Design Problem Research Articles

Transmit beampattern design for FDA-MIMO radar using complex manifold optimization

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar can produce time-stationary range-angle dependent beampattern. The problem with these beampatterns is the repeating lobes in the angle-range domain. Different methods are proposed to obtain better beampattern in terms of grating lobe and sidelobe suppression by using frequency offsets. However, these methods mostly choose the frequency offsets using an analytic expression and do not offer a control on the resulting range beampattern. In this paper, a new method for transmit beampattern design in range is presented. This method transfers the design problem into a tangential space and solves the problem using complex manifolds through an optimization procedure. The proposed approach considers amplitude tapering in the transmitter antenna elements and the practical instrumented range of the radar. Range beampattern constraints can be defined conveniently leading to an effective design process. Several simulations are done to show that the proposed method can significantly improve the beamwidth and sidelobe level in range patterns.

Selective clustered tourist trip design problem with time windows under intuitionistic fuzzy score and exponential travel times

Creating travel plans is a difficult task for tourists. Hence, this is due to planning trips for a selection of Points of Interest (POIs) that consider several constraints and preferences to identify POIs and optimize the attractiveness of routes in general. The literature addresses the problem as the Tourist Trip Design Problem (TTDP). This paper considers a particular variant of TTDP, where POIs are grouped into clusters corresponding to various destinations. Additionally, the visit to each POI must occur within a specific time interval. Minimum and maximum restrictions exist on how many POIs from each cluster may be visited on the same itinerary. Moreover, we proposed the Tourist Trip Design Problem with Clustered POIs under intuitionistic fuzzy scores and exponential travel times in uncertainty and randomness. This problem is finding a feasible route that maximizes the total score collected. The route must begin and end at a specified initial location, and its duration is limited to a specific maximum value. A mathematical model is proposed for this problem. Test problems are generated and solved by the proposed model. The solution to the test problems is obtained by using different parameters. The results show that the proposed methodology is an approach that allows tourists to get other travel plans and see how well these travel plans meet their demands and preferences.

Secure beamforming design for MISO URLLC networks in IoT applications

This paper investigates joint beamforming and artificial noise (AN) design for secure multiple-input single-output (MISO) ultra-reliable and low latency communication (URLLC) networks in Internet of Things (IoT) applications. In considered system, a base station (BS) transmits confidential information for individual IoT users using the short-packet communication technique under the wiretap of eavesdroppers. To enhance physical layer security, the BS injects additional dedicated AN symbols to degrade the information retrieval ability at eavesdroppers. In this paper, we aim to joint design the transmit beamforming and AN symbol to maximize the minimum URLLC secrecy rate of IoT user subject to the power budget of the BS. The optimization design problem is highly nonconvex due to coupled variables in the URLLC secrecy rate and channel dispersion expressions, and thus it is mathematically challenging to solve this problem directly. To overcome this issue, we first introduce various convex inner approximations to convexify the nonconvex terms, and then we develop an efficient iterative algorithm based on the sequential convex programming approach. Extensive numerical simulation results are conducted to investigate the URLLC secrecy rate region. In conclusion, the two new URLLC parameters, i.e., the transmit packet blocklength and block error probability, will cause the considerable degradation on the URLLC secrecy rate region when comparing to that of the traditional beamforming design based on the Shannon capacity.

Framework for the application of explainable artificial intelligence techniques in the service of democracy

Purpose This paper aims to explore explainable artificial intelligence (XAI) in democracy, proposing an applicable framework. With artificial intelligence’s (AI) increasing use in democracies, the demand for transparency and accountability in AI decision-making is recognized. XAI addresses AI “black boxes” by enhancing model transparency. Design/methodology/approach This study includes a thorough literature review of XAI. The methodology chosen was design science research to enable design theory and problem identification about XAI’s state of the art. Thereby finding and gathering crucial information to build a framework that aims to help solve issues and gaps where XAI can be of major influence in the service of democracy. Findings This framework has four main steps to be applied in the service of democracy by applying the different possible XAI techniques that may help mitigate existing challenges and risks for the democratic system. The proposed artifact intends to display and include all the necessary steps to select the most suitable XAI technology. Examples were given for every step of the artifact to provide a clear understanding of what was being proposed. Originality/value An evaluation of the proposed framework was made through interviews with specialists from different areas related to the topics in the study. The interviews were important for measuring the framework’s validity and originality.

Extrapolation is not the same as interpolation

AbstractWe propose a new machine learning formulation designed specifically for extrapolation. The textbook way to apply machine learning to drug design is to learn a univariate function that when a drug (structure) is input, the function outputs a real number (the activity): f(drug) $$\rightarrow$$ → activity. However, experience in real-world drug design suggests that this formulation of the drug design problem is not quite correct. Specifically, what one is really interested in is extrapolation: predicting the activity of new drugs with higher activity than any existing ones. Our new formulation for extrapolation is based on learning a bivariate function that predicts the difference in activities of two drugs F(drug1, drug2) $$\rightarrow$$ → difference in activity, followed by the use of ranking algorithms. This formulation is general and agnostic, suitable for finding samples with target values beyond the target value range of the training set. We applied the formulation to work with support vector machines , random forests , and Gradient Boosting Machines . We compared the formulation with standard regression on thousands of drug design datasets, gene expression datasets and material property datasets. The test set extrapolation metric was the identification of examples with greater values than the training set, and top-performing examples (within the top 10% of the whole dataset). On this metric our pairwise formulation vastly outperformed standard regression. Its proposed variations also showed a consistent outperformance. Its application in the stock selection problem further confirmed the advantage of this pairwise formulation.

A hybrid quantum computing pipeline for real world drug discovery

Quantum computing, with its superior computational capabilities compared to classical approaches, holds the potential to revolutionize numerous scientific domains, including pharmaceuticals. However, the application of quantum computing for drug discovery has primarily been limited to proof-of-concept studies, which often fail to capture the intricacies of real-world drug development challenges. In this study, we diverge from conventional investigations by developing a hybrid quantum computing pipeline tailored to address genuine drug design problems. Our approach underscores the application of quantum computation in drug discovery and propels it towards more scalable system. We specifically construct our versatile quantum computing pipeline to address two critical tasks in drug discovery: the precise determination of Gibbs free energy profiles for prodrug activation involving covalent bond cleavage, and the accurate simulation of covalent bond interactions. This work serves as a pioneering effort in benchmarking quantum computing against veritable scenarios encountered in drug design, especially the covalent bonding issue present in both of the case studies, thereby transitioning from theoretical models to tangible applications. Our results demonstrate the potential of a quantum computing pipeline for integration into real world drug design workflows.

Anti-Disturbance Bumpless Transfer Control for a Switched Systems via a Switched Equivalent-Input-Disturbance Approach

This paper concentrates on the issue of anti-disturbance bumpless transfer (ADBT) control design for switched systems. The ADBT control design problem refers to designing a continuous controller and a switching rule to ensure the switched system satisfies the ADBT property. First, the concept of the ADBT property is introduced. Then, via a switched equivalent-input-disturbance (EID) methodology, a switched EID estimator is formulated to estimate the impact of external disturbances within the switched system. Second, a bumpless transfer control is then constructed via a compensator integrating an EID estimation. Finally, the effectiveness of the presented control scheme is verified by controlling a switching resistor–inductor–capacitor circuit on the Matlab platform. Above all, a new configuration for ADBT control of switched systems is established via a switched EID methodology.

Cellular gradient algorithm for solving complex mechanical optimization design problems

In mechanical optimization design problems, there are often some non-continuous or non-differentiable objective functions. For these non-continuous and non-differentiable optimization objectives, it is often difficult for existing optimal design algorithms to find the desired optimal solutions. In this paper, we incorporate the idea of gradient descent into cellular automata and propose a Cellular Gradient (CG) method. First, we have given the basic rules and algorithmic framework of CG and designed three kinds of growth and extinction rules respectively. Then, the three evolutionary rules for cellular within a single cycle are analyzed separately for form and ordering. The best expressions for the cellular jealous neighbor rule and the solitary regeneration rule are given, and the most appropriate order in which the rules are run is selected. Finally, the solution results of the cellular gradient algorithm and other classical optimization design algorithms are compared with a multi-objective multi-parameter mechanical optimization design problem as an example. The computational results show that the cellular gradient algorithm has an advantage over other algorithms in solving global and dynamic mechanical optimal design problems. The novelty of CG is to provide a new way of thinking for solving optimization problems with global discontinuities.

Revolutionizing optimization: An innovative nutcracker optimizer for single and multi-objective problems

Nutcracker Optimization Algorithm (NOA) is a recently proposed meta-heuristic algorithm inspired by foraging and storing behavior of nutcracker birds. NOA demonstrates strong performance across various test sets and optimization problems. However, it faces challenges in effectively balancing exploration and exploitation, particularly in high-dimensional and complex applications. In this paper, an improved variant of NOA based on Bernoulli map strategy and seasonal behavior strategy, called INOA, is proposed. Firstly, the Bernoulli map strategy enhances the quality of the initial population during the initialization process. Secondly, the seasonal behavior strategy is employed to balance the exploration and exploitation of NOA, enabling it to effectively handle high-dimensional problems by improving convergence and exploration capabilities. Additionally, this paper extends INOA to a multi-objective version called MONOA, enabling the algorithm to solve multi-objective problems. The proposed algorithm, INOA, undergoes evaluation using 30 classical benchmark problems, CEC-2014, CEC-2017, CEC-2019 test suites, and two real-world engineering design problems. INOA’s performance is compared with three categories of optimization methods: (1) recently-developed algorithms, i.e., NOA, BWO, DBO, RIME, MGO, HBA, and SO, (2) highly-cited algorithms, i.e., SMA, MPA, GWO, and (3) high-performing optimizers and winners of CEC competition, i.e., CJADE, L-SHADE-RSP, L-SHADE, and EBOwithCMAR. The proposed algorithm, MONOA, undergoes evaluation using well-known ZDT and DTLZ suites, as well as six constrained and engineering design problems. MONOA’s performance is compared with some state-of-the-art approaches such as MOPSO, NSSO, MOGOA, MOSMA, and MOMGA. Five performance indicators are employed for comparison purposes. Experimental results and comparisons affirm the efficacy of INOA in solving complex and higher-dimensional optimization problems. Similarly, the findings underscore the effectiveness of MONOA in solving diverse multi-objective problems with distinct characteristics.

Novel Hybrid Crayfish Optimization Algorithm and Self-Adaptive Differential Evolution for Solving Complex Optimization Problems

This study presents the Hybrid COASaDE Optimizer, a novel combination of the Crayfish Optimization Algorithm (COA) and Self-adaptive Differential Evolution (SaDE), designed to address complex optimization challenges and solve engineering design problems. The hybrid approach leverages COA’s efficient exploration mechanisms, inspired by crayfish behaviour, with the symmetry of SaDE’s adaptive exploitation capabilities, characterized by its dynamic parameter adjustment. The balance between these two phases represents a symmetrical relationship wherein both components contribute equally and complementary to the algorithm’s overall performance. This symmetry in design enables the Hybrid COASaDE to maintain consistent and robust performance across a diverse range of optimization problems. Experimental evaluations were conducted using CEC2022 and CEC2017 benchmark functions, demonstrating COASaDE’s superior performance compared to state-of-the-art optimization algorithms. The results and statistical analyses confirm the robustness and efficiency of the Hybrid COASaDE in finding optimal solutions. Furthermore, the applicability of the Hybrid COASaDE was validated through several engineering design problems, where COASaDE outperformed other optimizers in achieving the optimal solution.

Green flexible production and intelligent factory building structure design based on improved ant colony algorithm

Traditional factory production has limitations such as energy waste, emission pollution, etc., and there are also certain unreasonable structural aspects. Therefore, this article proposes a method that can make the factory building structure more green, flexible, and intelligent. It is hoped that by improving the ant colony algorithm and applying it to factory design and production, the goal of reducing energy consumption, maximizing resource utilization, and reducing environmental impact can be achieved. Transforming the design problem of factory building structure into an optimization problem using ant colony algorithm, that is, finding the optimal path and layout. In this process, the ant colony algorithm simulates the behavior of ants by continuously iterating and updating the concentration of pheromones, in order to achieve the global optimal solution. In order to achieve the combination of green design principles and flexible production needs, corresponding constraint conditions and objective functions were introduced, taking into account the flexibility and adjustability within the factory to meet different production needs. Ant colony algorithm can provide the best structural design solution for factory buildings by optimizing search, combining green design principles and flexible production requirements, and promoting the development of green and sustainable factory buildings.

Static anti-windup compensator based on BMI optimisation for discrete-time systems with directional change in controls avoidance

In the paper, a design method of a static anti-windup compensator for systems with input saturations is proposed. First, an antiwindup controller is presented for system with cut-off saturations, and, secondly, the design problem of the compensator is presented to be a non-convex optimization problem easily solved using bilinear matrix inequalities formulation. This approach guarantees stability of the closedloop system against saturation nonlinearities and optimizes the robust control performance while the saturation is active.

Symmetric optical multipass matrix systems and the general rapid design methodology

We proposed an original type of multipass cell named symmetric optical multipass matrix system (SMMS), in which the same matrix patterns of various sizes can be formed on both sides. According to its special symmetric configurations, the SMMS design problem is modeled as a variant of the classical traveling salesman problem, which can be rapidly solved by evolutionary optimization algorithms. Two sets of 3-mirror SMMSs are designed, analyzed and built, which show superior characteristics of high stability, desirable beam quality and adjustable optical path lengths. Additionally, they can support simultaneous detection of multiple species with multi-laser channels. The proposed method is further extended to design a 4-mirror SMMS, which verifies the universality and robustness of the design methodology. The experimental observations are in consistent with the theoretical calculations. The newly proposed SMMSs have a broad application prospect in trace gas measurement.

Three network design problems for community energy storage

AbstractIn this article, we develop novel mathematical models to optimize utilization of community energy storage (CES) by clustering prosumers and consumers into energy sharing communities/microgrids in the context of a smart city. Three different microgrid configurations are modeled using a unifying mixed‐integer linear programming formulation. These configurations represent three different business models, namely: the island model, the interconnected model, and the Energy Service Companies model. The proposed mathematical formulations determine the optimal households' aggregation as well as the location and sizing of CES. To overcome the computational challenges of treating operational decisions within a multi‐period decision making framework, we also propose a decomposition approach to accelerate the computational time needed to solve larger instances. We conduct a case study based on real power consumption, power generation, and location network data from Cambridge, MA. Our mathematical models and the underlying algorithmic framework can be used in operational and strategic planning studies on smart grids to incentivize the communitarian distributed renewable energy generation and to improve the self‐consumption and self‐sufficiency of the energy sharing community. The models are also targeted to policymakers of smart cities, utility companies, and Energy Service Companies as the proposed models support decision making on renewable energy related projects investments.

Approximate dynamic programming for liner shipping network design

AbstractThe global containerised trade heavily relies on liner shipping services, facilitating the worldwide movement of large cargo volumes along fixed routes and schedules. The profitability of shipping companies hinges on how efficiently they design their shipping network; a complex optimization problem known as the liner shipping network design problem (LSNDP). In recent years, approximate dynamic programming (ADP), also known as reinforcement learning, has emerged as a promising approach for large-scale optimisation. This paper introduces a novel Markov decision process for the LSNDP and investigates the potential of ADP. We show that ADP methods based on value iteration produce optimal solutions to small instances, but their scalability is hindered by high memory demands. An ADP method based on a deep neural network requires less memory and successfully obtains feasible solutions. The quality of solutions, however, declines for larger instances, possibly due to the discrete nature of high-dimensional state and action spaces.

Study on pore structure evolution and water damage of asphalt mixture under cyclic loading

For a long time, the water damage issues of asphalt mixture pavements have been highly regarded by engineers. However, the existing research is difficult to accurately describe the fatigue damage process of asphalt mixture under hydrodynamic pressure environments. In order to reveal the influence of water damage on the asphalt mixture structure and its mechanism, this study combined low-field NMR technology to investigate the evolution law of asphalt mixture pore structure under cyclic loading with different parameters. The erosion effect of the coupled loading-dynamic water environment on asphalt mixtures and their failure mechanisms are analyzed. The results show that the initial damage dominated by harmful pores and major-harm pores provides space for water migration and storage, and improves the sensitivity of asphalt mixture to water damage. Under the action of repeated cycles of stress increase-decrease, the irreversible hydrodynamic extrusion-cracking and friction-stripping deformations in the asphalt matrix increase. The micro-cracks inside the materials continue to expand and the pores are interconnected. To comprehensively consider the influence of initial porosity, maximum cyclic stress and the number of cycles, the cumulative damage model of the material is optimized based on the mechanism of pore structure evolution, and obtained more accurate porosity prediction results. This study can provide scientific reference and guidance for solving the problem of asphalt pavement disease and durability design.

Method for Collaborative Layout Optimization of Ship Equipment and Pipe Based on Improved Multi-Agent Reinforcement Learning and Artificial Fish Swarm Algorithm

The engine room is the core area of a ship, critical to its operation, safety, and efficiency. Currently, many researchers merely address the ship engine room layout design (SERLD) problem using optimization algorithms and independent layout strategies. However, the engine room environment is complex, involving two significantly different challenges: equipment layout and pipe layout. Traditional methods fail to achieve optimal collaborative layout objectives. To address this research gap, this paper proposes a collaborative layout method that combines improved reinforcement learning and heuristic algorithms. For equipment layout, the engine room space is first discretized into a grid, and a Markov decision process (MDP) framework suitable for equipment layout is proposed, including state space, action space, and reward mechanisms suitable for equipment layout. An improved adaptive guided multi-agent Q-learning (AGMAQL) algorithm is employed to train the layout model in a centralized manner, with enhancements made to the agent’s exploration state, exploration action, and learning strategy. For pipe layout, this paper proposes an improved adaptive trajectory artificial fish swarm algorithm (ATAFSA). This algorithm incorporates a hybrid encoding method, adaptive strategy, scouting strategy, and parallel optimization strategy, resulting in enhanced stability, accuracy, and problem adaptability. Subsequently, by comprehensively considering layout objectives and engine room attributes, a collaborative layout method incorporating hierarchical and adaptive weight strategies is proposed. This method optimizes in phases according to the layout objectives and priorities of different stages, achieving multi-level optimal layouts and providing designers with various reference schemes with different focuses. Finally, based on a typical real-world engine room engineering case, various leading algorithms and strategies are tested and compared. The results show that the proposed AGMAQL-ATAFSA (AGMAQL-ATA) exhibits robustness, efficiency, and engineering practicality. Compared to previous research methods and algorithms, the final layout quality improved overall: equipment layout effectiveness increased by over 4.0%, pipe optimization efficiency improved by over 40.4%, and collaborative layout effectiveness enhanced by over 2.2%.

A New Latin Hypercube Sampling with Maximum Diversity Factor for Reliability-Based Design Optimization of HLM

This research paper presents a new Latin hypercube sampling method, aimed at enhancing its performance in quantifying uncertainty and reducing computation time. The new Latin hypercube sampling (LHS) method serves as a tool in reliability-based design optimization (RBDO). The quantification technique is termed LHSMDF (LHS with maximum diversity factor). The quantification techniques, such as Latin hypercube sampling (LHS), optimum Latin hypercube sampling (OLHS), and Latin hypercube sampling with maximum diversity factor (LHSMDF), are tested against mechanical components, including a circular shaft housing, a connecting rod, and a cantilever beam, to evaluate its comparative performance. Subsequently, the new method is employed as the basis of RBDO in the synthesis of a six-bar high-lift mechanism (HLM) example to enhance the reliability of the resulting mechanism compared to Monte Carlo simulation (MCS). The design problem of this mechanism is classified as a motion generation problem, incorporating angle and position of the flap as an objective function. The six-bar linkage is first adapted to be a high-lift mechanism (HLM), which is a symmetrical device of the aircraft. Furthermore, a deterministic design, without consideration of uncertainty, may lead to unacceptable performance during the manufacturing step due to link length tolerances. The techniques are combined with an efficient metaheuristic known as teaching–learning-based optimization with a diversity archive (ATLBO-DA) to identify a reliable HLM. Performance testing of the new LHSMDF reveals that it outperforms the original LHS and OLHS. The HLM problem test results demonstrate that achieving optimum HLM with high reliability necessitates precision without sacrificing accuracy in the manufacturing process. Moreover, it is suggested that the six-bar HLM could emerge as a viable option for developing a new high-lift device in aircraft mechanisms for the future.

LQR controller performance via particle swarm optimization and neural networks

AbstractThe inverted pendulum‐cart (IPC) control problem has long been a benchmark in the field of control systems due to its inherent instability and nonlinear dynamics. The linear quadratic regulator (LQR) control technique has been proven to be effective in stabilizing the inverted pendulum; however, the challenge lies in finding the optimal control gains that provide the best performance. This article presents a method to address the LQR control design problem for the IPC system which is compared against a particle swarm optimization based LQR and a neural network optimized LQR. The method provides a deterministic approach to finding the weighing matrices Q and R in accordance with the time domain characteristics chosen by the designer, such as settling time and maximum peak overshoot. Results from MATLAB simulations indicate that the suggested strategy has good performance.

A systematic problem analysis network for product conceptual design

Problem analysis is a starting point of product development. The effective problem analysis can narrow the scope of potential problems and improve design efficiency. Although different methods have been proposed for the design problem analysis, the existing tools of the problem analysis are not systematic and effective. This paper presents a product design problem analysis and solution method based on problem networks. The proposed method conducts the problem decomposition, derivation and solving by considering the problem generation, exploratory and hypotheticals. Computer-aided analysis tools are integrated to improve reproducibility of the analysis solution and reduce the workload of designers. The method is applied in developing a new type of the charging gun cable with a granted patent. Feasibility and effectiveness of the proposed method are proved.