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The unreacted equation of state (EOS) for an unreacted explosive can provide fundamental information to understand any analytical model for the shock and initiation process. Based on the Hugoniot expression in Jones–Wilkins–Lee (JWL) form derived from the Mie–Grüneisen EOS and conservation equation across the shock wave, a three-point calibrating method to determine the JWL EOS parameters for unreacted explosives was developed using intelligent algorithms and shock Hugoniot relationship of the explosives considered. The calibration method proposed utilizes the back propagation neural network to predict the nonlinear system composed of different JWL parameter sets; the genetic algorithm is then used to find the optimal solution of the JWL parameter set. Unreacted JWL EOS parameters of eight typical explosives were calibrated using the calibrating method developed, and an excellent agreement can be observed between JWL EOS and experimental p–v curves for all eight explosives selected, indicating the high accuracy of the three-point calibrating method. However, the effectiveness of the three-point calibrating method was experimentally validated with the experimental data measured from the shock tests of the dihydroxylammonium 5,5′-bitetrazole-1,1′-dioxide (TKX-50)-based explosive, where the JWL p–v curve derived from the three-point calibrating method is in good agreement with the experimental curve. [ABSTRACT FROM AUTHOR]

Demand-Driven Material Requirements Planning (DDMRP) is a recent inventory management method that has generated considerable interest in both academia and industry. Many recent papers have demonstrated the superiority of DDMRP over classical methods like MRP or Kanban, an observation confirmed by companies that have implemented DDMRP. However, DDMRP depends on many parameters that affect its performance. Only general rules are given by the authors of the method to fix these parameters but no algorithm. The present paper aims to fill this gap by proposing a multi-objective optimisation algorithm to fix a set of eight identified parameters. The suggested genetic algorithm is coupled with a simulation algorithm that computes the objective functions. Two opposing objective functions are considered: first, the maximisation of orders delivered on-time to the customer and, second, the minimisation of on-hand inventory. A set of data instances was generated to test the suggested method. Fronts of non-dominated solutions are found for all these instances. [ABSTRACT FROM AUTHOR]

Data mining and machine learning require feature selection because features can dramatically improve model performance. In contrast, there are no polynomial solutions for selecting a subset feature. It is possible to achieve this by using meta-heuristic algorithms, specifically population-based algorithms that are able to provide a subset of features that is optimal and not exact. Meta-heuristic algorithms face challenges such as staying in local minima, easily falling into local optimum, weakly global searchability, premature convergence, and slow convergence speeds. However, recent research has limitations such as high complexity and weak initialization. In order to overcome these limitations, a three-stage model is proposed. In the first stage, the correlation of features and the correlation of features with class are considered during feature selection and used to create the initial population in the pathfinder optimization algorithm (PFA). PFA is a population-based algorithm and has some drawbacks, in the last iterations, the fluctuation rate (A) and vibration vector (ε) parameters converge to 0, and finding a new solution is impossible. As a second stage, a fuzzy inference system is designed to adjust these parameters adaptively and is called fuzzy-pathfinder optimization (FPO). In the third stage, FPO is used to select relevant features based on classification error, proportion of selected features, and redundancy. Finally, different algorithms such as simulated annealing (SA), differential evolutionary (DE), genetic algorithm (GA), particle swarm optimization (PSO), PFA, estimation of distribution algorithm (EDA), and symmetrical uncertainty criterion (SUC-PSO) are used for comparison. Based on the results, the proposed model is able to reach an average accuracy of 96% on average. Based on a comparison of the proposed algorithm with SA, DE, GA, PSO, PFA, EDA, and SUC-PSO, the objective function is improved by 17.3%, 5.6%, 3.0%, 4.5%, 5.0%, 0.5%, and 1.2%, respectively. The use of comprehensive objective functions, the adaptive adjustment of parameters, and the creation of a targeted initial population are key strengths of FPO. [ABSTRACT FROM AUTHOR]

In this paper, the multi-objective optimization problem (MOOP) of selecting suitable materials from four materials for phononic crystals layouts is investigated based on an optimization method combined nondominated sorting-based genetic algorithm II (NSGA-II) and finite element method (FEM). Driven by different optimization objectives, the changes in the optimized structure compared to the seed structure are the change of the bandgap mechanism from a local resonance mechanism to a Bragg scattering mechanism, the change of the included materials from four to three, and the significant change in the location and extent of the pores and other materials, respectively. The obtained nondominated Pareto solution of MOOP can balance the bandgap and the structural mass, which provides the decision-maker trade-off to select the appropriate optimized solution. Compared with the single-objective optimization problem (SOOP), the MOOP not only obtains a nondominated solution close to the result of SOOP, but also obtains other nondominated solutions of MOOP, which proves the effectiveness of the optimization algorithm in this paper. The design method in this paper can be easily extended to select suitable materials from a wider variety of materials for bandgap optimization design of PnCs, which has very promising applications. [ABSTRACT FROM AUTHOR]

This paper presents a novel implementation of the Coral Reef Optimization with Substrate Layers (CRO‐SL) algorithm. Our approach, which we call TensorCRO, takes advantage of the TensorFlow framework to represent CRO‐SL as a series of tensor operations, allowing it to run on GPU and search for solutions in a faster and more efficient way. We evaluate the performance of the proposed implementation across a wide range of benchmark functions commonly used in optimization research (such as the Rastrigin, Rosenbrock, Ackley, and Griewank functions), and we show that GPU execution leads to considerable speedups when compared to its CPU counterpart. Then, when comparing TensorCRO to other state‐of‐the‐art optimization algorithms (such as the Genetic Algorithm, Simulated Annealing, and Particle Swarm Optimization), the results show that TensorCRO can achieve better convergence rates and solutions than other algorithms within a fixed execution time, given that the fitness functions are also implemented on TensorFlow. Furthermore, we also evaluate the proposed approach in a real‐world problem of optimizing power production in wind farms by selecting the locations of turbines; in every evaluated scenario, TensorCRO outperformed the other meta‐heuristics and achieved solutions close to the best known in the literature. Overall, our implementation of the CRO‐SL algorithm in TensorFlow GPU provides a new, fast, and efficient approach to solving optimization problems, and we believe that the proposed implementation has significant potential to be applied in various domains, such as engineering, finance, and machine learning, where optimization is often used to solve complex problems. Furthermore, we propose that this implementation can be used to optimize models that cannot propagate an error gradient, which is an excellent choice for non‐gradient‐based optimizers. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach to address the optimal coordination of directional overcurrent relays (DOCRs) in modern power distribution networks. By utilizing the different optimization methods such as genetic algorithm (GA), particle swarm optimization (PSO), pattern search (PS), grey wolf optimization (GWO), the study aims to tackle the inherent complexity and nonlinearity of the relay coordination problem effectively. GWO stands out due to its ability to handle highly nonlinear optimization problems by leveraging the social behavior and hunting mechanisms of grey wolves and its ability to quickly converge to near-optimal solutions make it a popular choice. This unique feature enables the algorithm to explore the solution space more efficiently by repositioning solutions around each other, thereby facilitating better exploitation of the solution space. The effectiveness of the proposed GWO algorithm is evaluated using fault data generated from various test systems ranging from small-scale 8-bus networks to large 15-bus systems. The results demonstrate several key advantages, reduced operating time, robust coordination, and reduced coordination interval. Compared to other optimization algorithms, the GWO algorithm achieves a reduced coordination interval between primary and backup relay pairs. This optimization contributes to faster and more precise fault detection and isolation within the network in comparison to other techniques. Overall, the findings highlight the superior performance and robustness of the GWO algorithm in addressing the optimal coordination challenges of DOCRs in modern power distribution networks thereby enhancing the efficiency and reliability of protection systems in complex electrical grids. [ABSTRACT FROM AUTHOR]

This work presents a unique technique for optimising composite laminates used as structural components, which is critical for situations where failure might result in disastrous effects. Unlike traditional surrogate‐based optimisation approaches, this methodology combines the accurate modelling capabilities of finite element (FE) analysis with the iterative refining capacity of metaheuristic algorithms. By combining these two methodologies, our method intends to improve the design process of laminated shell structures, assuring robustness and dependability is crucial. Compared to existing benchmark solutions, the current FE shows a <1% error for cylindrical and spherical shells. The prime objective of this study is to identify the optimum ply angles for attaining a high fundamental frequency. The problem is NP‐hard because the possible ply angles span a wide range (±90°), making it difficult for optimisation algorithms to find a solution. Seven popular metaheuristic algorithms, namely the genetic algorithm (GA), the ant lion optimisation (ALO), the arithmetic optimisation algorithm (AOA), the dragonfly algorithm (DA), the grey wolf optimisation (GWO), the salp swarm optimisation (SSO), and the whale optimisation algorithm (WOA), are applied to and compared on a wide range of shell design problems. It assesses parameter sensitivity, discovering significant design elements that influence dynamic behaviour. Convergence studies demonstrate the superior performance of AOA, GWO, and WOA optimisers. Rigorous statistical comparisons assist practitioners in picking the best optimisation technique. FE‐GWO, FE‐DA, and FE‐SSA methods surpass the other techniques as well as the layerwise optimisation strategy. The findings obtained, employing the GWO, DA, and SSA optimisers, demonstrate ~3% improvement over the existing literature. With respect to conventional layup designs (cross‐ply and angle‐ply), the current optimised designs are better by at least 0.43% and as much as 48.91%. [ABSTRACT FROM AUTHOR]

To improve the economic benefits of river dredging engineering construction, studies have been undertaken to optimize construction period costs. This study suggests a scheme for optimizing schedule costs through the use of three algorithms: non-dominated sorting genetic algorithm with elite strategy, simulated annealing colony algorithm, and ant colony algorithm. To achieve the preliminary algorithm selection of construction duration cost, the objectives have single and multi-objective, and iterative models are constructed separately. The validation results showed that the simulated annealing algorithm achieved the optimal solution in single objective optimization after the 81st iteration. The optimal solution of genetic algorithm in multi-objective optimization was a construction period of 49 days and a cost of 1788.15 million yuan. The non-dominated algorithm reduced the construction period to 313 days, which can save 52 days of construction period and reduce costs by 52.32 million yuan. This optimization algorithm has high efficiency in predicting shorter construction periods and lower costs, and has strategic foresight in the decision plans of decision-makers. [ABSTRACT FROM AUTHOR]

This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers. A mathematical model for the parallel disassembly line is established to achieve maximized disassembly profit and minimized workstation cycle time. Based on a product's AND/OR graph, matrices for task-skill, worker-skill, precedence relationships, and disassembly correlations are developed. A multi-objective discrete chemical reaction optimization algorithm is designed. To enhance solution diversity, improvements are made to four reactions: decomposition, synthesis, intermolecular ineffective collision, and wall invalid collision reaction, completing the evolution of molecular individuals. The established model and improved algorithm are applied to ball pen, flashlight, washing machine, and radio combinations, respectively. Introducing a Collaborative Resource Allocation (CRA) strategy based on a Decomposition-Based Multi-Objective Evolutionary Algorithm, the experimental results are compared with four classical algorithms: MOEA/D, MOEAD-CRA, Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Non-dominated Sorting Genetic Algorithm III (NSGA-III). This validates the feasibility and superiority of the proposed algorithm in parallel disassembly production lines. [ABSTRACT FROM AUTHOR]

The signal strength in 5G mobile communication systems is significantly influenced by the surroundings, with key factors including the path difference, operating frequency, and obstructions at specific locations. Consequently, planning a communication system that can deliver improved signal strength becomes highly challenging. To address this issue, indoor path loss models are employed to estimate signal loss in different environments, frequencies, and distances. This paper introduces an intelligent multi-objective genetic algorithm aimed at enhancing path loss and received signal power. A comparative analysis is conducted to evaluate the performance of the proposed intelligent optimization algorithm against the traditional approach. The path loss and received power of various scenarios are estimated using various path loss models. The 5GCM indoor officce, 5GCM InH shopping mall, 3GPP TR 38.91 InH office, mmMAGIC InH office, METIS InH shopping mall, and IEEE 802.11 ad InH office indoor path loss models estimates the path loss of 62.37 dB, 62.15 dB, 63.12 dB, 50 dB, 55.18 dB, and 52.89 dB in traditional approach and 36.87 dB, 35.86 dB, 36.84 dB, 68.80 dB, 36.23 dB and 33.94 dB using GA approach and received powers of - 12.17 d B m , - 11.37 d B m , - 12.17 d B m , - 5.80 d B m , - 12.24 d B m and - 8.68 d B m in traditional approach and 26.13 dBm, 27.14 dBm, 26.15 dBm, - 5.80 d B m , 26.75 dBm and 29.05 dBm using GA approach repectively. The 5GCM and 3GPP models produces the path loss difference above 25 dB and mmMAGIC, METIS and IEEE models produces a path loss below 19 dB. Except mmMAGIC model, all models produces the recceiver power difference above 37 dBm. Therefore, the highest path loss difference of 26 dB is observed in 5GCM InH shopping mall model and the highest reccieved power difference of 39.01 dBm is observed in METIS InH shopping mall model. The results clearly demonstrate that the proposed intelligent optimization approach outperforms the traditional approach across various indoor scenarios. [ABSTRACT FROM AUTHOR]

Continuous carbon fiber-reinforced polymer (CFRP) can be used in material extrusion-based additive manufacturing (AM). By appropriately arranging and orienting the continuous fibers, lightweight or high-strength mechanical parts and structures with complex deformation behavior and locally modified stiffness can be fabricated. Although many studies have been conducted to optimize the arrangement of continuous fibers fabricated using CFRP in AM, most of them have focused on the mechanical properties of the fabricated object in the lamination plane. This focus is due to the characteristics of AM, in which continuous fibers are placed in a plane and then layered, allowing for optimization at a relatively low computational cost. However, the computational cost of targeting mechanical properties outside the fabrication plane is enormous, making optimization design difficult. Furthermore, if the fiber material is arranged discontinuously, a process to cut the fibers is required during fabrication, resulting in decreased productivity and fabrication accuracy. Therefore, it is necessary to optimize the fiber arrangement, considering the continuity of the fiber material. To address these problems, this study aims to propose an efficient fiber arrangement optimization method, considering the continuity of the fiber material. For efficient stiffness optimization, the stiffness is represented by a combination of lamination parameters that have been used in the lamination design of CFRP sheets. A genetic algorithm was employed as the optimization algorithm. The proposed method using lamination parameters was implemented, and a case study of fiber arrangement optimization was performed on a simple structure. In addition, a full search was performed to evaluate all possible fiber arrangements for the target structure. The results of the proposed method and the full search confirmed the reliability of the proposed method, which achieved results that were equivalent to the best results obtained in the full search. In addition, a conventional method that directly optimizes the fiber arrangement as a design parameter was implemented. This result was compared with that of the proposed method. For a simple structure with a small number of layers, averaged over 20 runs, the conventional method converged faster than the proposed method, but the convergence speed worsened as the number of layers increased. Moreover, the fiber arrangement obtained by the conventional method was less continuous than the result of the proposed method. These results confirm the usefulness of the proposed method. [ABSTRACT FROM AUTHOR]

Throughout history, maritime transportation has been preferred for international and intercontinental trade thanks to its lower cost than other transportation ways, which have a risk of ship accidents. To avoid these risks, underwater wireless sensor networks can be used as a robust and safe solution by monitoring maritime environment where energy resources are critical. Energy constraints must be solved to enable continuous data collection and communication for environmental monitoring and surveillance so they can last. Their energy limitations and battery replacement difficulties can be addressed with a path planning approach.This paper considers the energy-aware path planning problem with autonomous underwater vehicles by five commonly used approaches, namely, Ant Colony Optimization-based Approach, Particle Swarm Optimization-based Approach, Teaching Learning-based Optimization-based Approach, Genetic Algorithm-based Approach, Grey Wolf Optimizer-based Approach. Simulations show that the system converges faster and performs better with genetic algorithm than the others. This paper also considers the case where direct traveling paths between some node pairs should be avoided due to several reasons including underwater flows, too narrow places for travel, and other risks like changing temperature and pressure. To tackle this case, we propose a modified genetic algorithm, the Safety-Aware Genetic Algorithm-based Approach, that blocks the direct paths between those nodes. In this scenario, the Safety-Aware Genetic Algorithm-based approach provides just a 3% longer path than the Genetic Algorithm-based approach which is the best approach among all these approaches. This shows that the Safety-Aware Genetic Algorithm-based approach performs very robustly. With our proposed robust and energy-efficient path-planning algorithms, the data gathered by sensors can be collected very quickly with much less energy, which enables the monitoring system to respond faster for ship accidents. It also reduces total energy consumption of sensors by communicating them closely and so extends the network lifetime. [ABSTRACT FROM AUTHOR]

Wireless sensor networks contain of many sensors that can serve as powerful tools for data collection in environments. A key challenge in these networks is the limited lifetime of sensor batteries. Ideally, all nodes would exhaust their energy simultaneously or through regular scheduling, maximizing the lifetime. Consequently, the primary concern is achieving optimal energy utilization to extend the network's lifetime over a logical duration. Depleting the batteries of the sensors means stopping the operation of the network, because it is practically impossible to replace the batteries of thousands of nodes. To address this issue, the low energy adaptive clustering hierarchical (LEACH) protocol has been widely recognized as one of the prominent solutions for clustering WSNs. However, the random selection of cluster heads in each round under the LEACH protocol fails to guarantee proper convergence. To overcome this limitation, this paper proposes a refined approach by utilizing a genetic algorithm and a novel objective function that incorporates various factors, including energy level and distance. The algorithm employs chromosomes to represent CHs and facilitates the selection of cluster nodes. Notably, the proposed algorithm dynamically performs clustering, meaning that clustering is conducted iteratively, considering identifying dead nodes. By leveraging this approach, the algorithm significantly enhances the clustering quality, ultimately leading to an increased network lifetime. To validate its effectiveness, it is compared with LEACH, LEACH_E and LEACH_EX algorithms, demonstrating its superior capabilities. On average, the proposed algorithm has more alive nodes in the network, and the remaining energy is at least 11% higher than the best other algorithms. [ABSTRACT FROM AUTHOR]

To enhance the performance of meta-heuristic algorithms, the development of new operators and the efficient combination of various optimization techniques are valuable strategies for discovering global optimal solutions. In this research endeavor, we introduce a novel optimization algorithm called PGS (Particle Swarm Optimization-GA-Sliding Surface). PGS combines the strengths of particle swarm optimization (PSO), genetic algorithm (GA), and sliding surface (SS) to tackle both mathematical test functions and real-world optimization problems. To achieve this, we adaptively tune the weighting function and learning coefficients of the PSO algorithm using the sliding mode control's SS relation. The global best particle discovered through the PSO method serves as one of the parents in the GA's crossover operation. This new crossover operator is then probabilistically integrated with an improved particle swarm optimization algorithm, enhancing convergence speed and facilitating escape from local optima. We evaluate the proposed algorithm's performance on both uni-modal and multi-modal mathematical test functions, considering un-rotated and rotated cases, thereby testing its effectiveness and efficiency against other prominent optimization techniques. Furthermore, we successfully implement the PGS algorithm in optimizing the state feedback controller for a nonlinear quadcopter system and determining the cross-section for an inelastic compression member. [ABSTRACT FROM AUTHOR]

In order to comprehensively measure these two indicators and make reasonable portfolio investment decisions, the author proposes using swarm intelligence optimization algorithm - artificial fish swarm algorithm to solve multi-objective investment portfolio problems, and has achieved good results. In order to verify the effectiveness and superiority of the artificial fish school algorithm, the author used MATLAB programming to conduct simulation experiments using AFSA algorithm and genetic algorithm (GA), and compared the results obtained. The results show that compared to the GA algorithm, the artificial fish school algorithm can obtain better investment portfolio decision-making solutions for investing in five types of assets, making investment returns as large as possible while minimizing risks, indicating the efficiency and superiority of the algorithm in solving multi-objective investment portfolio problems. [ABSTRACT FROM AUTHOR]

Summary: Low Earth Orbit (LEO) satellite‐based Internet of Things (IoT) has become a hot topic in IoT networks due to the ability of global coverage, especially in remote areas. How to design a commercial LEO satellite‐based constellation to meet the IoT traffic requirement remains an open problem. In this paper, we propose a throughput optimization algorithm for LEO satellite‐based IoT networks meanwhile reducing the number of LEO satellite. Based on stochastic geometry theory, a closed‐form expression is derived for the throughput of a dynamic LEO satellite‐based IoT networks when LEO satellite equips with capture effect (CE) receiver and successive interference cancelation (SIC) receiver, respectively. Furthermore, a joint altitude and beamwidth optimization problem is formulated under the constraint of Walker constellation to optimize the throughput and the number of LEO satellites. To solve this multi‐objective optimization problem, we design an iterative non‐dominated sorting genetic algorithm II (NSGA‐II) for the rapid development of IoT traffic. Simulation results show that our proposed algorithm can effectively improve the throughput performance of random access (RA) protocol in LEO satellite‐based IoT networks compared to benchmark problems. [ABSTRACT FROM AUTHOR]

Optimization challenges effectively mirror numerous complex real‐world problems. These are typically addressed using two methodologies: approximate methods and exact methods. Within the approximate category, meta‐heuristic methods are prominent. These algorithms employ systematic principles to avoid local optima or prevent their occurrence altogether. The evolution of computing technology has spurred the development of various meta‐heuristic algorithms designed for precise and expedient resolution of optimization issues. This paper conducts a comparative analysis of meta‐heuristic algorithms applied to structural optimization. It encompasses 11 distinct meta‐heuristic optimization algorithms: atomic orbital search algorithm, chaos game optimization, energy valley optimizer, firefly algorithm, genetic algorithm, gray wolf optimization, harmony search, material generation algorithm, particle swarm optimization, social network search, and whale optimization algorithm. The efficacy of these algorithms is assessed through their application to structural problems such as trusses, cantilever‐cracked beams, welded beams, tubular columns, and I‐shaped beams. Evaluation criteria include computational efficiency, accuracy, and statistical analysis based on reports among the 50 iterations. The findings confirm the effectiveness of the algorithms, with particle swarm optimization and social network search frequently delivering superior results across most problem sets. [ABSTRACT FROM AUTHOR]

A multiobjective optimization model for energy‐saving design of green buildings is established by considering the two key indicators (energy efficiency and comfort) that are important for the design of green buildings. Using the energy consumption simulation software EnergyPlus to evaluate the fitness value of individuals, a multiobjective evolutionary optimization algorithm based on decomposition is used to optimize the above model. A multiobjective evolutionary optimization algorithm and its execution method are proposed for building energy efficiency, which integrates EnergyPlus. Taking a common multiroom residential building in northern China as an example, a multiobjective optimization is carried out. The results show that compared to classic intelligent optimization design algorithms such as nondominated sorting genetic algorithm II (NSGA‐II), the proposed method reduces discomfort time by 1.29% while only increasing energy consumption by 0.61%. [ABSTRACT FROM AUTHOR]

Biomass supply chain (BSC) activities have caused social and environmental disruptions, such as climate change, energy security issues, high energy demand, and job opportunities, especially in rural areas. Moreover, different economic problems have arisen globally in recent years (e.g., the high costs of BSC logistics and the inefficiency of generating bioenergy from low-energy-density biomass). As a result, numerous researchers in this field have focused on modeling and optimizing sustainable BSC. To this end, this study aims to develop a multi-objective mathematical model by addressing three sustainability pillars (economic cost, environmental emission, and job creation) and three decision levels (i.e., strategic (location of facilities), tactical (type of transportation and routing), and operational (vehicle planning). A palm oil BSC case study was selected in the context of Malaysia in which two advanced evolutionary algorithms, i.e., non-dominated sorting genetic algorithm II (NSGA-II) and Multiple Objective Particle Swarm Optimization (MOPSO), were implemented. The study results showed that the highest amounts of profit obtained from the proposed supply chain (SC) design were equal to $13,500 million and $7000 million for two selected examples with maximum emissions. A better target value was achieved in the extended example when 40% profit was reduced, and the minimum emissions from production and transportation in the BSC were attained. In addition, the results demonstrate that more Pareto solutions can be obtained using the NSGA-II algorithm. Finally, the technique for order of preference by similarity to the ideal solution (TOPSIS) was adopted to balance the optimum design points obtained from the optimization algorithm solutions through two-objective problems. The results indicated that MOPSO worked more efficiently than NSGA-II, although the NSGA-II algorithm succeeded in generating more Pareto solutions. [ABSTRACT FROM AUTHOR]

With the increasing aging of the global population, the efficiency and accuracy of the elderly monitoring system become crucial. In this paper, a sensor layout optimization method, the Fusion Genetic Gray Wolf Optimization (FGGWO) algorithm, is proposed which utilizes the global search capability of Genetic Algorithm (GA) and the local search capability of Gray Wolf Optimization algorithm (GWO) to improve the efficiency and accuracy of the sensor layout in elderly monitoring systems. It does so by optimizing the indoor infrared sensor layout in the elderly monitoring system to improve the efficiency and coverage of the sensor layout in the elderly monitoring system. Test results show that the FGGWO algorithm is superior to the single optimization algorithm in monitoring coverage, accuracy, and system efficiency. In addition, the algorithm is able to effectively avoid the local optimum problem commonly found in traditional methods and to reduce the number of sensors used, while maintaining high monitoring accuracy. The flexibility and adaptability of the algorithm bode well for its potential application in a wide range of intelligent surveillance scenarios. Future research will explore how deep learning techniques can be integrated into the FGGWO algorithm to further enhance the system's adaptive and real-time response capabilities. [ABSTRACT FROM AUTHOR]

In modern manufacturing, optimization algorithms have become a key tool for improving the efficiency and quality of machining technology. As computing technology advances and artificial intelligence evolves, these algorithms are assuming an increasingly vital role in the parameter optimization of machining processes. Currently, the development of the response surface method, genetic algorithm, Taguchi method, and particle swarm optimization algorithm is relatively mature, and their applications in process parameter optimization are quite extensive. They are increasingly used as optimization objectives for surface roughness, subsurface damage, cutting forces, and mechanical properties, both for machining and special machining. This article provides a systematic review of the application and developmental trends of optimization algorithms within the realm of practical engineering production. It delves into the classification, definition, and current state of research concerning process parameter optimization algorithms in engineering manufacturing processes, both domestically and internationally. Furthermore, it offers a detailed exploration of the specific applications of these optimization algorithms in real-world scenarios. The evolution of optimization algorithms is geared towards bolstering the competitiveness of the future manufacturing industry and fostering the advancement of manufacturing technology towards greater efficiency, sustainability, and customization. [ABSTRACT FROM AUTHOR]

Due to the dynamic complexities of the multi-unmanned vessel target assignment problem at sea, especially when addressing moving targets, traditional optimization algorithms often fail to quickly find an adequate solution. To overcome this, we have developed a multi-agent reinforcement learning algorithm. This approach involves defining a state space, employing preferential experience replay, and integrating self-attention mechanisms, which are applied to a novel offshore unmanned vessel model designed for dynamic target allocation. We have conducted a thorough analysis of strike positions and times, establishing robust mathematical models. Additionally, we designed several experiments to test the effectiveness of the algorithm. The proposed algorithm improves the quality of the solution by at least 30% in larger scale scenarios compared to the genetic algorithm (GA), and the average solution speed is less than 10% of the GA, demonstrating the feasibility of the algorithm in solving the problem. [ABSTRACT FROM AUTHOR]

The application of educational big data analytics holds significant importance in enhancing decision-making processes for school teaching quality. This study explores the effective utilization of educational big data analytics technologies to support the improvement of teaching quality in schools. Initially, the challenges and needs faced by current school teaching quality decision-making were analyzed, highlighting the critical role of educational big data analytics in this context. Subsequently, the limitations and gaps in existing study were identified through a review of related studies, underscoring the study value of this study. Based on this foundation, this study progresses through an examination of the decision-making factors that influence school teaching quality, problem description and model assumptions, construction of decision models, and model solutions using genetic algorithms. By analyzing key factors and constraints in the decision-making process for school teaching quality and integrating optimization algorithms, a viable decision support model was proposed and empirically analyzed. This study aims to provide a scientific basis for school administrators and decision-makers, thereby promoting continuous improvement in school teaching quality [ABSTRACT FROM AUTHOR]

The purpose of this research is to develop a new method for automatically optimizing the seawall cross-section with composite slopes and a berm, considering both overtopping discharge and construction cost. Minimizing these competing multi-objectives is highly challenging due to the intricate geometry of seawalls. In this study, the surrogate model optimization algorithm DYCORS (Dynamic COordinate search using Response Surface models) is employed to search for the optimal seawall geometry, coupled with the ANN (Artificial Neural Network) model for determining the overtopping discharge. A total of 20 trials have been run to evaluate the performance of our methodology. Even the worst-performing Trial 7 among these 20 trials shows a satisfactory performance, with a reduction of 17.67% in overtopping discharge and a 12.1% decrease in cost compared to the original solution. Furthermore, compared to other optimization schemes using GAs (Genetic Algorithms) with the same decision vectors, constraints, and multi-objective functions, the methodology has been proven to be more effective and robust. Additionally, when facing different combinations of wave conditions and water levels, there was a 27.8% reduction in objective function value compared to the original solution. The optimal results indicate that this method can still be effectively applied for optimizing the seawall cross-section as it is a general method. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms are computational techniques based on the collective behavior of swarms and the study of organisms acting in communities. These algorithms involve different types of organisms. Finding controller values for nonlinear systems is a challenging task using classical approaches. Hence, using metaheuristics to find the controller values of a twin rotor multi-input multi-output system (TRMS), one of the nonlinear systems studied in the literature, seems to be more appropriate than using classical methods. In this study, different types of metaheuristic algorithms were used to find the PID controller values for a TRMS, including a genetic algorithm (GA), a dragonfly algorithm, a cuckoo algorithm, a particle swarm optimization (PSO) algorithm, and a coronavirus optimization algorithm (COVIDOA). The obtained graphs were analyzed based on certain criteria for the main rotor and tail rotor angles to reach the reference value in the TRMS. The experimental results show that when the rise and settlement times of the TRMS are compared in terms of performance, the GA took 1.5040 s (seconds) and the COVIDOA took 9.59 s to increase the pitch angle to the reference value, with the GA taking 0.7845 s and the COVIDOA taking 2.4950 s to increase the yaw angle to the reference value. For the settling time, the GA took 11.67 s and the COVIDOA took 28.01 s for the pitch angle, while the GA took 14.97 s and the COVIDOA took 26.69 s for the yaw angle. With these values, the GA and COVIDOA emerge as the foremost algorithms in this context. [ABSTRACT FROM AUTHOR]

Wind farm layout optimization (WFLO) is focused on utilizing algorithms to devise a more rational turbine layout, ultimately maximizing power generation efficiency. Traditionally, genetic algorithms have been frequently employed in WFLO due to the inherently discrete nature of the problem. However, in recent years, researchers have shifted towards enhancing continuous optimization algorithms and incorporating constraints to address WFLO challenges. This approach has shown remarkable promise, outperforming traditional genetic algorithms and gaining traction among researchers. To further elevate the performance of continuous optimization algorithms in the context of WFLO, we introduce a multi-local search-based SHADE, termed MS-SHADE. MS-SHADE is designed to fine-tune the trade-off between convergence speed and algorithmic diversity, reducing the likelihood of convergence stagnation in WFLO scenarios. To assess the effectiveness of MS-SHADE, we employed a more extensive and intricate wind condition model in our experiments. In a set of 16 problems, MS-SHADE's average utilization efficiency improved by 0.14% compared to the best algorithm, while the optimal utilization efficiency increased by 0.3%. The results unequivocally demonstrate that MS-SHADE surpasses state-of-the-art WFLO algorithms by a significant margin. [ABSTRACT FROM AUTHOR]

Ballistic impacts on human thorax without penetration can produce severe injuries or even death of the carrier. Soft tissue finite element models must capture the non-linear elasticity and strain-rate dependence to accurately estimate the dynamic human mechanical response. The objective of this work is the calibration of a visco-hyperelastic model for soft tissue simulants. Material model parameters have been calculated by fitting experimental stress–strain relations obtained from the literature using genetic algorithms. Several parametric analyses have been carried out during the definition of the optimization algorithm. In this way, we were able to study different optimization strategies to improve the convergence and accuracy of the final result. Finally, the genetic algorithm has been applied to calibrate two different soft tissue simulants: ballistic gelatin and styrene–ethylene–butylene–styrene. The algorithm is able to calculate the constants for visco-hyperelastic constitutive equations with high accuracy. Regarding synthetic stress–strain curves, a short computational time has been shown when using the semi-free strategy, leading to high precision results in stress–strain curves. The algorithm developed in this work, whose code is included as supplementary material for the reader use, can be applied to calibrate visco-hyperelastic parameters from stress–strain relations under different strain rates. The semi-free relaxation time strategy has shown to obtain more accurate results and shorter convergence times than the other strategies studied. It has been also shown that the understanding of the constitutive models and the complexity of the stress–strain objective curves is crucial for the accuracy of the method. [ABSTRACT FROM AUTHOR]

A set of non-linear optimisation algorithms are combined with a finite element simulation code to analyse the energy absorption and elastic stress wave propagation problem in multilayer/multimaterial armour systems under ballistic impacts. An Abaqus Python script is used to simulate the ballistic event and to generate the variables and post-processing outputs necessary for the integration with the optimisation algorithms. A number of modelling strategies are considered and three optimisation algorithms are used: Particle Swarm Optimisation (PSO), Genetic Algorithm (GA) and Simulated Annealing (SA). The performance and efficiency of each algorithm are assessed through four benchmark tests with different levels of complexity. A multi-objective optimisation procedure is proposed that uses the most efficient algorithm based on every single-objective formulation, variables and constraints from the benchmark tests, resulting in a highly non-linear problem. The proposed optimisation methods successfully achieve the study purposes both in the simulation of generic ballistic impacts and in the quality of the optimised solutions, demonstrating the potential for this type of optimisation method on terminal ballistic applications, serving as a standpoint for further studies into higher energy impacts and material non-linearities. [ABSTRACT FROM AUTHOR]

Global path planning is one of the key technologies in unmanned underwater vehicle (UUV) intelligent control. At present, research on UUV global path planning technology tends to choose long-distance and large-scale 3D space as the research environment, which leads to a sharp increase in the amount of data and search range for 3D spatial path planning. Therefore, an efficient and relatively small data volume 3D spatial path planning method is an urgent problem that needs to be solved for UUV engineering applications. To solve this problem, a new bilevel path planning algorithm for UUV is proposed. In the upper level of the algorithm, a Max Min Ant System-Elite Genetic (MMAS-EGA) algorithm is put forward, which is a hybrid ant colony optimization/genetic algorithm, in order to improve the convergence speed of the algorithm. In the lower level of the bilevel algorithm, a function optimization algorithm and the MMAS algorithm are used to minimize the number of variables to be optimized. To verify the effectiveness of the algorithm, we conducted simulation experiments in a three dimensional environment. The simulation results in the three-dimensional environment show that, compared with the existing bilevel algorithm, the time to search the global optimal solution is reduced by 9%, and the number of iterations is reduced by 4.4%. Furthermore, the new algorithm we proposed is more efficient and suitable for global path planning for different tasks. [ABSTRACT FROM AUTHOR]

Purpose: Project scheduling plays an essential role in the implementation of a project due to the limitation of resources in practical projects. However, the existing research tend to focus on finding suitable algorithms to solve various scheduling problems and fail to find the potential scheduling rules in these optimal or near-optimal solutions, that is, the possible intrinsic relationships between attributes related to the scheduling of activity sequences. Data mining (DM) is used to analyze and interpret data to obtain valuable information stored in large-scale data. The goal of this paper is to use DM to discover scheduling concepts and obtain a set of rules that approximate effective solutions to resource-constrained project scheduling problems. These rules do not require any search and simulation, which have extremely low time complexity and support real-time decision-making to improve planning/scheduling. Design/methodology/approach: The resource-constrained project scheduling problem can be described as scheduling a group of interrelated activities to optimize the project completion time and other objectives while satisfying the activity priority relationship and resource constraints. This paper proposes a new approach to solve the resource-constrained project scheduling problem by combining DM technology and the genetic algorithm (GA). More specifically, the GA is used to generate various optimal project scheduling schemes, after that C4.5 decision tree (DT) is adopted to obtain valuable knowledge from these schemes for further predicting and solving new scheduling problems. Findings: In this study, the authors use GA and DM technology to analyze and extract knowledge from a large number of scheduling schemes, and determine the scheduling rule set to minimize the completion time. In order to verify the application effect of the proposed DT classification model, the J30, J60 and J120 datasets in PSPLIB are used to test the validity of the scheduling rules. The results show that DT can readily duplicate the excellent performance of GA for scheduling problems of different scales. In addition, the DT prediction model developed in this study is applied to a high-rise residential project consisting of 117 activities. The results show that compared with the completion time obtained by GA, the DT model can realize rapid adjustment of project scheduling problem to deal with the dynamic environment interference. In a word, the data-based approach is feasible, practical and effective. It not only captures the knowledge contained in the known optimal scheduling schemes, but also helps to provide a flexible scheduling decision-making approach for project implementation. Originality/value: This paper proposes a novel knowledge-based project scheduling approach. In previous studies, intelligent optimization algorithm is often used to solve the project scheduling problem. However, although these intelligent optimization algorithms can generate a set of effective solutions for problem instances, they are unable to explain the process of decision-making, nor can they identify the characteristics of good scheduling decisions generated by the optimization process. Moreover, their calculation is slow and complex, which is not suitable for planning and scheduling complex projects. In this study, the set of effective solutions of problem instances is taken as the training dataset of DM algorithm, and the extracted scheduling rules can provide the prediction and solution of new scheduling problems. The proposed method focuses on identifying the key parameters of a specific dynamic scheduling environment, which can not only reproduces the scheduling performance of the original algorithm well, but also has the ability to make decisions quickly under the dynamic interference construction scenario. It is helpful for project managers to implement quick decisions in response to construction emergencies, which is of great practical significance for improving the flexibility and efficiency of construction projects. [ABSTRACT FROM AUTHOR]

Portal frames with built-up cold-formed cross-sections hold significant potential; however, there is a notable gap in the analysis of cross-section types and connections. In this study, an optimization algorithm was developed for the closed cross-sections of portal frame members. An optimization algorithm was tested against optimized open cold-formed cross-sections. The results indicated a portal frame volume up to 38% lower where members were assembled of optimal closed cross-sections when compared to frames with optimal open cross-sections. Parametric analysis was carried out, where two types of cross-sections were examined: Type A, with four web stiffeners bent inwards, and Type B, with four web stiffeners bent outwards. The optimization was conducted using a Genetic Algorithm in MATLAB R2022b. Portal frames with optimal Type B cross-sections had a volume that was up to 22% lower when compared to frames with optimal Type A cross-sections. Significant differences were noted between the optimal beam and column cross-sections, with the optimal column cross-section thickness being on average 74% greater, but the optimal beam cross-section height being on average 81% greater than those of the respective counterparts. In this article, a practical assembly solution for the connection of the frame members was proposed for the optimized novel closed cross-section types in portal frames. However, the strength and stiffness of these connections were not analyzed in this research. [ABSTRACT FROM AUTHOR]

The optimization of numerical functions with multiple independent variables was a significant challenge with numerous practical applications in process control systems, data fitting, and engineering designs. Although RNA genetic algorithms offer clear benefits in function optimization, including rapid convergence, they have low accuracy and can easily become trapped in local optima. To address these issues, a new heuristic algorithm was proposed, a gradient descent-based RNA genetic algorithm. Specifically, adaptive moment estimation (Adam) was employed as a mutation operator to improve the local development ability of the algorithm. Additionally, two new operators inspired by the inner-loop structure of RNA molecules were introduced: an inner-loop crossover operator and an inner-loop mutation operator. These operators enhance the global exploration ability of the algorithm in the early stages of evolution and enable it to escape from local optima. The algorithm consists of two stages: a pre-evolutionary stage that employs RNA genetic algorithms to identify individuals in the vicinity of the optimal region and a post-evolutionary stage that applies a adaptive gradient descent mutation to further enhance the solution's quality. When compared with the current advanced algorithms for solving function optimization problems, Adam RNA Genetic Algorithm (RNA-GA) produced better optimal solutions. In comparison with RNA-GA and Genetic Algorithm (GA) across 17 benchmark functions, Adam RNA-GA ranked first with the best result of an average rank of 1.58 according to the Friedman test. In the set of 29 functions of the CEC2017 suite, compared with heuristic algorithms such as African Vulture Optimization Algorithm, Dung Beetle Optimization, Whale Optimization Algorithm, and Grey Wolf Optimizer, Adam RNA-GA ranked first with the best result of an average rank of 1.724 according to the Friedman test. Our algorithm not only achieved significant improvements over RNA-GA but also performed excellently among various current advanced algorithms for solving function optimization problems, achieving high precision in function optimization. [ABSTRACT FROM AUTHOR]

Feature selection plays an essential role in enhancing the efficiency and effectiveness of machine learning models, particularly in the context of large-scale datasets where the dimensionality of features presents significant challenges. This research presents a novel approach to feature selection, a critical aspect of enhancing machine learning model efficiency, particularly in the context of large-scale datasets. By integrating the Flamingo Search Algorithm (FSA), Non-dominated Sorting Genetic Algorithm III (NSGA-III), and Regularised Extreme Learning Machine (RELM), the proposed method addresses limitations in existing feature selection and multi-objective optimisation algorithms. Leveraging FSA’s emulation of flamingo behaviours, the approach achieves a balance between global exploration and local exploitation, mitigating issues like premature convergence and local optima. Integration with NSGA-III enhances multi-objective optimisation capabilities, maintaining a delicate equilibrium between convergence and diversity. FSA-RELM is employed for accurate feature assessment, given its rapid learning and suitability for large datasets with multiple labels. Experimental evaluations demonstrate the proposed method’s superiority in feature selection accuracy, classification performance, and computational efficiency compared to existing approaches. This research contributes to advancing feature selection methodologies, offering a comprehensive solution for high-dimensional datasets in machine learning and data mining applications. [ABSTRACT FROM AUTHOR]

When it comes to modelling complex systems using an agent-based approach, there is a problem of choosing the appropriate parameter optimisation technique. This problem is further aggravated by the fact that the parameter space in complex agent-based systems can have a large dimension, and the time required to perform numerical experiments can be large. An alternative approach to traditional optimisation methods are the so-called metaheuristic algorithms, which provide an approximate solution in an acceptable time. The purpose of this study is to compare various metaheuristic algorithms for parameter tuning and to analyse their effectiveness applied to two agent-based models with different complexities. In this study, we considered commonly used metaheuristic algorithms for agent-based model optimisation: the Markov chain Monte Carlo method, the surrogate modelling approach, the particle swarm optimisation algorithm, and the genetic algorithm, as well as the more novel chaos game optimisation algorithm. The proposed algorithms were tested on two agent-based models, one of which was a simple toy model of the spread of contagious disease, and the other was a more complex model of the circulation of respiratory viruses in a city with 10 million agents and 26 calibrated parameters. [ABSTRACT FROM AUTHOR]

AbstractThe tension uniformity and surface accuracy of mesh antenna cable net are two well-accepted evaluation criteria for form finding. Traditional form finding techniques often rely on single-objective optimization and weighting factors. Although the Non-dominated Sorting Genetic Algorithm II (NSGA-II) can address the limitation, there are still challenges of wide distribution and insufficient precision of solutions. In the present work, a novel multi-objective optimization method based on an improved NSGA-II is introduced to conduct mesh antenna cable net form finding optimization. Based on the preference region (PR), a self-adaptive penalty function is proposed. The function is designed to steer the optimization process toward the PR, and enhancing the precision of the solutions. The extent of penalization for an individual solution depends on its positioning relative to the PR and is modified by a penalty coefficient that adapts dynamically to the population number within the PR. Additionally, a form finding method considering flexible frames is developed. The initial population for the flexible frame optimization is sourced from results of rigid frames, with the maximum truss node position error as the convergence criterion. Benchmark tests and case studies confirm that the improved NSGA-II enhances distributivity and convergence, while also providing form-finding results with higher surface accuracy and more uniform tension distribution. [ABSTRACT FROM AUTHOR]

With recent methodological advances in the field of computational protein design, in particular those based on deep learning, there is an increasing need for frameworks that allow for coherent, direct integration of different models and objective functions into the generative design process. Here we demonstrate how evolutionary multiobjective optimization techniques can be adapted to provide such an approach. With the established Non-dominated Sorting Genetic Algorithm II (NSGA-II) as the optimization framework, we use AlphaFold2 and ProteinMPNN confidence metrics to define the objective space, and a mutation operator composed of ESM-1v and ProteinMPNN to rank and then redesign the least favorable positions. Using the two-state design problem of the foldswitching protein RfaH as an in-depth case study, and PapD and calmodulin as examples of higher-dimensional design problems, we show that the evolutionary multiobjective optimization approach leads to significant reduction in the bias and variance in RfaH native sequence recovery, compared to a direct application of ProteinMPNN. We suggest that this improvement is due to three factors: (i) the use of an informative mutation operator that accelerates the sequence space exploration, (ii) the parallel, iterative design process inherent to the genetic algorithm that improves upon the ProteinMPNN autoregressive sequence decoding scheme, and (iii) the explicit approximation of the Pareto front that leads to optimal design candidates representing diverse tradeoff conditions. We anticipate this approach to be readily adaptable to different models and broadly relevant for protein design tasks with complex specifications. Author summary: Proteins are the fundamental building blocks of life, and engineering them has broad applications in medicine and biotechnology. Computational methods that seek to model and predict the protein sequence-structure-function relationship have seen significant advancement from the recent development in deep learning. As more models become available, it remains an open question how to effectively combine them into a coherent computational design approach. One approach is to perform computational design with one model, and filter the design candidates with the others. In this work, we demonstrate how an optimization algorithm inspired by evolution can be adapted to provide an alternative framework that outperforms the post hoc filtering approach, especially for problems with multiple competing design specifications. Such a framework may enable researchers to more effectively integrate the strengths of different modeling approaches to tackle more complex design problems. [ABSTRACT FROM AUTHOR]

Centralized traffic signal control has long been a challenging, high‐dimensional optimization problem. This study establishes a simulation‐based optimization framework and develops a novel optimization algorithm based on trust region Bayesian optimization (TuRBO), which can efficiently obtain an approximate optimal solution to the high‐dimensional traffic signal control problem. Local Gaussian process (GP), trust region, and Thompson sampling are employed in the TuRBO and contribute considerably to performance in terms of computational speed, solution quality, and scalability. Empirical studies are carried out using data from Mudanjiang and Chengdu, China. The performance of TuRBO is compared with that of Bayesian optimization (BO), genetic algorithm and random sampling. The results show that TuRBO converges the fastest because of its ability to balance exploration and exploitation through the trust region and Thompson sampling. Meanwhile, because TuRBO enables more efficient exploitation through the local GP, the solution quality of TuRBO outperforms others significantly. The average waiting time achieved by TuRBO was 2.84% lower than that achieved by BO. Finally, the method has been successfully extended to a large network with 233‐dimensional spaces and 122 signalized intersections, demonstrating that the developed methodology can deal with high‐dimensional traffic signal control effectively for real case applications. [ABSTRACT FROM AUTHOR]

In response to the issue of harvesting machine failures affecting crop harvesting timing, this study develops an emergency scheduling model and proposes a hybrid optimization algorithm that combines a genetic algorithm and an ant colony algorithm. By enhancing the genetic algorithm's crossover and mutation methods and incorporating the ant colony algorithm, the proposed algorithm can prevent local optima, thus minimizing disruptions to the overall scheduling plan. Field data from Deyang, Sichuan Province, were utilized, and simulations on various harvesting machines experiencing random faults were conducted. Results indicated that the improved genetic algorithm reduced the optimal comprehensive scheduling cost during random fault occurrences by 47.49%, 19.60%, and 32.45% compared to the basic genetic algorithm and by 34.70%, 14.80%, and 24.40% compared to the ant colony algorithm. The improved algorithm showcases robust global optimization capabilities, high stability, and rapid convergence, offering effective emergency scheduling solutions in case of harvesting machine failures. Furthermore, a visual management system for agricultural machinery scheduling was developed to provide software support for optimizing agricultural machinery scheduling. [ABSTRACT FROM AUTHOR]

Testing is one of the most time‐consuming and unpredictable processes within the software development life cycle. As a result, many test case optimization (TCO) techniques have been proposed to make this process more scalable. Object Constraint Language (OCL) was initially introduced as a constraint language to provide additional details to Unified Modeling Language models. However, as OCL continues to evolve, an increasing number of systems are being expressed by this language. Despite this growth, a noticeable research gap exists for the testing of systems whose specifications are expressed in OCL. In our previous work, we verified the effectiveness and efficiency of performing the test case prioritization (TCP) process for these systems. In this study, we extend our previous work by integrating the test case minimization (TCM) process to determine whether TCM can also benefit the testing process under the context of OCL. The evaluation of TCO approaches often relies on well‐established metrics such as the average percentage of fault detection (APFD). However, the suitability of APFD for model‐based testing (MBT) is not ideal. This paper addresses this limitation by proposing a modification to the APFD metric to enhance its viability for MBT scenarios. We conducted four case studies to evaluate the feasibility of integrating the TCM and TCP processes into our proposed approach. In these studies, we applied the multi‐objective optimization algorithm NSGA‐II and the genetic algorithm independently to the TCM and TCP processes. The objective was to assess the effectiveness and efficiency of combining TCM and TCP in enhancing the testing phase. Through experimental analysis, the results highlight the benefits of integrating TCM and TCP in the context of OCL‐based testing, providing valuable insights for practitioners and researchers aiming to optimize their testing efforts. Specifically, the main contributions of this work include the following: (1) we introduce the integration of the TCM process into the TCO process for systems expressed by OCL. This integration benefits the testing process further by reducing redundant test cases while ensuring sufficient coverage. (2) We comprehensively analyze the limitations associated with the commonly used metric, APFD, and then, a modified version of the APFD metric has been proposed to overcome these weaknesses. (3). We systematically evaluate the effectiveness and efficiency of OCL‐based TCO processes on four real‐world case studies with different complexities. [ABSTRACT FROM AUTHOR]

Stock markets around the world are growing rapidly, and vulnerability created by default risk of option holders has become a serious issue. In such circumstances, this paper suggests the pricing of vulnerable European and barrier options, when the underlying asset and asset held by the option writer follow the geometric Liu process and uncertain mean-reverting process, respectively. According to the uncertainty theory, we present closed-form analytic formulas of these option prices and use the residual method to estimate the model parameters. In order to estimate the model parameters, the genetic algorithm to solve the generalized moment estimation problem derived by the residual method is suggested. From the uncertain hypothesis test, we then evaluate whether the proposed uncertain stock models fit the observed data. Ultimately, based on the estimated parameters, we evaluate the effect of the vulnerable option parameters on its price. [ABSTRACT FROM AUTHOR]

This paper aims to solve the multi-objective operating planning problem in the radioactive environment. First, a more complicated radiation dose model is constructed, considering difficulty levels at each operating point. Based on this model, the multi-objective operating planning problem is converted to a variant traveling salesman problem (VTSP). Second, with respect to this issue, a novel combinatorial algorithm framework, namely hyper-parameter adaptive genetic algorithm (HPAGA), integrating bio-inspired optimization with reinforcement learning, is proposed, which allows for adaptive adjustment of the hyperparameters of GA so as to obtain optimal solutions efficiently. Third, comparative studies demonstrate the superior performance of the proposed HPAGA against classical evolutionary algorithms for various TSP instances. Additionally, a case study in the simulated radioactive environment implies the potential application of HPAGA in the future. [ABSTRACT FROM AUTHOR]

Community detection in complex networks plays a pivotal role in identifying the structural composition of nodes, enabling us to pinpoint key topological features critical for a multitude of applications. Usually, the complex networks are in graphical format in which graph nodes denote objects and edges denote a connection between two things. The existing techniques have limitations such as early convergence and local optima issues. To overcome this issue, this manuscript proposed an Improved Hybrid Harris Hawk and Coot Bird Optimization (IHHHCBO) algorithm in community detection. The optimum separate movement of CBO is incorporated in HHO population initialization to strengthen both optimizations. It is employed to initialize the population for enhancing the position of diversity and change with other individuals. The Karate, Dolphin, Football and Political Books datasets are considered for assessing the IHHHCBO performance. The Ensemble Mutation Strategy (EMS) is developed to produce mutant candidate locations which enhances the exploration and population diversity of optimization. The Normalized Mutual Information (NMI), and Modularity (Q) are considered fitness functions in this research. The IHHHCBO performance is estimated through metrics like NMI, Q, f1-score and accuracy. The IHHHCBO reaches better accuracy of 1, 1, 0.998 and 0.883 for Karate, Dolphin, Football and Political Books respectively which is better when compared to existing algorithms like Core Structure Extraction Algorithm (CSEA), and Modified Crossover Opposition-based Genetic Algorithm (MCOBGA). [ABSTRACT FROM AUTHOR]

Aerodynamic/stealth optimization is a key issue during the design of a stealth UAV. Balancing the weight of different incident angles of the RCS and combining stealth characteristics with aerodynamic characteristics are hotspots of aerodynamic/stealth optimization. To address this issue, this paper introduces a radar detection probability model to solve the weight balance problem of incident angles of the RCS and a penetration efficiency model to transfer the multi-object optimization into single-objective optimization. In this paper, a parameterized model of a flying-wing UAV is selected as the research object. A gradient-free optimization algorithm based on the genetic algorithm is used for maximizing efficiency. The optimization model balances the influence of the RCS mean value and RCS peak value on stealth performance. Moreover, the model achieves an optimal entire life cycle penetration efficiency coefficient by balancing aerodynamic and stealth optimization. The results show that the optimized model improves the penetration efficiency coefficient by 13.84% and increases maximum flight sorties by 1.8%. These results prove that the model has a reasonable combination of aerodynamic and stealth optimization for UAVs undertaking penetration missions. [ABSTRACT FROM AUTHOR]

Objective functions of which an analytical solution is very difficult or time-consuming are solved using stochastic optimization algorithms. Those optimization algorithms compute an approximate solution for objective functions. For a specific search space, the objective function might have one or more local optima along with the global optimum. When a comparison is made among the algorithms, one optimization algorithm could be more effective than others in finding a solution for certain objective functions. The most important factors affecting the success of optimization algorithms are the greatness of search space and the complexity of the objective function. Reaching the global optimum in huge search spaces is very difficult. In complex objective functions that have many local optima or where the differences between global optimum and local optima are very small, the probability of trapping into the local optimum is high. Existing optimization algorithms could be improved using the search space scanned more successfully to give a better performance. To achieve this aim, we present a novel algorithm, called Army-Inspired Genetic Algorithm (AIGA), which is inspired from military movement. The presented algorithm, apart from other optimization algorithms, searches global optima effectively by dividing the entire search area into territories instead of searching in one piece. Thus, the probability of getting trapped in a local optimum reduces and the probability of finding the global optimum increases. The presented algorithm was tested on well-known benchmark problems. The results shows that AIGA is more efficient algorithm in finding the global optimum than traditional algorithms. [ABSTRACT FROM AUTHOR]

This research delves into the impact of Twist Winglets–Cross-Section Twist Tape (TWs-CSTT) structures within heat exchangers on thermal performance. Utilizing Computational Fluid Dynamics (CFD) and machine learning methodologies, optimal geometrical parameters for the TWs-CSTT configuration were examined. The outcomes demonstrate that fluid undergoing a rotational motion within tubes featuring this structure leads to more effective secondary flows, intensified mixing, and improved thermal boundary layer disturbance. Moreover, by integrating machine learning with multi-objective optimization techniques, the performance of heat exchangers can be accurately predicted and optimized, facilitating enhanced heat exchanger design. Through the application of the multi-objective optimization algorithm Non-dominated Sorting Genetic Algorithm II (NSGA-II), the ideal configurations for TWs-CSTT were ascertained: L1 is the cross-sectional length of the Twisted Wings, L2 is the radius of the Central Straight Twisted, and P is the pitch. P = 50.699 mm, L1 = 4.3282 mm, L2 = 4.9736 mm for the Gaussian Process Regression (GPR) model; P = 50.864 mm, L1 = 4.4961 mm, L2 = 4.9992 mm for the LR model; and P = 50.699 mm, L1 = 4.3282 mm, L2 = 4.9736 mm for the Support Vector Regression (SVR) model, aiming to maximize heat exchange efficiency while minimizing friction losses. This study proposes a novel methodological approach to heat exchanger design, leveraging CFD and machine learning technologies to enhance energy efficiency and performance. [ABSTRACT FROM AUTHOR]

This paper proposes the shortest path optimization algorithm for domestic and overseas e-commerce logistics based on a bilateral search method. This paper uses the logistics distribution route optimization algorithm based on the shortest path to set the collaborative parameters. Then, it builds an adaptive optimization model for the grid planning of domestic and overseas e-commerce logistics path. The route is optimized. Then, the PSO and genetic algorithm are integrated to establish the logistics path planning model of domestic and overseas e-commerce. The superiority of the proposed route optimization algorithm in domestic and overseas e-commerce logistics distribution is verified through simulation experiments. This algorithm has high spatial positioning efficiency and high transportation efficiency. [ABSTRACT FROM AUTHOR]

The pass rate of granules is an essential indicator during the high-pressure grinding process, as it accurately reflects the processing quality. Currently, the pass rate of granules is detected primarily based on manual experience judgments or offline inspections. Hence, this article presents a methodology for predicting the pass rate of granularity via an optimized support vector regression approach improved through genetic algorithms. Initially, a time-delay analysis method based on the particle swarm optimization algorithm is applied to mitigate the effects of time delays between the granularity pass rate and other data, thus aligning the dataset on a temporal scale. Subsequently, the feature data were selected using the maximum information coefficient analysis technique, which identified the most significant variables for inclusion in the training and testing sets of the predictive model. Predictions are then made using a support vector machine model that has been enhanced via genetic algorithm optimization. Furthermore, an online prediction model has been established, enabling real-time forecasting of the granularity pass rate and online model updates through root mean square propagation gradient descent optimization algorithm. This method leverages end-edge-cloud collaboration to provide a smart detection mechanism for the throughput rate of particles in high-pressure grinding mills. Experimental results demonstrate that, compared to traditional time-delay analysis, the improved time-delay analysis method proposed in this study is more effective and accurate. Simultaneously, the ɛ-GASVR granularity pass-rate prediction model proposed in this article achieved an R2 of 0.89. [ABSTRACT FROM AUTHOR]

Ant Lion Optimization (ALO) method is one of the population-based nature-inspired optimization algorithms which mimics the hunting strategy of antlions. ALO is successfully employed for solving many complicated optimization problems. However, it is reported in the literature that the original ALO has some limitations such as the requirement of high number of iterations and possibility of trapping to local optimum solutions, especially for complex or large-scale problems. For this purpose, the SHuffled Ant Lion Optimization (SHALO) approach is proposed by conducting two improvements in the original ALO. Performance of the proposed SHALO approach is evaluated by solving some unconstrained and constrained problems for different conditions. Furthermore, the identified results are statistically compared with the ones obtained by using the original ALO, two improved ALOs which are the self-adaptive ALO (saALO) and the exponentially weighted ALO (EALO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) approaches. Identified results indicated that the proposed SHALO approach significantly improves the solution accuracy with a mean success rate of 76% in terms of finding the global or near-global optimum solutions and provides better results than ALO (22%), saALO (25%), EALO (14%), GA (28%), and PSO (49%) approaches for the same conditions. [ABSTRACT FROM AUTHOR]

A battery track engineering vehicle faces challenges such as derailment and other safety concerns when navigating an R20m minimum radius curve, primarily owing to its low vertical and horizontal stabilities. To address these issues, a methodology integrating genetic optimization algorithms with a rigid and flexible coupled multi-body dynamics simulation is proposed to optimize the primary suspensions of the bogie of the vehicle. Initially, a multi-objective optimization model combining rigid and flexible coupled multi-body dynamics of battery track engineering vehicles with a genetic optimization algorithm was formulated. Subsequently, the optimal Latin hypercube design was applied to analyze the sensitivity of vertical and horizontal stability to various suspension parameters. Finally, a non-dominated sorting genetic algorithm (NSGA-II) and an archive-based micro genetic algorithm (AMGA) were applied to optimize the primary suspensions to enhance stability. Consequently, a set of optimal suspension parameter combinations was obtained. A notable enhancement was observed in the lateral stability of the optimized battery track engineering vehicles by 23.33% and in the vertical stability by 3.5% when traversing the R20m minimum radius curve, thereby establishing a theoretical foundation for further improving the running safety of railway vehicles and resolving the shortcomings of less research on the smallest radius curve. [ABSTRACT FROM AUTHOR]

In an antenna array, the radiations characteristics may be disturbed by some failed elements out of total antenna elements that caused increase of side lobe level power and ultimately reduces the efficiency or performance of an antenna system. In this work, computationally efficient brain storm optimization algorithm technique has been employed to recover the original characteristics of antenna array system. The normalized excitation coefficients (amplitude values of all elements) of 32 × 1 and 16 × 1 elements of linear antenna array design has been re-computed with the help of proposed technique to regain original features of antenna. The results has been validated by analyzing the various antenna parameters such as half power beam width, first null beam width, side lobe level etc. Secondly, two nulls have been incorporated in the radiation characteristics of antenna array in order to suppress the unwanted signals that reaching to the system from specific directions. The performance of antenna system for nulls has been verified through parameter such as null depressed level. The work has been compared with other computationally efficient techniques (Biogeography-based optimization and genetic algorithm). [ABSTRACT FROM AUTHOR]