regions, especially those isolated by infeasible barriers. This is achieved by estimating directions to the Constrained Pareto Set (CPS). Secondly, a manifold learning-based exploration strategy is employed to spread the population along the Pareto Set (PS) manifold by estimating the manifold distribution. Moreover, two populations are exploited, where the first population serves as the primary population, considering both constraints and objectives to explore the feasible region and search along the CPS. The second population, on the other hand, does not consider constraints and serves as an auxiliary population to explore the Unconstrained PS. By cooperating, these two populations effectively approach and cover separated CPS segments. The proposed algorithm is evaluated against seven state-of-the-art algorithms on 37 CMOP test functions and 5 CMOPs with fraudulent constraints. The experimental results clearly demonstrate that our algorithm can reliably locate multiple CPSs and is considered state-of-the-art in handling CMOPs.

Flexible Job-shop Scheduling Problem with parallel operations using Reinforcement Learning: An approach based on Heterogeneous Graph Attention Networks

The Flexible Job-shop Scheduling Problem (FJSP) has received considerable scholarly attention as a classic problem. However, in practical industrial manufacturing scenarios, it is common for an operation to have multiple preceding parallel operations. This not only necessitates adhering to the sequential relationships inherent in FJSP but also requires ensuring that preceding operations are completed simultaneously whenever feasible. We term this scenario as the Flexible Job-shop Scheduling Problem with Parallel Operations (FJSP-PO), a pervasive challenge encountered across nearly every production line in real-world discrete manufacturing applications. Despite its prevalence, there is a noticeable scarcity of research on FJSP-PO in existing literature. Given the objective of synchronizing multiple preceding operations, FJSP-PO presents a broader solution space and more intricate optimization challenges compared to traditional FJSP. To address this, we propose an Attention Restart method based on Heterogeneous Graph Attention Networks (AR-HGAT). Leveraging a heterogeneous graph network structure and reinforcement learning, AR-HGAT learns the implicit features of operations and machines through node-level and semantic-level attention mechanisms. The AR mechanism is utilized to determine the optimal scheduling of operations at specific time slots. Compared to existing FJSP methods, our AR-HGAT approach demonstrates superior performance in terms of inference time and solution effectiveness. Furthermore, we conducted a comparative analysis using authentic operational data from companies and contrasted it with results obtained from an online tree search algorithm, thereby providing empirical validation of the effectiveness of the proposed AR-HGAT method.

Heliostat Clustering for Aiming Point Strategies Optimization

The performance of solar tower systems is closely linked to the aiming point strategy of the heliostats. The optimization process of obtaining the best aiming point strategy for a field is complex and has a high computational cost. The use of the clustering technique relieves the requirements by decreasing the space of possible solutions to the problem. Results show that the application of the technique for aiming point strategy optimization reduces the time of optimization significantly.

CIR-DE: A chaotic individual regeneration mechanism for solving the stagnation problem in differential evolution

Stagnant evolution is a problem frequently encountered by the population in differential evolution (DE). Aiming at the stagnation phenomenon, a comprehensive interpretation is provided in this paper. Our experiment confirms that the individuals that continuously stop evolving can be classified into two categories: global and local stagnant individuals, whose causes and exhibited characteristics are associated with the search behavior of the population. Based on the above findings, we propose a chaotic individual regeneration framework (CIR) for DEs. In the CIR-DE, a monitor is designed to recognize different types of stagnant individuals by evaluating the whole population’s convergence speed and specific individual’s location. Besides, two chaotic regeneration techniques are proposed to guide the above two types of individuals away from stagnation using the knowledge from solution and objective spaces. The CIR framework is implemented in nine representative DEs and tested in the CEC 2014, CEC 2017, CEC 2022 theoretical benchmarks and five real-world problems. The results reveal that our framework can significantly improve original DEs’ performance and alleviate stagnation in both theoretical and practical scenarios. The CIR framework also shows strong competitiveness compared to the other stagnation-related frameworks and the state-of-the-art DE variants.

Insights into yeast response to chemotherapeutic agent through time series genome-scale metabolic models.

Organism-specific genome-scale metabolic models (GSMMs) can unveil molecular mechanisms within cells and are commonly used in diverse applications, from synthetic biology, biotechnology, and systems biology to metabolic engineering. There are limited studies incorporating time-series transcriptomics in GSMM simulations. Yeast is an easy-to-manipulate model organism for tumor research.Here, a novel approach (TS-GSMM) was proposed to integrate time-series transcriptomics with GSMMs to narrow down the feasible solution space of all possible flux distributions and attain time-series flux samples. The flux samples were clustered using machine learning techniques, and the clusters' functional analysis was performed using reaction set enrichment analysis.A time series transcriptomics response of Yeast cells to a chemotherapeutic reagent-doxorubicin-was mapped onto a Yeast GSMM. Eleven flux clusters were obtained with our approach, and pathway dynamics were displayed. Induction of fluxes related to bicarbonate formation and transport, ergosterol and spermidine transport, and ATP production were captured.Integrating time-series transcriptomics data with GSMMs is a promising approach to reveal pathway dynamics without any kinetic modeling and detects pathways that cannot be identified through transcriptomics-only analysis. The codes are available at https://github.com/karabekmez/TS-GSMM.

Dual subspace clustering for spectral-spatial hyperspectral image clustering

Subspace clustering supposes that hyperspectral image (HSI) pixels lie in a union vector spaces of multiple sample subspaces without considering their dual space, i.e., spectral space. In this article, we propose a promising dual subspace clustering (DualSC) for improving spectral-spatial HSIs clustering by relaxing subspace clustering. To this end, DualSC simultaneously optimizes row and column subspace-representations of HSI superpixels to capture the intrinsic connection between spectral and spatial information. From the new perspective, the original subspace clustering can be treated as a special case of DualSC that has larger solution space, so tends to finding better sample representation matrix for applying spectral clustering. Besides, we provide theoretical proofs that show the proposed method relaxes the subspace space clustering with dual subspace, and can recover subspace-sparse representation of HSI samples. To the best of our knowledge, this work could be one of the first dual clustering method leveraging sample and spectral subspaces simultaneously. As a result, we conduct several clustering experiments on four canonical data sets, implying that our proposed method with strong interpretability reaches comparable performance and computing efficiency with other state-of-the-art methods.

A hound-inspired pre-hybridized genetic approach for router placement in wireless mesh networks

In the last years, wireless mesh networks (WMNs) have gained more and more popularity in many research and industrial applications thanks to their easy implementation, maintenance, and great reliability at a low cost. Nevertheless, for a large number of nodes, the performance of such networks is heavily influenced by the positioning of routers and gateways over the area to be covered. In this paper, we tackle the router placement problem, which is known to be NP-hard, and its approximate solution through a meta-heuristic approach. The proposed solution empowers the benefits offered by a genetic algorithm pre-hybridized with a local search approach inspired by the behavior of hound dogs. The basic idea is to exploit the dogs’ capabilities in moving throughout the solution space to effectively explore it by placing themselves in areas that are more favorable for achieving a high-quality approximate solution in a reasonable time. Experimental results on several benchmarking instances and comparisons with the most effective state-of-the-art algorithms have demonstrated the potential of the proposed approach. This is evidenced by very high connectivity and coverage, a low number of generations, and a small GA population required for convergence. This results in low computational effort and significant time savings, which are of paramount importance in IoT and edge scenarios. We remark that, although offering potential, at the current state, our proposal is not able to adapt to areas with obstacles or irregular shapes.

Multi-guided population co-evolutionary algorithm based on multiple similarity decomposition for large-scale flexible job shop scheduling problem

As manufacturing shifts towards large-scale production, the size of the workshop increases, and its search space exponentially expands. It is difficult for existing algorithms to obtain an ideal scheduling solution in an acceptable time. For the large-scale flexible job shop scheduling problem (LSFJSP), a multi-guided population co-evolutionary algorithm based on multiple similarity decomposition (MPCSD) is designed. Faced with the problem of high dimensionality and complex solution space, a multiple similarity decomposition strategy is proposed. It proceeds to group based on similarity information at the dimension and population level. To obtain convergence-preferred and diversity-preferred dimension groupings, a training-set solution selection method is proposed. Inspired by the idea of divide-and-conquer, a multi-guided co-evolutionary strategy is proposed. It improves the exploration efficiency of the algorithm in the search space. To test effectiveness on more complex LSFJSP, a set of large-scale test problems including LS1-12 are designed. On LS1-12, MPCSD is compared with seven other algorithms to demonstrate its superiority. MPCSD performed best on 11, 7, and 10 of the 12 test problems on Inverted Generational Distance (IGD), Hypervolume (HV) and Schott’s Spacing Metric (SP), respectively. Meanwhile, the Relative Deviation (RD) results showed that MPCSD obtained the best fitness performance.

Obstacle Avoidance Path Planning Strategy for Autonomous Vehicles Based on Genetic Algorithm

In order to enhance the driving ability of autonomous vehicles on structured roads and enable them to plan safe and comfortable paths, we propose an obstacle avoidance path strategy for autonomous vehicles based on genetic algorithm. The use of Frenet-Serret enhances the adaptability of the algorithm in complex environments. In order to improve the generation and optimisation of obstacle avoidance trajectory, we establish an anti-collision model. When the vehicle faces a potential collision with an obstacle, the genetic algorithm quickly iterates and selects the first nine genes to generate the rough solution and convex space of the path. Combined with convex space, the quadratic programming method will numerically optimise the generated rough solution to generate an accurate path that satisfies the constraints. In addition, in order to ensure the safety and comfort in the process of obstacle avoidance, based on the dynamic constraints of the vehicle, the speed planning is used to determine the speed curve. We simulate in various scenarios involving moving obstacles. The real-time simulation based on the HIL platform proves that the proposed path planning strategy is effective in various driving scenarios.

Staying positive: producing net power.

The Spherical Tokamak for Energy Production (STEP) prototype powerplant (SPP) will be a first-of-a-kind powerplant-its prime objective is to export electrical power, to the national power transmission system ('grid'), above 100 MWe. As part of a wider issue, addressing the STEP concept design, this article seeks to explore how electrical power will be generated from a spherical tokamak heat source. Accordingly, the following key functions of the SPP power infrastructure are reviewed.Cooling the tokamak: cooling the tokamak while extracting useful thermal energy.Generating power: conversion of thermal energy to electrical energy (power generation).Managing energy: management of the site-wide distribution, storage and energy export.In each of these areas, the design scope, challenges and solution spaces have been discussed. This has shaped the design of the SPP power infrastructure, which in turn has ensured a powerplant design focused on operability and performance. Furthermore, it has been demonstrated that the SPP will achieve its prime objective in generating net power, which is enabled by a unique power infrastructure. Confidence in the ability to generate net power will be refined as the design matures. Finally, this article recommends key opportunities that STEP could use to improve power generation and reduce the parasitic load of the SPP.This article is part of the theme issue 'Delivering Fusion Energy - The Spherical Tokamak for Energy Production (STEP)'.

Investigating the correlation between visual representation flow, physical prototyping, and designers’ creativity

This study aims to explore how prototypes, sketches and CAD models supported designers during the designers during the early ideation, idea development in the engineering design process flow. Using a mixed-methods study, the designer’s roles and contributions were evaluated with respect to the achieved design quality. 81 Professional industrial designers were observed and interviewed after participating in five workshop sessions. After exploring different permutations of sketching, prototyping and CAD modelling, results indicate that starting with ‘Prototyping’ in the early ideation stage contributed significantly to more ideas of higher quality, broadening the ideation space. Using CAD modelling too early in the engineering design process constrains ideation and prematurely limits solution spaces.

Gender diversity in construction: demystifying the pipeline leaks in Australia, United States, United Kingdom and Brazil

Globally, the construction industry is a key contributor to the gross domestic product. However, compared to the gender diversity performance of the workforce in the world economy, historically, construction has been performing significantly poorly. Literature argued that these consistently poor performances in diversity, equity and inclusion were causing leaks in the education and career pipeline. However, a systematic investigation with evidence base was lacking. In this vacuum, the proposed study aims to explore the evolution of gender dynamics within the construction sector in Australia, United States, United Kingdom, and Brazil through quantitative evidence. This study collected industry gender representation data, gender pay gaps and tertiary degrees conferred from government agencies in four countries: Australia, the United States, the United Kingdom and Brazil. Quantitative data analysis was conducted with an exploration of factual figures, significant trajectories and fluctuations. Results were explored to understand local jurisdictions’ possible causal relationships and interventions. Delving into findings from the education pipeline revealed declining trends and alarming opportunities for the education institutions to take a lead role in moving from a “challenge leaky pipeline” towards a “shared solution space” through international cross-sectorial collaborations with the paradigm shift in the construction industry with the emerging fifth industrial revolution.

A Decomposition Approach for Sequencing Mixed-Model Two-Sided Assembly Line with Stochastic Processing Time

This study tackles the challenge of optimizing mixed-model two-sided assembly lines, where task processing times are uncertain. The objective is to reduce the expected cycle time the average time to complete one product across the assembly line. Given the complexity of assessing objectives amidst stochastic conditions, we formulate the problem as a simulation optimization problem. We introduce a strategic decomposition method that breaks down the core problem into two discrete sub-tasks: allocating tasks to respective work-stations, and determining the sequence of tasks at each station. The decomposition framework systematically partitions the solution space, though it does not ensure a global optimum, it can efficiently guide the search towards a high-quality near-optimal solution with a practical time frame. Based on this framework, we develop a novel simulation-optimization algorithm, termed the Decomposition Approach with Harmony Search (DAHS), which incorporates a harmony search heuristic to effectively navigate the partitioned solution space. Additionally, we implement two innovative strategies to improve the search and simulation procedures. Numerical experiments reveal that our DAHS algorithm outperforms benchmark algorithms in terms of solution quality and computational efficiency.

Methods for automating and improving the robustness of linear Kramers–Kronig tests for the purposes of validating immittance spectra

Methods for automating the selection of key parameters for linear Kramers–Kronig tests are described. These methods include not only new methods and improvements to existing methods for suggesting the optimum number of time constants, but also methods for estimating the limits of the solution space. Methods for optimizing the range of time constants and a method for suggesting the representation of the immittance spectrum are also described. These methods are evaluated using both valid and invalid synthetic immittance spectra that present various challenges. Practical considerations regarding the implementation of the linear Kramers–Kronig tests and the various methods, the choice of linear Kramers–Kronig test and data representation to use, and the visualization and interpretation of results are also discussed.

Enhancing Concurrent Emergency Response: Joint Scheduling of Emergency Vehicles on Freeways with Tailored Heuristic

Scheduling decisions for concurrent emergency response (CER) across multiple disaster sites presents numerous difficulties. The main challenge is to minimize human casualties while taking into account the rationality of resource allocation across different disaster sites. This paper establishes a joint scheduling model for emergency vehicles on freeways in the context of CER. The model aims to minimize the transportation time, dispatch cost, and casualty risk, by using the resource site scheduling scheme as the decision variable, addressing multiple disaster and resource sites. Specifically, a casualty risk function based on the rescue waiting time was designed to balance the competing needs among various disaster sites, enhance equitable resource allocation, and reduce the probability of casualties. To achieve global convergence in a high-dimensional solution space, a tailored heuristic algorithm called adaptive dual evolutionary particle swarm optimization (ADEPSO) is proposed. The numerical results show that the scheduling scheme proposed by the ADEPSO algorithm satisfies all constraints and demonstrates significant advantages in large-sized instances. Compared to the two basic algorithms, ADEPSO provides a more cost-effective scheme and reduces the average rescue waiting time. Moreover, integrating the casualty risk function significantly decreases the average rescue waiting time at both high- and low-priority disaster sites, thereby directly lowering the casualty risk.

Polar lights optimizer: Algorithm and applications in image segmentation and feature selection

This study introduces Polar Lights Optimization (PLO), an algorithm based on the aurora phenomenon or polar lights. The aurora is a unique natural spectacle that occurs when energetic particles from the solar wind converge at the Earth's poles, influenced by the geomagnetic field and the Earth's atmosphere. By analyzing the motion of high-energy particles and delving into the underlying principles of physics, we propose a unique model for mimicking particle motion. This model integrates gyration motion and aurora oval walk, with the former facilitating local exploitation, while the latter enabling global exploration. By synergistically combining these two strategies, the proposed PLO achieves a balanced approach to local exploitation and global exploration. Additionally, a particle collision strategy is introduced to enhance the efficiency of escaping local optima. To evaluate the performance of PLO, a qualitative analysis experiment is designed to assess its ability to explore the problem space and search for solutions. PLO is compared against 9 classic algorithms and 8 high-performance algorithms using 30 benchmark functions from IEEE CEC2014. Furthermore, we compare and analyze PLO with the current state-of-the-art methods in the field, utilizing 12 benchmark functions from IEEE CEC2022. Subsequently, PLO is successfully applied to multi-threshold image segmentation and feature selection. Specifically, a PLO-based multi-threshold segmentation model and a binary PLO-based feature selection method are developed. The performance of PLO is also evaluated using 10 images from the Invasive Ductal Carcinoma (IDC) medical dataset, while the overall adaptability and accuracy of the feature selection model are tested using 8 medical datasets. These results affirm the emergence of PLO as an effective optimization tool ready for solving real-world problems, including those in the medical field. The source codes of PLO are available at https://aliasgharheidari.com/PLO.html and other websites.

XDL-ESI: Electrophysiological Sources Imaging via explainable deep learning framework with validation on simultaneous EEG and iEEG

Electroencephalography (EEG) or Magnetoencephalography (MEG) source imaging aims to estimate the underlying activated brain sources to explain the observed EEG/MEG recordings. Solving the inverse problem of EEG/MEG Source Imaging (ESI) is challenging due to its ill-posed nature. To achieve a unique solution, it is essential to apply sophisticated regularization constraints to restrict the solution space. Traditionally, the design of regularization terms is based on assumptions about the spatiotemporal structure of the underlying source dynamics. In this paper, we propose a novel paradigm for ESI via an Explainable Deep Learning framework, termed as XDL-ESI, which connects the iterative optimization algorithm with deep learning architecture by unfolding the iterative updates with neural network modules. The proposed framework has the advantages of (1) establishing a data-driven approach to model the source solution structure instead of using hand-crafted regularization terms; (2) improving the robustness of source solutions by introducing a topological loss that leverages the geometric spatial information applying varying penalties on distinct localization errors; (3) improving the reconstruction efficiency and interpretability as it inherits the advantages from both the iterative optimization algorithms (interpretability) and deep learning approaches (function approximation). The proposed XDL-ESI framework provides an efficient, accurate, and interpretable paradigm to solve the ESI inverse problem with satisfactory performance in both simulated data and real clinical data. Specially, this approach is further validated using simultaneous EEG and intracranial EEG (iEEG).

Mission design for space telescope servicing at Sun–Earth L2

The Sun–Earth Lagrange points, L1 and L2, are excellent locations for space based observatories, due to their favorable dynamical and environmental properties. Space telescopes, however, come with an extremely high price tag, and without refueling and servicing the typical lifetime is only about ten to fifteen years. The return on investment of a space telescope could be significantly increased by developing on-orbit servicing and refueling capabilities, assuming that the servicing vehicle can be launched at a fraction of the cost of the original mission and that the lifetime of the space telescope can be significantly extended to allow for continued scientific activity. In this paper we demonstrate the feasibility of this class of servicing mission from a trajectory design perspective, using novel techniques that we have developed for quick sampling of the solution space, as well as an efficient and reliable means to obtained high-fidelity, time-optimal, end-to-end transfer trajectories. As candidates for this investigation we consider Gaia and the James Webb Space Telescope. Our trajectory design methodology is, however, general enough to be applicable to future space observatories that are designed to be serviceable in space.

Fitness landscapes of buffer allocation problem for production lines with unreliable machines

We study the structural properties of the buffer allocation problem from the fitness landscape perspective. We consider manufacturing flow lines with series–parallel network structure. The machines are supposed to be unreliable, their time to failure and repair time are exponentially distributed. Tentative solutions are evaluated by means of an approximate method based on the Markov models aggregation. We carry out computational experiments with local search and genetic algorithms in order to evaluate the fitness landscape properties of previously published instances and their modifications. It turns out that the so-called ‘massif central’ or ‘big valley’ structure of the fitness landscape is present but only partially: The fitness of local optima is negatively correlated with the distance to the best found solution, yet the set of local optima cannot be encompassed by a ball of relatively small size with respect to the size of solution space. Moreover, we show that in many problem instances, several clusters of local optima can be identified. The performance of genetic algorithms is discussed with respect to population clustering and the permanent usage of crossover is recommended.