Main Objective Of Optimization Research Articles

Optimization of dynamic multi-leaf collimator based on multi-objective particle swarm optimization algorithm

Background The dynamic multi-leaf collimator (DMLC) plays a crucial role in shaping X-rays, significantly enhancing the precision, efficiency, and quality of tumor radiotherapy. Objective To improve the shaping effect of X-rays by optimizing the end structure of the DMLC leaf, which significantly impacts the collimator's performance. Methods This study introduces the innovative application of the multi-objective particle swarm optimization (MOPSO) algorithm to optimize DMLC parameters, including leaf end radius, source-to-leaf distance, leaf height, and tangent angle between the leaf end and the central axis. The main optimization objectives are to minimize the width and variance of the penumbra, defined as the distance between the 80% and 20% dose of X-rays on the isocenter plane, which directly impacts treatment accuracy. Results Structural optimization across various scenarios showed significant improvements in the size and uniformity of the penumbra, ensuring a more precise radiation dose. Based on the optimized structure, a three-dimensional model of the MLC was designed and an experimental prototype was fabricated for performance testing. The results indicate that the optimized MLC exhibits a smaller penumbra. Conclusion The proposed optimization method significantly enhances the precision of radiotherapy while minimizing radiation exposure to healthy tissue, representing a notable advancement in radiotherapy technology.

Evolutionary mechanisms that promote cooperation may not promote social welfare.

Understanding the emergence of prosocial behaviours among self-interested individuals is an important problem in many scientific disciplines. Various mechanisms have been proposed to explain the evolution of such behaviours, primarily seeking the conditions under which a given mechanism can induce highest levels of cooperation. As these mechanisms usually involve costs that alter individual pay-offs, it is, however, possible that aiming for highest levels of cooperation might be detrimental for social welfare-the latter broadly defined as the total population pay-off, taking into account all costs involved for inducing increased prosocial behaviours. Herein, by comparing stochastic evolutionary models of two well-established mechanisms of prosocial behaviour-namely, peer and institutional incentives-we demonstrate that the objectives of maximizing cooperation and of maximizing social welfare are often misaligned. First, while peer punishment is often more effective than peer reward in promoting cooperation-especially with a higher impact-to-cost ratio-the opposite is true for social welfare. In fact, welfare typically decreases (increases) with this ratio for punishment (reward). Second, for institutional incentives, while maintaining similar levels of cooperation, rewards result in positive social welfare across a much broader range of parameters. Furthermore, both types of incentives often achieve optimal social welfare when their impact is moderate rather than maximal, indicating that careful planning is essential for costly institutional mechanisms to optimize social outcomes. These findings are consistent across varying mutation rates, selection intensities and game configurations. Overall, we argue for the need of adopting social welfare as the main optimization objective when designing and implementing evolutionary mechanisms for social and collective goods.

Electric vehicle power system in intelligent manufacturing based on soft computing optimization

Soft computing technology has attracted extensive attention in computer engineering and automatic control domains because it can deal with uncertainties, fuzziness, and complex practical problems. This study adopts a Genetic Algorithm (GA) in soft computing technology to realize the cooperative optimization of electric vehicle's dynamic and economic performance. The advantage of soft computing technology lies in its adaptability to uncertainty, fuzziness, and complex practical problems, making GA an effective tool for solving complex optimization problems. Firstly, the electric vehicles' power system structure and energy management strategy are investigated and analyzed. Secondly, the improved non-dominated sorting genetic algorithm II (NSGA-II) is selected to optimize the parameters of electric vehicles because of its simple operation and high optimization accuracy. Thirdly, NSGA-II is used to construct the electric vehicles' power and energy configuration, with power and economic performance as the main optimization objectives. Meanwhile, a fuzzy logic controller is designed to adjust the parameters of GA online, so that the optimization process is closer to the actual operating conditions. Finally, the relevant variables are selected to achieve the optimization goal, the optimization objective function and constraint conditions are established, and the model is simulated and evaluated. The results show that the optimized electric vehicle's acceleration time is remarkably reduced, the dynamic performance is improved by more than 7 %, and the power loss is reduced by 5 %. In addition, compared with the current multi-objective optimization model, this model enables electric vehicles to travel longer distances under the same power. This study provides a new idea and method for the performance optimization of electric vehicles. Moreover, it offers a valuable reference for the innovation and development of electric vehicle technology in the intelligent manufacturing field. This study indicates that electric vehicles could be more efficient, energy-saving, and environmentally friendly to serve people's travel needs in the future.

Cost optimization in edge computing: a survey

The edge computing paradigm is becoming increasingly commercialized due to the widespread adoption of wireless communication technologies and the growing demand for compute-intensive mobile applications. Edge computing complements the cloud computing model by deploying computation, storage, and network resources to the edge locations of wireless access networks, empowering end devices to run resource-intensive applications. In order to promote the commercialization of edge computing, it is important to explore effective ways to reduce the cost of edge computing networks. This paper provides a comprehensive review of the research findings in recent years, offering a clear perspective on the research dynamics. This paper first recalls the architectural framework of edge computing. Then, the main optimization objectives and optimization methods are comprehensively described. Mainstream mathematical models for cost reduction are then shown in depth. The paper also discusses the methods used to evaluate the effectiveness. Then, typical examples of typical application scenarios for edge computing networks are examined in depth. Finally, the paper identifies some unresolved issues. We expect future research to make more attempts in these directions.

C2FResMorph: A high-performance framework for unsupervised 2D medical image registration

Deformable medical image registration is an important precursor task for surgical automation, while enhancing the registration performance of 2D medical images remains a challenging work. Existing methods primarily minimize the similarity loss between image pairs as the main optimization objective, leading to limited registration accuracy and a lack of pixel matching. Moreover, the scarcity of informative features in 2D images often results in overfitting on the training set, hampering generalization. To address these issues, we propose C2FResMorph, a learning-based deformable registration algorithm specifically designed for 2D medical images. C2FResMorph employs a two-stage framework that improves registration accuracy and preserves topology during deformation in a coarse-to-fine manner. Inside the framework, by leveraging the convolutional neural network’s locality and the multi-head self-attention mechanism’s globality, a ResMorph registration network is designed. Additionally, the integration of residual image knowledge addresses deformation folding in 2D image registration, enhancing the preservation of local structures and improving generalization. Experimental evaluations on three datasets demonstrate that C2FResMorph outperforms existing learning-based methods in terms of accuracy, generalization ability for 2D medical image registration, and also retains the efficiency advantages.

DEM simulation and optimization of crushing chamber shape of gyratory crusher based on Ab-t10 model

To study and optimize the crushing chamber performance of the gyratory crusher, the impact of the crushing chamber shape on the gyratory crusher performance is explored in this paper. The discrete element method (DEM) analysis model of ore particle and gyratory crusher is established, where ore particle crushing is realized based on the Ab-t10 breakage model, and its validity is verified based on actual working conditions. On this basis, the performance of the gyratory crusher under different crushing chamber shapes is studied by combining the DEM simulation with the response surface method (RSM), and the performance prediction model is obtained based on multiple nonlinear regression. Further, the productivity is considered as the main optimization objective, the mass percentage of effective particles and the crushing force are used as auxiliary optimization objectives. The multi-objective optimization is implemented and the optimized effects are analysed by DEM simulation, and the optimized quartic equation coefficients of the concave curve are obtained. Finally, the results show that the productivity, mass percentage of effective particles, and crushing force are increased to varying degrees. Meanwhile, the clogged layer area increases to some extent, which promotes the crushing of ore particles.

Entropy analysis of double diffusion in a Darcy medium with tangent hyperbolic fluid and slip factors over a stretching sheet: Role of viscous dissipation

This study aims to numerically investigate entropy optimization for a time-independent tangent hyperbolic fluid flowing past a porous sheet that is stretching linearly. The fluid motion is generated by stretching surfaces and buoyancy forces. The analysis includes momentum, energy, and mass transport associated with mixed convection, thermal radiation, velocity, thermal, and concentration slip factors. Heat dissipation is also considered in energy transport through the viscous dissipation effect. The mathematical formulation leads to a set of non-linearly coupled partial differential equations. To obtain a similarity solution, similarity variables are introduced. The numerical solution of the leading differential equations is obtained using the fourth-order Runge-Kutta method with the shooting technique. The graphical results are presented to show the physical significance of the relevant parameters. The main objective of entropy optimization is achieved by increasing the magnitude of the Darcy dissipation parameter. Entropy generation is directly proportional to the Brinkmann number, Reynolds number, and radiation parameter. The second law of thermodynamics is fulfilled by introducing slip conditions over the porous medium. The use of a tangent hyperbolic fluid over a porous medium has practical applications in various porous geometries, such as oil/gas reservoir simulation, enhanced oil recovery, carbon dioxide sequestration, and water soil infiltration. Therefore, optimizing entropy generation in these mechanical systems is crucial.

Location and path planning for urban emergency rescue by a hybrid clustering and ant colony algorithm approach

Rescue station setup and rescue path planning are two important tasks in urban emergency rescue. The former task ensures rescue response capability and the latter task provides effective rescue solutions. When emergencies occur in cities, evacuees are distributed along the urban road network. Rescue resources refer to rescue vehicles whose available number and capacity are both limited. With the constraints of rescue resources and the number of rescues, this paper aims to simultaneously optimize the tasks of rescue station setup and rescue path planning. In the addressed scenario, the priority of each evacuee is quantified as a weight value that is used as the main optimization objective. To solve the problem, a comprehensive urban emergency rescue planning approach is proposed. The proposed approach consists of components of road network processing, road network weight calculation, rescue station setup and rescue path planning. For the setup of rescue stations, this paper employs a clustering method to provide a set of high-quality candidate rescue stations for subsequent path planning based on the locations of evacuees and the road network structure. For rescue path planning, an improved ant colony optimization algorithm is developed. The proposed method is called the planning algorithm with clustering and improved ant colony optimization (PA-C-IACO). The proposed PA-C-IACO redefines the degree of heuristic and pheromone concentration increments for transfer between intersections in the ant colony algorithm and incorporates a reward mechanism during the pheromone update process. Experimental results on six different size datasets show that PA-C-IACO outperforms state-of-the-art algorithms and shows good robustness and feasibility.

Machine Learning-Based Design of Ionic Liquids at the Atomic Scale for Highly Efficient CO2 Capture

Ionic liquids (ILs) are considered excellent substitutes for aqueous alkanolamine solutions in CO2 capture systems. However, the smart design of ILs, facing the small sparse data set and complex ionic structures, poses a huge challenge. To address this issue, a novel machine learning method based on a syntax-directed variational autoencoder (SDVAE), deep factorization machine (DeepFM), and gradient-based particle swarm optimization (GBPSO) was proposed in this work. The SDVAE converts the molecular structure and chemical space of the ILs, and then DeepFM predicts the solubility of each coordinate in the chemical space representing an IL. Finally, GBPSO identifies the coordinates that represent ILs with ideal properties. Our main optimization objective is a high solubility difference for CO2 between its absorption and desorption conditions in commercial plant capture systems, which represents the CO2 capture ability. The best IL generated has a predicted solubility difference that is 35.3% higher than that of the best one in the data set. A synthetic novel IL [EMIM][TOS] from the generated results was experimentally evaluated; it has a sufficiently high solubility difference to be a capture solvent with low energy consumption. Our model has proved to be a high-efficiency molecular design model that can be used for sparse small data sets.

Discrete Whale Optimization Algorithm for Disassembly Line Balancing With Carbon Emission Constraint

With the advancement of science and technology in recent years, the rate of product upgrading by end users has increased, resulting in a high number of End-Of-Life (EOL) products. For environmental protection and economic benefits, disassembly lines are set up to recycle them. A disassembly line balancing problem arises and has attracted widespread attention. In this paper, we design a model to address a class of disassembly line balancing problems in which the main optimization objective is to maximize profits, while keeping carbon emission in a tolerable range. A discrete whale optimization algorithm is proposed to search their high-quality solutions. Its efficiency is verified by comparing its solutions with their analytic solutions obtained from a commercial optimization solver and other swarm intelligence algorithms. Experimental results show that it is significantly faster than CPLEX and other swarm intelligence algorithms in solving large-scale optimization problems while guaranteeing its solutions quality.

Application of Visual Recognition Based on BP Neural Network in Architectural Design Optimization.

In order to establish the mapping relationship between architectural design parameters and building performance and optimize architectural design parameters, an architectural design optimization method based on BP neural network is proposed. The selected main design parameters of building ventilation include spacing coefficient, air outlet area, and height from the bottom of the window sill to the ground. Take the comprehensive performance of building ventilation design as the main optimization objective to optimize the building design. First, nine groups of samples of building optimization design are obtained through uniform experimental design. Then, based on the architectural design sample data obtained by BP neural network training, the mapping relationship between architectural design parameters and building performance is established, and based on this mapping, the optimal design parameters of the building are calculated. The research results have a certain reference value for architectural design optimization.

Driver Route Planning Method Based on Accident Risk Cost Prediction

The number of cars on roadways around the world continues to increase year over year. However, the imbalance between traffic supply and demand has not only brought traffic congestion but also caused serious safety problems. To reduce travel risk, this study proposes a driver route planning method based on accident risk cost prediction for connected and automated vehicles. According to the entropy weight method and an improved algorithm of K shortest paths, a route planning model with accident risk as the main optimization objective was established. Firstly, an accident risk evaluation system was built based on traffic accident data, and a quantitative prediction model of accident risk cost based on driver-, vehicle-, road-, and environment-related factors was constructed. Secondly, the entropy weight method was used to calculate the weights of each indicator to determine accident risk considering the aforementioned factors. Then, the route planning model was established, and the solution algorithm based on K shortest paths was designed to solve the optimal route by comprehensively considering accident risk cost and travel time. The accident risk index of each road section in the example road network was assigned, and the risk of the road section was quantified according to the accident risk cost model. Three candidate paths were calculated by using the path planning algorithm proposed in this study; the total risk cost is 6.19, 6.26, and 6.39, respectively; and the total travel time is 29, 29, and 31, respectively. After comparison, the optimal path and two alternative paths are obtained. The results show that the accident risk cost prediction model based on historical accident data can be used to quantify driving risk. The proposed method can help drivers in the connected and automated environment choose the optimal travel route with the lowest risk and shortest travel time and improve overall traffic safety and efficiency.

Single-time power frequency modulation and peak -modulation market combined clearing method considering new energy absorption capacity

In order to support the transaction clearing business of power frequency modulation and peak load modulation market in China and improve the absorption capacity of new energy, the coupling relationship between power frequency modulation and peak load modulation auxiliary service is studied, and the combined clearing method of single time period power frequency modulation and peak load modulation market considering the absorption of new energy is proposed. The coupling relationship between power frequency modulation and power peak modulation auxiliary services is reflected in that the two auxiliary services are mutually exclusive. Considering the important promoting effect of peak regulation capacity on new energy consumption, the index of residual depth peak regulation capacity was proposed as the evaluation basis of the clearance result on new energy consumption capacity. On this basis, a single time combined clearing model of power frequency modulation and peak-shaving market is constructed, which takes the minimum clearing cost as the main optimization objective and the maximum remaining peak-shaving capacity as the secondary optimization objective. Numerical examples show that the proposed method has significant advantages over the current sequential cleaning method and the combined cleaning method without considering the consumption of new energy in terms of transaction cost and residual depth peak-shaving capacity. The results show that the method proposed in this paper meets the requirements of promoting the construction of new power system with new energy as the main body in China, and has significant benefits in promoting the reform of power market and improving the consumption of new energy.

A Novel Hybrid Optimization-Based Algorithm for the Single and Multi-Objective Achievement With Optimal DG Allocations in Distribution Networks

Distribution networks are facing new challenges with the emergence of smart grids, such as capacity limitations, voltage instability, and many others. These challenges can potentially lead to brownouts and blackouts. This paper presents an innovative technique for optimal siting and sizing of distributed generators (DGs) in radial distribution networks (RDNs). The proposed technique uses a novel algorithm that combines improved grey wolf optimization with particle swarm optimization (I-GWOPSO) by incorporating dimension learning-based hunting (DLH). The proposed I-GWOPSO employs a novel aspect of DLH to reduce the gap between local and global searches to maintain a balance. The main optimization objectives aim to optimally site and size the DG with minimization of active power loss, voltage deviation, and improvement of voltage stability in RDNs. Case studies are simulated with IEEE 33-bus and IEEE 69-bus test systems, for the optimal allocation of DG units by considering various power factors. The results validate the efficacy of the proposed algorithm with a significant reduction in real power loss (up to 98.1%), improvement in voltage profile, and optimal reduced cost of DG operation with optimal sizing across all considered cases. A comparative analysis of the proposed approach with existing literature validates the improved performance of the proposed algorithm.

Optimization of flight test tasks allocation and sequencing using genetic algorithm

Flight test tasks arrangement is one of the most significant problems in the development of new civil aircraft. Normally, there are many factors restraining flight test tasks arrangement, including characteristics of experimental aircraft, requirements of tasks themselves, and logical relationships among them, leading to increased development period and costs. Hence, flight test tasks arrangement is generally viewed as a multi-constraint nonlinear optimization problem. To improve flight test efficiency, a multi-level optimization model of flight test tasks allocation and sequencing is introduced in this paper, where flight test period is the main optimization objective, and a penalty function evaluating tasks testing dates is the minor optimization objective. A flight test tasks sequence oriented improved genetic algorithm (FTTSOIGA) is proposed to solve the model. Firstly, a tasks allocation algorithm is designed to establish the mapping between tasks sequence and tasks arrangement result, which is independent of feasible sequence. Then, the arrangement result is optimized by optimizing the tasks sequence using the genetic algorithm. Furthermore, a tasks sequence adjustment strategy is applied to accelerate algorithm convergence. Simulation cases of 3 experimental aircraft and 80 flight test tasks demonstrate the efficiency of FTTSOIGA.

Response Surface Methods Used for Optimization of Abrasive Waterjet Machining of the Stainless Steel X2 CrNiMo 17-12-2.

Abrasive waterjet machining (AWJM) has a particularly high potential for the machining of stainless steels. One of the main optimization objectives of the machining of X2 CrNiMo 17-12-2 stainless steel is obtaining a minimal surface roughness. This entails selecting an optimum configuration of the main influencing factors of the machining process. Optimization of the machining system was achieved by intervening on four selected input quantities (traverse speed, waterjet pressure, stand-off distance, and grit size), with three set points considered for each. The effects of modifying the set-points of each input parameter on the surface roughness were studied. By means of response surface methodology (RSM) the combination of factor set points was determined that ensures a minimum roughness of the machined surface. The main benefit of RSM is the reduced time needed for experimenting.

Analysis of Global Warming’s Influence on the Dimensioning of Borehole Heat Exchangers at a Climate-Exposed Site

A borehole heat exchanger (BHE) presents the most reliable source of geothermal energy for any object where the heat pump system is to be installed. The main objective of BHE optimization in a specific rock massive and for calculated heat consumption is to design a BHE with proper capacity and sustainable performance. One of the most important inputs for the preparation of such a model is the average outer air temperature on the site during the year. While the properties of the local rock massive are from the heat project lifetime view (tens of years) stable, the local average outer temperature fluctuates according to global climate changes. This article presents a study of the impact of climate changes on the dimensioning process of a BHE and consequently on its performance using data from a real installation at a highly climate-exposed site in the Czech Republic. During the dimensioning of a BHE, this study could help to better quantify the objective risks that result from climate changes. However, the results of this study show that the currently calculated impact of an increase in outer temperature is not crucial, but it is advisable to take this new fact into account during the design and dimensioning process, especially for large installations. To study the ground in terms of usable energy potential at climate-exposed locations properly, the available data were critically analyzed and the results were then synthesized in an appropriate way for the needs of the data simulations.

Metaheuristic Techniques in Optimizing Traffic Control Lights: A Systematic Review

Traffic congestion is a serious problem on every roadway and streets in many cities around the world. This systematic review is devoted to analyze research papers that deal with the optimization of traffic signal timing. The main objective of such optimization is maximizing the number of the vehicles leaving the network in a given period of time. This will lead to enhancing the performance of the road system. In this work, we researched the most recent metaheuristic optimized traffic light control techniques. It was shown that integrating optimization techniques in the field of traffic lights control had a great impact on the performance of traffic monitoring. During our research, we found that the most used method was the Genetic Algorithm (GA).

An evolutionary game approach to IoT task offloading in fog-cloud computing

Developing the edge and fog computing has been the result of the fast growth of cloud-based IoT applications. These new paradigms define new resource management problems such as IoT task offloading challenge. Despite the many conducted effort in the IoT task offloading, this issue remains a research gap partly because this problem is NP-hard inherently, and also because many proposed solutions are based on unrealistic assumptions. Indeed, many proposed solutions suffer from the lack of a real set of tasks for a comprehensive evaluation. Thus, we prepare a collection of Python tasks to provide a realistic context for the assessment of the proposed task offloading methods. Also, this paper proposes a four-tier architecture to determine the specific decision-maker for the task offloading. Then, we formulate this problem as a population (evolutionary) game that is solved by Maynard replicator dynamics. The time and energy consumption are the main optimization objectives in this work. Finally, we simulate the proposed scheme in MATLAB by emphasizing the use of realistic values and parameters. The experimental results indicate that our scheme is a practical approach partly because it contributes to the core traffic decreases and because its convergence time is less than 6 s to solve the problem with 100 tasks.

Optimal Allocation of ESSs in Active Distribution Networks to Achieve Their Dispatchability

This paper presents a method for the optimal siting, and sizing of energy storage systems (ESSs) in active distribution networks (ADNs) to achieve their dispatchability. The problem formulation accounts for the uncertainty inherent to the stochastic nature of distributed energy sources, and loads. Thanks to the operation of ESSs, the main optimization objective is to minimize the dispatch error, which accounts for the mismatch between the realization, and prediction of the power profile at the ADN connecting point to the upper layer grid, while respecting the grid voltages, and ampacity constraints. The proposed formulation relies on the so-called Augmented Relaxed Optimal Power Flow (AR-OPF) method: it expresses a convex full AC optimal power flow, which is proven to provide a global optimal, and exact solution in the case of radial power grids. The AR-OPF is coupled with the proposed dispatching control resulting in a two-level optimization problem. In the first block, the site, and size of the ESSs are decided along with the level of dispatchability that the ADN can achieve. Then, in the second block, the adequacy of the ESS allocations, and the feasibility of the grid operating points are verified over operating scenarios using the Benders decomposition technique. Consequently, the optimal size, and site of the ESSs are adjusted. To validate the proposed method, simulations are conducted on a real Swiss ADN hosting a large amount of stochastic Photovoltaic (PV) generation.