In this paper, a multi-objective particleswarm optimizer based on adaptive dynamic neighborhood (ADN-MOPSO) is proposed to locate multiple Pareto optimal solutions to solve multimodal multi-objective problems. In the...
详细信息
ISBN:
(纸本)9798350334722
In this paper, a multi-objective particleswarm optimizer based on adaptive dynamic neighborhood (ADN-MOPSO) is proposed to locate multiple Pareto optimal solutions to solve multimodal multi-objective problems. In the proposed algorithm, a spatial distance-based non-overlapping ring topology is used to form multiple subpopulations for parallel search to enhance the local search capability of the algorithm. In addition, an adaptive dynamic neighborhood selection strategy is proposed to balance the exploration and exploitation capabilities of the algorithm, allowing the size of the subpopulation to change automatically when the neighborhood switch time is met. To prevent the algorithm from premature convergence, a stagnation detection strategy is introduced to apply a Gaussian perturbation operation to the particles that fall into the neighborhood optimum. Finally, the proposed algorithm is used to solve multimodal multi-objective test problems and compared with existing multimodal multi-objective optimizationalgorithms. The results show that the proposed algorithm can obtain more Pareto solutions when solving different types of multimodal multi-objective functions.
Injecting CO2 into the reservoir can not only improve crude oil recovery but also achieve the goal of CO2 geological storage. It can not only reduce the greenhouse effect but also obtain additional economic benefits. ...
详细信息
Injecting CO2 into the reservoir can not only improve crude oil recovery but also achieve the goal of CO2 geological storage. It can not only reduce the greenhouse effect but also obtain additional economic benefits. From the perspective of minimum miscible pressure, carbon dioxide flooding can be divided into miscible flooding and immiscible flooding, and miscible flooding is widely used in the oil field. The most important condition for miscible flooding is to achieve the minimum miscible pressure (MMP). In the study, combining the particleswarmoptimization (PSO) with Gaussian process regression (GPR), a novel intelligent GPR and particleswarmoptimization (GPR-PSO) method was proposed to establish the model of predicting the MMP of the CO2 and oil system. The model uses the database with more data than in the previous literature, with 365 data points, and the value range of the data is also wider. Moreover, the accuracy of GPR-PSO model was evaluated by statistical error and graph error and compared with the prediction results of existing models. The results show that compared with other models, the GPR-PSO model has higher accuracy and wider application range, the mean absolute relative error is only 1.66%. Meanwhile, the reliability of the model is verified by the sensitivity analysis of parameters. The results show that the most influential parameter on the prediction results is the reservoir temperature, and the least influential parameter is the critical temperature of injected gas. The GPR-PSO model can be used not only to predict the MMP of CO2 and oil system but also to predict the MMP of other gases and crude oil system.
With the development of information technology, computer technology has been increasingly applied to management. This study aims to address problems such as low efficiency, high cost, and unstable quality in engineeri...
详细信息
With the development of information technology, computer technology has been increasingly applied to management. This study aims to address problems such as low efficiency, high cost, and unstable quality in engineering project management. A research proposes an optimization method for engineering project efficiency management based on multi-objective particle swarm optimization algorithm. The research can find a balance between cost, schedule and quality through multi-objective optimization, thereby achieving the maximization of comprehensive benefits. The multi-objective particleswarmalgorithm combines a segmented pulse module and a cloud adaptation module. The segmented pulse module is used to improve the global search capability, while the cloud adaptation module achieves fast convergence and global optimization by dynamically adjusting inertia weights. The experimental results showed that the distribution index of the multi-objective particleswarmalgorithm was 0.084, and the average convergence index was 0.33. After optimizing the application of a new model in a certain construction project, the cost decreased from 651,100 yuan to 456,500 yuan. The construction time was reduced from 132 days to 106.19 days. The quality coefficient of the main body increased from 0.78 to 0.96, an increase of 23.08 %. This results indicated that the particle swarm optimization algorithm could provide efficient, cost-effective, and high-quality optimization solutions for engineering project management. The new model could effectively reduce the engineering cost, significantly shorten the construction time, and improve the construction quality coefficient. In addition, in medical resource allocation, multi-objective particle swarm optimization algorithm can optimize the resource allocation plan, balancing fairness and efficiency. In transportation planning, multi-objective particle swarm optimization algorithm can optimize path planning, improve transportation efficiency and re
In the present work, a new hybrid approach combining particleswarmoptimization (PSO) algorithm with recurrent dynamic neural network (RDNN), which is described as PSO-RDNN algorithm, is proposed for multi-performanc...
详细信息
In the present work, a new hybrid approach combining particleswarmoptimization (PSO) algorithm with recurrent dynamic neural network (RDNN), which is described as PSO-RDNN algorithm, is proposed for multi-performance optimization of machining parameters in finish turning of hardened AISI D2. The suggested optimization problem is solved using the weighted sum technique. Process parameters including cutting speed and feed rate are optimized for minimizing operation cost, maximizing tool life, and producing parts with acceptable surface roughness. Based on experimental results, two neural network models were developed for predicting tool flank wear and surface roughness during the machining process. Based on trained neural networks and structured hybrid algorithm, optimum cutting parameters were obtained. The coefficient of determination for trained neural networks was calculated as R-2 = 0.9893 and R-2 = 0.9879 for predicted flank wear and surface roughness, respectively, which proves the efficiency of trained neural models in real industrial applications. Furthermore, the offered methodology returns a Pareto optimality graph, which represents optimized cutting variables for several various cutting conditions.
Percutaneous puncture interventional therapy is an important method for pathological examination, local anesthesia, and local drug delivery in modern clinics. Due to the existence of complex obstacles such as nerves, ...
详细信息
Percutaneous puncture interventional therapy is an important method for pathological examination, local anesthesia, and local drug delivery in modern clinics. Due to the existence of complex obstacles such as nerves, arteries, bones and so on in the puncture path, it is a challenging work to design the optimal path for surgical needle. In this paper, we propose a new path planning method based on the adaptive intelligent particleswarmoptimization (PSO) algorithm with parameter adjustment mechanism. First, force and motion analysis are carried out on the bevel-tip flexible needle after piercing into human tissues, the motion model of the needle and the spatial transformation model of puncture route in three-dimensional space are obtained, respectively. Then, a multi-objective function is established, which includes puncture path length function, puncture error function and collision detection function. Finally, the optimal puncture path is obtained based on the adaptive intelligent PSO algorithm. The simulation results show that the newly proposed path planning method has higher efficiency, better adaptability to complex environments and higher accuracy than other path planning methods in literature.
In this paper, attitude tracking control of spacecraft is studied. Firstly, the fully actuated system models of rigid body satellite is established, and then the linear sliding mode controller is designed by direct pa...
详细信息
ISBN:
(纸本)9798350332162
In this paper, attitude tracking control of spacecraft is studied. Firstly, the fully actuated system models of rigid body satellite is established, and then the linear sliding mode controller is designed by direct parameterization method. One of the key problems in direct parameterization is the tuning of matrix parameters. Aiming at the difficulty of parameter setting of spacecraft attitude controller based on fully actuated system approach, a parameter setting method based on particle swarm optimization algorithm was proposed. particleswarmoptimization is used to adjust matrix parameters more quickly and accurately. At the same time, inertia weight factor is introduced to avoid the early local optimal phenomenon of particle swarm optimization algorithm. Simulation results reveal the effect of the proposed control approach.
Based on the theoretic study of location of general facilities, this paper makes an attempt to optimize the typical discrete element of the location of aviation rescue base through discrete binary particleswarm (PSO)...
详细信息
ISBN:
(纸本)9783642385247;9783642385230
Based on the theoretic study of location of general facilities, this paper makes an attempt to optimize the typical discrete element of the location of aviation rescue base through discrete binary particleswarm (PSO) algorithm in order to find out an optimized location method with more simplified calculation and more optimized result, which will finally provide a solid theoretical foundation for the location of aviation rescue base.
During the residual life evaluation process of the cable, since the evaluation factor is the weight value calculation exists, there is a lower accuracy of the evaluation result, and therefore, the residual life assess...
详细信息
optimization is an interesting method that finds the optimal response (i.e., objective function) by searching the decision variables in the search space. The present study deals with the optimal design of a labyrinth ...
详细信息
optimization is an interesting method that finds the optimal response (i.e., objective function) by searching the decision variables in the search space. The present study deals with the optimal design of a labyrinth spillway (LS) having half-round or quarter-round crest shape. For optimal the design of the spillway geometry, an evolutionary Hybrid algorithm (HA) of Bat algorithm (BA) and particleswarmoptimization (PSO) algorithm was developed. By this procedure, the worst responses of one algorithm are replaced by the best responses of the other algorithm. This HA does not get trapped in local minima and has high convergence rate concerning the optimal absolute response. Results of using HA showed that LS with half-round crest requires less concrete, compared to quarter-round crest. Moreover, the discharge capacity for LS with half-round crest was more than the quarter-round crest. The HA significantly reduced the computational time for the optimal design of LS with half-round or quarter-round crest, as compared to BA or PSO.
In most of the test suite minimization techniques, either the size minimization is more or the fault detection is more. But a combination of both would yield better qualified reduced test suite. This paper presents a ...
详细信息
ISBN:
(纸本)9781479915941;9781479915958
In most of the test suite minimization techniques, either the size minimization is more or the fault detection is more. But a combination of both would yield better qualified reduced test suite. This paper presents a technique where the size minimization is obtained through the optimizationalgorithm, particleswarmoptimization and the Fault Detection Effectiveness is obtained through Concept Analysis. In spite of our algorithm producing results similar to Genetic algorithm, the computation time of our algorithm is simple and improves the fault detection capacity. The experimental results indicate that PSO outperforms GAs for most code elements to be covered in terms of effectiveness and efficiency.
暂无评论