Optimization Issues Research Articles

Nature-Inspired Approach: A Novel Rat Optimization Algorithm for Global Optimization

This work presents a rat optimization algorithm (ROA), which simulates the social behavior of rats and is a new nature-inspired optimization technique. The ROA consists of three operators that simulate rats searching for prey, chasing and fighting prey, and jumping and hunting prey to deal with optimization issues. The Levy flight strategy is introduced into the ROA to keep the algorithm from running into issues with slow convergence and local optimums. The ROA is tested with four real-world engineering optimization issues and twenty-two benchmark functions. Experiments show that the ROA is particularly effective at solving real-world optimization problems compared to other well-known optimization techniques.

Wellbore Stability Analysis Based on the Combination of Geomechanical and Petrophysical Studies

Determining the physical and mechanical properties of rocks is crucial for planning drilling operations. Ignoring geomechanical studies and tools has led to millions of dollars in additional costs related to drilling, meshwork optimization, production optimization, casing collapse, and other issues. These costs can be managed with a thorough geomechanical study. Additionally, some costs may not be apparent in the short term but will become significant over the life of the field. Long-term reservoir management is essential to control these costs. Issues such as reservoir compression, rock cover fractures, and gas storage, if neglected in the short term, can lead to substantial and irreparable damage to the reservoir over time. The "geomechanical model" is fundamental to geomechanical studies, particularly regarding wellbore stability. Given the importance of reservoir geomechanics and petrophysics, this research investigates wellbore stability analysis by combining geomechanical and petrophysical studies through the construction of a one-dimensional geomechanical model of the well. First, the dynamic geomechanical coefficients of the rock for the studied depth range were calculated from wells and petrophysical charts. Then, the static geomechanical coefficients of the rock were estimated based on laboratory-developed relationships. Subsequently, rock mechanics test results were used to estimate rock resistance parameters. The pore pressure was estimated using the compressibility and Eaton methods for the studied range from Well No. 5. Comparing the results of these two methods with RFT test data indicated that the compressibility method is more accurate than Eaton’s method. By estimating the in situ and induced stresses in the well's studied area, the lower and upper limits of the safe mud weight were calculated using the Mohr-Coulomb and Hooke-Brown criteria. Comparison of the results showed that the Mohr-Coulomb criterion is more accurate and, moreover, more conservative than the Hooke-Brown method.

Research on Cooperative Arrival and Energy Consumption Optimization Strategies of UAV Formations

The formation operation of unmanned aerial vehicles (UAVs) is a current research hotspot, particularly in specific mission scenarios where UAV formations are required to cooperatively arrive at designated task areas to meet the needs of coordinated operations. This paper investigates the issues of cooperative arrival and energy consumption optimization for UAV formations in such scenarios. First, focusing on rotorcraft UAVs, the flight energy consumption optimization model and cooperative arrival model are derived and constructed. Next, to address the challenges in solving these models, the multi-objective non-convex functions are transformed into single-objective continuous functions, thereby reducing computational complexity. Furthermore, an interior-point-method-based solving strategy is designed by estimating the initial values of the solving parameters. Finally, simulation experiments validate the feasibility and effectiveness of the proposed method. The experimental results show that when optimizing the energy consumption of a formation of five UAVs, the algorithm converges in just 16 iterations, demonstrating its suitability for practical applications.

Co-Optimization of Speed Planning and Energy Management for Plug-In Hybrid Electric Trucks Passing Through Traffic Light Intersections

To tackle the energy-saving optimization issue of plug-in hybrid electric trucks traversing multiple traffic light intersections continuously, this paper presents a double-layer energy management strategy that utilizes the dynamic programming–twin delayed deep deterministic policy gradient (DP-TD3) algorithm to synergistically optimize the speed planning and energy management of plug-in hybrid electric trucks, thereby enhancing the vehicle’s passability through traffic light intersections and fuel economy. In the upper layer, the dynamic programming (DP) algorithm is employed to create a speed-planning model. This model effectively converts the nonlinear constraints related to the position, phase, and timing information of each traffic signal on the road into time-varying constraints, thereby improving computational efficiency. In the lower layer, an energy management model is constructed using the twin delayed deep deterministic policy gradient (TD3) algorithm to achieve optimal allocation of demanded power through the interaction of the TD3 agent with the truck environment. The model’s validity is confirmed through testing on a hardware-in-the-loop test machine, followed by simulation experiments. The results demonstrate that the DP-TD3 method proposed in this paper effectively enhances fuel economy, achieving an average fuel saving of 14.61% compared to the dynamic programming–charge depletion/charge sustenance (DP-CD/CS) method.

A comprehensive survey of recent advances in facility location problems: Models, solution methods, and applications

Logistics management research has significantly benefited from the increased prominence of warehouse management challenges in recent years, which are now extensively utilized in analyzing logistics distribution and transportation networks. Concerning Facility Location Problems and their variations, this work attempts to perform an extensive literature assessment of current model advances and improvements. This study also aims to ascertain the influence and developments in Facility Location Problems, which are classic optimization problems with numerous applications in manufacturing, distribution, transportation, and supply chain management decisions. This study thoroughly analyzes current developments in facility location problems, a category of optimization issues frequently used in network architecture, logistics, and supply chain management. The review analyzes literature from the past decade, categorizing works based on problem variants, mathematical formulations, solution methods, and exact approaches. Key findings highlight significant progress in solving complex facility location problems through advances in mathematical programming, scalable exact methods, and hybrid metaheuristics. Emerging trends such as uncertainty modeling, multi-objective optimization, and sustainability are also discussed. Sustainable Development Goals (SDGs): SDG 9: Industry, Innovation and Infrastructure; SDG 11: Sustainable Cities and Communities; SDG 12: Responsible Consumption and Production; SDG 13: Climate Action

Lean facility layout selection in a post service to enhance operational performance: a graph theory model

PurposeThis study attempts to analyze the overall effectiveness of Lean System (LS)-based India post facility layout design (FLD) selection.Design/methodology/approachWe have grouped all Lean Six Sigma elements into three different categories: Lean Technical Practices, Lean Workplace Environment Practices, and Lean Ergonomics Practices based on literature support and field surveys in the Indian postal service. Further, it employs a mathematical model known as the graph theory (GT) method. From the GT approach, interactions were identified through LS tools represented through the digraph, matrix model approach. This study was directed at the National Sorting Hub in Mysuru, Karnataka, in the southern part of India. A number of insights were given in the GT area where FLD is possible to evaluate.FindingsThe results showed that Layout five has the highest value, followed by Layout one from the GT approach. The relative relevance and effect values are given as inputs to the permanent function in the digraph technique, and an evaluation index is obtained as an output.Research limitations/implicationsWhen solving any optimization issue with a high number of attributes, the digraph and matrix technique is successful. The layouts that have been identified have yielded valuable insights into how to enhance critical decision-making processes, which are necessary to accomplish company strategic objectives like growing postal business services.Originality/valueThe process operations carried out in India post service as well as its heterogeneity of articles and the intangible measures of production are the significant challenges to keenly analyze the layout design through lean service principles.

Convex set reliability-based optimal attitude control for space solar power station with bounded and correlated uncertainties

Considering the bounded and correlated uncertainties in the inherently nonlinear attitude dynamics, this paper proposes an uncertain optimal attitude control for space solar power stations (SSPS), subjected to the convex set-based reliability constraint. To accurately and cost-effectively quantify the uncertainty in the SSPS attitude control system, the uncertain attitude dynamics are proposed based on nominal SSPS with convex set-based uncertainties. Given the known bounds and correlations of the random variables, the convex set-based attitude dynamics of SSPS can be conveniently constructed and transformed into corresponding uncertain state-space equations. Uncertain state and output can be solved using the extended-order matrix and uncertainty propagation method. A convex set-based Riccati equation is: developed using the convex set theory, traditional algebraic Riccati equation (ARE), and Lyapunov equation, which can obtain all the uncertain components of feedback gain, input torque, and cost function, respectively. Convex set-based time-dependent reliability (CSTR) is applied to assess the safety of the attitude control system by setting critical values and considering the time-dependent effect, which is regarded as an important constraint in the optimal attitude control issue with uncertainty. A numerical example of SSPS is provided to verify the proposed uncertain controller within the reliability-constrained optimization framework, demonstrating its effectiveness in managing nonlinear system dynamics under uncertainty.

Gossip-based asynchronous algorithms for distributed composite optimization

The distributed composite optimization problem associated a multi-agent network is investigated in this paper. Different from conventional optimization issues, the cost function of composite optimization consists of a convex function and a regularization function (possibly nonsmooth). The gossip protocol is also introduced to enhance the robustness of the network, and a gossip-based distributed composite mirror descent algorithm is presented to deal with the previous problem, which adopts the asynchronous communication method. Moreover, the algorithm performance is analyzed and the theoretical results on the corresponding error bounds are obtained. Finally, the distributed logistic regression is provided as an example to validate the practicability of the proposed algorithm.

The Genetic Edge: Revolutionizing Multi-Constraint Fractional Knapsack Solutions

Under linear constraints, a greedy algorithm effectively solves the fractional knapsack problem. However, when additional constraints, such as weight and risk, are added, the complexity of the approach rises. Previous studies have shown that the greedy strategy is optimal under single linear constraints and have provided comprehensive documentation of its efficacy in solving the fractional knapsack problem in straightforward, unconstrained circumstances. Nevertheless, there hasn't been much research done on using greedy algorithms to solve issues with many constraints. This study examines how well genetic algorithms (GAs) perform in comparison to the greedy technique for solving the fractional knapsack problem under conditions with various restrictions. Our results show that, although the greedy method is still efficient in linear or unconstrained circumstances, GAs perform better when dealing with various constraints and provide better solutions in spite of their increased complexity of calculation. This study demonstrates the benefits of using evolutionary algorithms to solve difficult restricted optimization issues when more conventional approaches fall short.

Fault detection of key parts of wind turbine based on BP neural network combination prediction model

A BP neural network incorporated regression forecast technique based upon fragment swarm optimization (PSO) is proposed to design the state of crucial components of wind turbine so regarding realize mistake identification and detection. Firstly, specification recognition is carried out on the collection and tracking information of the system, and parameters connected to fault detection are extracted. Then, the residual optimization issue is made use of to establish the forecast model of nonlinear state evaluation and semantic network combination, and the gearbox temperature level or generator bearing are input as criteria right into the semantic network combination model and single model specifically, and the precision of the design is mirrored by the examination index. Lastly, BP design and PSO-BP combined forecast model are developed respectively by using the actual operation data of wind ranch SCADA, and the mistake state is evaluated according to whether the anticipated residual exceeds the set threshold, so regarding keep an eye on the temperature level of wind turbine transmission and generator bearing. By contrasting the data videotaped prior to and after the failing and making the information prediction analysis, the speculative results show that the forecast model established in this paper is viable for the device element fault detection.

Distributed optimal consensus control for discrete-time multi-agent systems with composite switching topologies via an iterative Q-learning method

This paper aims to achieve distributed optimal consensus control for discrete-time multi-agent systems (MASs) under composite switching topologies through utilising the iterative Q-learning approach. First, the optimal consensus control strategy, independent of system dynamics information, is developed by using the designed iterative Q-learning approach to guarantee that all follower states can synchronise to the desired trajectory formulated by the leader. Compared with the invariable algebraic or edge-fixed topology structures, the designed Q-learning control algorithm in this paper is implemented based on the composite switching topologies consisting of periodic and stochastic mechanisms. Then, an error dynamic system is established, which transforms the optimal consensus control issue into an optimal regulation issue and removes the influence of the two types of switching topologies. To conquer the problems of lack of model information and the inaccessible analytical solution of Hamilton-Jacobi-Isaacs (HJI) equation, an iterative Q-learning approach is designed according to the reconstructed Q-function Bellman equation. Through further iterative learning, the optimal control strategy can be acquired so as to realise consensus control and ensure the stability of the closed-loop systems. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed distributed optimal control scheme.

Groupers and moray eels (GME) optimization: a nature-inspired metaheuristic algorithm for solving complex engineering problems

AbstractAs engineering technology advances and the number of complex engineering problems increases, there is a growing need to expand the abundance of swarm intelligence algorithms and enhance their performance. It is crucial to develop, assess, and hybridize new powerful algorithms that can be used to deal with optimization issues in different fields. This paper proposes a novel nature-inspired algorithm, namely the Groupers and Moray Eels (GME) optimization algorithm, for solving various optimization problems. GME mimics the associative hunting between groupers and moray eels. Many species, including chimpanzees and lions, have shown cooperation during hunting. Cooperative hunting among animals of different species, which is called associative hunting, is extremely rare. Groupers and moray eels have complementary hunting approaches. Cooperation is thus mutually beneficial because it increases the likelihood of both species successfully capturing prey. The two predators have complementary hunting methods when they work together, and an associated hunt creates a multi-predator attack that is difficult to evade. This example of hunting differs from that of groups of animals of the same species due to the high level of coordination among the two species. GME consists of four phases: primary search, pair association, encircling or extended search, and attacking and catching. The behavior characteristics are mathematically represented to allow for an adequate balance between GME exploitation and exploration. Experimental results indicate that the GME outperforms competing algorithms in terms of accuracy, execution time, convergence rate, and the ability to locate all or the majority of local or global optima.

How might model uncertainty and transaction costs impact retained earning & dividend strategies? An examination through a classical insurance risk model

Model uncertainty and ambiguity aversion have important consequences for decision-making under uncertainty in diverse fields such as insurance, finance and economics. Although model uncertainty has been considered in decision-making problems in finance and economics, as well as problems relevant to (re)-insurance, relatively little attention has been given to exploring implications of model uncertainty and ambiguity aversion for the optimal policies governing cash retention and dividend payout. On the other hand, taxes and transaction costs/fees have a significant impact on retained earnings and dividend strategies. Despite its technically challenging, their impacts on optimal dividend strategies have been studied in the literature. However, consequences of model uncertainty and ambiguity aversion for optimal dividend payout policies and related decision-making issues in the presence of transaction costs/taxes have not been well-understood. This paper aims to explore this relatively unknown zone and to articulate this technically challenging problem. Specifically, we shall provide a rigorous approach to examine the impacts of model uncertainty and ambiguity aversion on optimal cash retention and dividend payout strategies with fixed and proportional transaction costs/taxes. Our key findings include (1) model uncertainty and ambiguity aversion change the qualitative behaviour of optimal strategies. Say the optimal strategy is a multi-level lump-sum strategy and tends to have more levels than that of the problem without capturing model uncertainty (2) the value function tends to be rougher (in terms of smoothness) than that of the problem without incorporating model uncertainty.

A strategy for addressing large-scale hinge issues in large two-dimensional planar phased array antennas

In large space structures, the numerous connecting hinges exhibit significant nonlinear characteristics that profoundly impact the structural dynamic properties. The accuracy of hinge parameters is crucial for ensuring the precision of structural dynamic models. This paper addresses the issue of large-scale hinge parameter optimization for a two-dimensional planar phased array antenna structure in space. Firstly, the structure of the antenna is introduced, and dynamic modeling is conducted using the finite element method. Then, a hinge parameter optimization method based on a clustering strategy is proposed, determining the optimal number of clusters and hinge parameters using a genetic algorithm. Subsequently, a piecewise approximation method is employed to handle the nonlinear vibration caused by hinge clearance. Finally, a genetic algorithm optimizes actuator positions, and a combination of LQR and Bang-Bang control algorithms is used for segmented linear vibration active control of the structure. Simulation results demonstrate that the proposed method effectively addresses large-scale hinge uncertainty and nonlinear vibration and control issues arising from hinge clearance.

Dynamic combustion optimization of a pulverized coal boiler considering the wall temperature constraints: A deep reinforcement learning-based framework

Coal-fired power generation boilers are susceptible to low combustion efficiency, pollutant exceedance, and over-temperature tube burst during deep peak shaving due to load fluctuation and mass flow rate variation, which seriously affects unit economics, environmental protection, and safety. In this paper, the issue of boiler combustion optimization under variable load conditions is investigated using a data-driven approach. (1) Based on the published literature, a multi-objective prediction model based on Channel Selection Convolutional Neural Network (CS-CNN) for boiler thermal efficiency, NOx emission and wall temperature was developed, taking into account the safety of the unit under fast variable loads (the range of wall temperature varies widely and is prone to over-temperature). (2) Conventional multi-objective optimization algorithms are time-consuming and hard to satisfy the real-time decision-making of combustion in the furnace. In this research, we developed a combustion decision-making agent based on the Twin Delayed Depth Deterministic Policy Gradient (TD3). By interacting with the predictive model and learning the combustion strategy, the agent has the ability to optimize the unfamiliar operating conditions of the boiler. Simulation experimental tests were carried out on real historical data from a 600 MW down-fired boiler and the results showed that thermal efficiency increased by 0.411 %, and NOx emissions decreased by 17.701 mg/m3, with the safety of the wall temperature maintained. A single decision by the TD3 is almost instantaneous, with an average time of 0.004 s. The decision-making efficiency was much better than the traditional search-based optimization algorithms.

COMPARISON OF CHOSEN METAHEURISTIC ALGORITHMS FOR THE OPTIMIZATION OF THE ABRASIVE WATER JET TREATMENT PROCESS

Abrasive waterjet machining (AWJ) is characterized by significantly better efficiency and better precision for difficult-to-machine materials than conventional machining technologies. However, the larger number of control parameters characterizing this process needs optimization. The study compares the performance of three nature-inspired metaheuristic algorithms, ALO, GWO, and MFO for optimizing the abrasive water jet (AWJ) treatment. The Response Surface Methodology was used to determine the cost function. The study evaluates the convergence and computational cost of the algorithms to aid future developments in this field. The study aims to maximize the cutting thickness by predicting the optimal water-abrasive cutting parameters (nozzle diameter, abrasive concentration, feed speed). For all three algorithms, the maximum cutting depth was determined to be 87.47 mm, which differs only less than 3% from the actual value. The results highlight the potential of ant-lion optimization (ALO), grey wolf optimizer (GWO), and (MFO) moth-flame optimization algorithms for resolving optimization issues in AWJ machining.

Neural Network Methods in the Development of MEMS Sensors.

As a kind of long-term favorable device, the microelectromechanical system (MEMS) sensor has become a powerful dominator in the detection applications of commercial and industrial areas. There have been a series of mature solutions to address the possible issues in device design, optimization, fabrication, and output processing. The recent involvement of neural networks (NNs) has provided a new paradigm for the development of MEMS sensors and greatly accelerated the research cycle of high-performance devices. In this paper, we present an overview of the progress, applications, and prospects of NN methods in the development of MEMS sensors. The superiority of leveraging NN methods in structural design, device fabrication, and output compensation/calibration is reviewed and discussed to illustrate how NNs have reformed the development of MEMS sensors. Relevant issues in the usage of NNs, such as available models, dataset construction, and parameter optimization, are presented. Many application scenarios have demonstrated that NN methods can enhance the speed of predicting device performance, rapidly generate device-on-demand solutions, and establish more accurate calibration and compensation models. Along with the improvement in research efficiency, there are also several critical challenges that need further exploration in this area.

Modeling and Solving the Knapsack Problem with a Multi-Objective Equilibrium Optimizer Algorithm Based on Weighted Congestion Distance

The knapsack problem is a typical bi-objective combinatorial optimization issue, wherein maximizing the value of the packed items is achieved concurrently with minimizing the weight of the load. Due to the conflicting objectives of the knapsack problem and the typical discrete property of the items involved, swarm intelligence algorithms are commonly employed. The diversity of optimal combinations in the knapsack problem has become a focal point, which involves finding multiple packing solutions at the same value and weight. For this purpose, this paper proposes a Multi-Objective Equilibrium Optimizer Algorithm based on Weighted Congestion Distance (MOEO_WCD). The algorithm employs a non-dominated sorting method to find a set of Pareto front solutions rather than a single optimal solution, offering multiple decision-making options based on the varying needs of the decision-makers. Additionally, MOEO_WCD improves the balance pool generation mechanism and incorporates a weighted congestion incentive, emphasizing the diversity of packing combination solutions under the objectives of value and weight to explore more Pareto front solutions. Considering the discrete characteristics of the knapsack combination optimization problem, our algorithm also incorporates appropriate discrete constraint handling. This paper designs multiple sets of multi-objective knapsack combinatorial optimization problems based on the number of knapsacks, the number of items, and the weights and values of the items. This article compares five algorithms suitable for solving multi-objective problems: MODE, MO-PSO-MM, MO-Ring-PSO-SCD, NSGA-II, and DN-NSGAII. In order to evaluate the performance of the algorithm, this paper proposes a new solution set coverage index for evaluation. We also used the hypervolume indicator to evaluate the diversity of algorithms. The results show that our MOEO-WCD algorithm achieves optimal coverage of the reference composite Pareto front in the decision space of four knapsack problems. The experimental results indicate that our MOEO_WCD algorithm achieves the optimal coverage of the reference composite Pareto front in the decision space for four sets of knapsack problems. Although our MOEO_WCD algorithm covers less of the composite front in the objective space compared with the MODE algorithm for knapsack problem 1, its coverage of the integrated reference solutions in the decision space is greater than that of the MODE algorithm. The experiments demonstrate the superior performance of the MOEO_WCD algorithm on bi-objective knapsack combinatorial optimization problem instances, which provides an important solution to the search for diversity in multi-objective combinatorial optimization solutions.

Hybrid energy system optimization integrated with battery storage in radial distribution networks considering reliability and a robust framework

This research presents a robust optimization of a hybrid photovoltaic-wind-battery (PV/WT/Batt) system in distribution networks to reduce active losses and voltage deviation while also enhancing network customer reliability considering production and network load uncertainties. The best installation position and capacity of the hybrid system (HS) are found via an improved crow search algorithm with an inertia weight technique. The robust optimization issue, taking into account the risk of uncertainty, is described using the gap information decision theory method. The proposed approach is used with 33- and 69-bus networks. The results reveal that the HS optimization in the network reduces active losses and voltage variations, while improving network customer reliability. The robust optimization results show that in the 33-bus network, the system remains resilient to prediction errors under the worst-case uncertainty scenario, with a 44.53% reduction in production and a 22.18% increase in network demand for a 30% uncertainty budget. Similarly, in the 69-bus network, the system withstands a 36.22% reduction in production and a 16.97% increase in load for a 25% uncertainty budget. When comparing stochastic and robust methods, it was found that the stochastic Monte Carlo method could not consistently provide a reliable solution for all objectives under uncertainty, whereas the robust approach successfully managed the maximum uncertainty related to renewable generation and network demand across different uncertainty budgets.

Unlocking the potential of copper-reinforced fibre metal laminates: The influence of stacking sequences

Fibre-Metal Laminates (FMLs) are a class of advanced hybrid materials consisting of a metal layer and fibre-reinforced polymers that give an excellent combination of mechanical properties. The present study deals with the development and characterization of copper-based FMLs developed through the hand layup method by considering multiple stacking sequences for improved mechanical properties and by incorporating carbon nanotubes. Extensive tensile, flexural, fatigue, and wear tests were carried out on the FMLs. SEM analysis was also done to investigate the fracture. According to the test results, in the Type III stacking sequence where optimum 0.5% CNT content is used, the best overall tensile strength together with good flexibility, fatigue resistance, and wear resistance could be obtained. The Type III stacking sequence exhibited a maximum tensile strength of 117.85 MPa, flexural strength of 168.0 MPa, and fatigue life of 22,00,000 cycles at 50 MPa stress amplitude. Further SEM analysis has shown that enhancement in microstructural integrity actually occurred with the inclusion of CNT, especially in fiber-matrix bonding improvement and reduction of voids. The results obtained in this work indicate the suitable possibility for application in high-performance aerospace and automotive industries of copper-based FMLs. This study highlights optimization issues related to both material composition and stacking sequence toward the attainment of superior mechanical properties in FMLs.