Hybrid Algorithm Combining Particle Swarm Research Articles

Prediction of the Permeability Tensor of Marine Clayey Sediment during Cyclic Loading and Unloading of Confinement Pressure Using Physical Tests and Machine Learning Techniques

In this study, a method was introduced to validate the presence of a Representative Elementary Volume (REV) within marine clayey sediment containing cracks during cyclic loading and unloading of confinement pressure. Physical testing provided the basis for this verification. Once the existence of the REV for such sediment was confirmed, we established a machine-learning predictive model. This model utilizes a hybrid algorithm combining Particle Swarm Optimization (PSO) with a Support Vector Machine (SVM). The model was trained using a database generated from the aforementioned physical tests. The machine-learning model demonstrates favorable predictive performance based on several statistical metrics, including the coefficient of determination (R2), mean residual error (MSE), mean relative residual error (MRSE), and the correlation coefficient R during the verification process. Utilizing the established machine-learning predictive model, one can effortlessly obtain the permeability tensor of marine clayey sediment containing cracks during cyclic loading and unloading of confinement pressure by inputting the relevant stress condition parameters. The original research cannot estimate the permeability tensor under similar loading and unloading conditions through REV. In this study, the physical model test was used to determine the REV of marine cohesive sediments with cracks by cyclic-constrained pressure loading and unloading. Referring to the results of physical tests, we developed a machine-learning prediction model that can easily estimate the permeability tensor of marine cohesive sediments with cracks under cyclic loading and constrained pressure unloading conditions. This method greatly saves time and computation and provides a direct method for engineering and technical personnel to predict the permeability tensor in this case.

Collaborative Task Allocation and Optimization Solution for Unmanned Aerial Vehicles in Search and Rescue

Earthquakes pose significant risks to national stability, endangering lives and causing substantial economic damage. This study tackles the urgent need for efficient post-earthquake relief in search and rescue (SAR) scenarios by proposing a multi-UAV cooperative rescue task allocation model. With consideration the unique requirements of post-earthquake rescue missions, the model aims to minimize the number of UAVs deployed, reduce rescue costs, and shorten the duration of rescue operations. We propose an innovative hybrid algorithm combining particle swarm optimization (PSO) and grey wolf optimizer (GWO), called the PSOGWO algorithm, to achieve the objectives of the model. This algorithm is enhanced by various strategies, including interval transformation, nonlinear convergence factor, individual update strategy, and dynamic weighting rules. A practical case study illustrates the use of our model and algorithm in reality and validates its effectiveness by comparing it to PSO and GWO. Moreover, a sensitivity analysis on UAV capacity highlights its impact on the overall rescue time and cost. The research results contribute to the advancement of vehicle-routing problem (VRP) models and algorithms for post-earthquake relief in SAR. Furthermore, it provides optimized relief distribution strategies for rescue decision-makers, thereby improving the efficiency and effectiveness of SAR operations.

Credibility-based chance-constrained multimode resource-constrained project scheduling problem under fuzzy uncertainty

This paper investigates a multimode resource-constrained project scheduling problem under hybrid uncertain environment. With the activity durations are fuzzy variables, the resource requests change with the durations. The goal is to minimize the project duration and the cost associated with the required resources. Considering the stochastic duration and resource demand, a chance constrained programming model based on credibility is established. In order to solve the model, the chance constraints and fuzziness are treated by introducing the credibility distribution function and its inverse function. And, a hybrid algorithm combining particle swarm optimization (PSO) and genetic algorithm (GA) is proposed. It has shown fine performance in solving problems with 30, 60, and 120 activities. We also analyze the sensitivity of confidence, fuzziness, number of activities, number of modes, and resource unit cost. Meanwhile, fuzzy value-at-risk is used as a risk measure, and a model based on it is constructed. These numerical results show that the developed algorithm can obtain the near optimal solution of large-scale projects in a reasonable calculation time.

Hydrodeoxygenation of stearic acid to produce diesel–like hydrocarbons: kinetic modeling, parameter estimation and simulation

• Different assumptions for the adsorption step were tested to develop a suitable kinetic model for the hydrodeoxygenation of stearic acid. • Reaction rates constants, equilibrium adsorption constants and activation energies were estimated. • The model based on non-dissociate H 2 and single-site adsorption provided the best fitting quality. • The Peng-Robinson equation of state had better performance than the Predictive-Soave-Redlich-Kwong equation to calculate hydrogen solubilities in the reaction mixture. There has been an increasing search for renewable sources to produce alternative biofuels due to the growth in the global energy demand and the environmental impacts caused by fossil fuels. Green diesel obtained via hydrotreatment of vegetable oils and fats is a promising option, mainly due to the possibility of being integrated into an existing refinery. Here, we investigate the kinetics of hydrodeoxygenation of stearic acid as a model compound of triglyceride raw materials over NiMo/Al 2 O 3 catalyst to obtain hydrocarbons in the diesel range. Kinetic models based on different assumptions to the adsorption step, such as Eley–Rideal and Langmuir–Hinshelwood mechanisms, were evaluated through nonlinear regression to parameter estimation using a hybrid algorithm combining Particle Swarm Optimization and Nelder–Mead method. All proposed models fitted well to the experimental data, presenting correlation coefficients higher than 90% for the fatty acid and the final hydrocarbons. However, the surface reaction limited model based on competitive adsorption with non-dissociate H 2 adsorbed showed the best results and was used to simulate the process.

Optimization of Shielding Electrode and Inner Shielding Structure for UHV Oil-gas Bushings with Improved Hybrid Algorithm Combining Particle Swarm Optimization and Back-Propagation Neural Network

With the development of prototype for UHV oil-gas bushings, E-field distribution around bushing end and structure optimization for shielding electrode is an important issue to be studied. The basic principles and processes of hybrid algorithm combining particle swarm optimization and back-propagation neural network (PSO-BP algorithm) has been discussed in this paper. The optimization capability and accuracy of proposed algorithm has been verified with continuous explicit function. The structure optimization of shielding electrode has been conducted using PSO-BP algorithm. The study shows that PSO-BP algorithm can seek extreme point of testing function exactly, and jump over trap of local optimal solution; three-dimensional full model of bushing is needed in E-field distribution calculation; PSO-BP algorithm has found the best structure parameters of shielding electrode, with which more even E-field distribution can be obtained, and maximum electric strength can be reduced by 64.9%, moreover, the computing time is about 75.2% less than traditional PSO algorithm. The study results have been applied in bushing prototype manufacture which has passed through all testing experiments. The optimization method proposed in the paper can also be used in optimization design for other complex insulation structures.

Real-Time Detection of Moving Magnetic Target Using Distributed Scalar Sensor Based on Hybrid Algorithm of Particle Swarm Optimization and Gauss–Newton Method

Using a prior data to solve for the characteristic parameters of a moving magnetic target is crucial in its detection. For this purpose, we developed a real-time detection method using distributed scalar sensor networks based on a hybrid algorithm combining particle swarm optimization and the Gauss-Newton method. The magnetic anomaly fitting model of the target is established using the magnetic-dipole-moment principle. The PSO’s insensitivity to the initial solution is used to obtain a rough solution of the coefficients of the fitting, and then a more accurate solution is developed using the Gauss-Newton method, exploiting its advantage in high precision. Furthermore, to evaluate the proposed method, we built a test platform. The detection results show that the goodness-of-fit reaches 0.9565 and only requires 3.4 s; in the 16 m horizontal distance, the calculated trajectory overlaps significantly the actual trajectory, and errors in position and speed are around 10% and 4.25%, respectively.

Integrated Multi-Project Scheduling and Hierarchical Workforce Allocation in the ETO Assembly Process

The engineer-to-order (ETO) production strategy plays an important role in today’s manufacturing industry. This paper studies integrated multi-project scheduling and hierarchical workforce allocation in the assembly process of ETO products. The multi-project scheduling problem involves the scheduling of tasks of different projects under many constraints, and the workforce allocation problem involves assigning hierarchical workers to each task. These two problems are interrelated. The task duration depends on the number of hierarchical workers assigned to the task. We developed a mathematical model to represent the problem. In order to solve this issue with the minimization of the makespan as the objective, we propose a hybrid algorithm combining particle swarm optimization (PSO) and Tabu search (TS). The improved PSO is designed as the global search process and the Tabu search is introduced to improve the local searching ability. The proposed algorithm is tested on different scales of benchmark instances and a case that uses industrial data from a collaborating steam turbine company. The results show that the solution quality of the hybrid algorithm outperforms the other three algorithms proposed in the literature and the experienced project manager.

Integrated Multi-project Scheduling and Worker Allocation for the Assembly of Steam Turbines

The steam turbine is typically an engineer-to-order product, the assembly of steam turbine is mainly performed by a group of workers. In this paper, we investigate the integrated multi-project scheduling and worker allocation for the assembly of steam turbine. These two problems are interrelated as the tasks durations are not predefined, but depend on the number of workers assigned to that task as well as their skill levels. We develop a mathematical model to represent the problem. In order to solve with the minimization of makespan as the objective, we propose the hybrid algorithm combining particle swarm optimization (PSO) and simulated annealing (SA). The proposed algorithm is tested on the case that uses industrial data from a steam turbine company. The experimental results show that the solution quality of the hybrid algorithm outperform the other three traditional metaheuristic algorithms.

A hybrid algorithm of product-service framework for the multi-project scheduling in ETO assembly process

The engineer-to-order (ETO) production strategy plays an important role in today’s manufacturing industry. This paper studies the multi-project scheduling in assembly process of ETO products. The multi-project scheduling problem is to schedule tasks of different projects under many constraints and assign hierarchical workers to each task. We develop a mathematical model to represent the problem. In order to solve with the minimization of makespan as the objective, we propose a hybrid algorithm combining particle swarm optimization (PSO) and Tabu search (TS). The improved PSO is designed as the global search process and the Tabu search is introduced to improve the local searching ability. The proposed algorithm is tested on different scale benchmark instances and the case that uses industrial data from a collaborating steam turbine company. The results show that the solution quality of the hybrid algorithm outperforms the other three algorithms proposed in the literature and the experienced project manager.

On the estimation and optimization capabilities of the fatigue life prediction models in composite laminates

In this study, the estimation and optimization capabilities of the multiaxial fatigue life prediction models, namely, Failure Tensor Polynomial in Fatigue, Fawaz–Ellyin, Sims–Brogdon and Shokrieh–Taheri are investigated comparatively. Fatigue life predictions are obtained for multidirectional graphite/epoxy, glass/epoxy, carbon/epoxy and carbon/PEEK composite laminate data taken from the literature. The prediction study shows that the models can predict the fatigue behavior of the multidirectional laminates at different degrees of proximity. In the optimization, a hybrid algorithm combining particle swarm algorithm and generalized pattern search algorithm is used to search the optimum stacking sequence designs of the laminated composites for maximum fatigue life. The hybrid algorithm shows superior performance in terms of computational time and finding improved global optima compared to the best results presented in the literature. After the capability of the models and the reliability of the algorithm are revealed, several lay-up design problems involving different cyclic loading scenarios are solved. The results indicate that the reliability of the optimization may considerably change according to the used model even if the model may yield reasonable prediction results.

PSO and simulated annealing for the two-machine flowshop scheduling problem with coupled-operations

In this paper, we proposed an hybrid algorithm combining particle swarm optimisation with the simulated annealing (PSO-SA) to solve the flowshop problem with coupled-operations in the presence of the time lags. The criterion to minimise is the makespan. To verify the performance of the hybridised algorithm, computational experiments are performed and the obtained results are compared with the simulated annealing and PSO algorithms. However, the experimental results show that the PSO-SA performs better than the others approaches. [Received: 6 May 2017; Revised: 20 July 2017; Revised: 17 September 2017; Accepted: 16 September 2017]

Optimization of a Heliostat Field Layout on Annual Basis Using a Hybrid Algorithm Combining Particle Swarm Optimization Algorithm and Genetic Algorithm

Of all the renewable power generation technologies, solar tower power system is expected to be the most promising technology that is capable of large-scale electricity production. However, the optimization of heliostat field layout is a complicated process, in which thousands of heliostats have to be considered for any heliostat field optimization process. Therefore, in this paper, in order to optimize the heliostat field to obtain the highest energy collected per unit cost (ECUC), a mathematical model of a heliostat field and a hybrid algorithm combining particle swarm optimization algorithm and genetic algorithm (PSO-GA) are coded in Matlab and the heliostat field in Lhasa is investigated as an example. The results show that, after optimization, the annual efficiency of the heliostat field increases by approximately six percentage points, and the ECUC increases from 12.50 MJ/USD to 12.97 MJ/USD, increased about 3.8%. Studies on the key parameters indicate that: for un-optimized filed, ECUC first peaks and then decline with the increase of the number of heliostats in the first row of the field (Nhel1). By contrast, for optimized field, ECUC increases with Nhel1. What is more, for both the un-optimized and optimized field, ECUC increases with tower height and decreases with the cost of heliostat mirror collector.

Minimax and Minimax Projection Designs Using Clustering

ABSTRACTMinimax designs provide a uniform coverage of a design space by minimizing the maximum distance from any point in this space to its nearest design point. Although minimax designs have many useful applications, for example, for optimal sensor allocation or as space-filling designs for computer experiments, there has been little work in developing algorithms for generating these designs, due to its computational complexity. In this article, a new hybrid algorithm combining particle swarm optimization and clustering is proposed for generating minimax designs on any convex and bounded design space. The computation time of this algorithm scales linearly in dimension p, meaning our method can generate minimax designs efficiently for high-dimensional regions. Simulation studies and a real-world example show that the proposed algorithm provides improved minimax performance over existing methods on a variety of design spaces. Finally, we introduce a new type of experimental design called a minimax projection design, and show that this proposed design provides better minimax performance on projected subspaces of compared to existing designs. An efficient implementation of these algorithms can be found in the R package minimaxdesign. Supplementary material for this article is available online.

Cooperative rendezvous between two spacecraft under finite thrust

Dynamic equations of orbital elements of a modified vernal equinox for a far-distance cooperative rendezvous between two spacecraft were set up in this paper. The process of the far-distance cooperative rendezvous was optimized by a hybrid algorithm combining particle swarm optimization and differential evolution. The convergent costate vectors were obtained and set as the initial values of sequential quadratic programming to search for precise solutions, and the results proved to be stable and convergent. It can be seen from the results that the flight time of the cooperative rendezvous would be largely saved the amplitude of the thrust would be increased if the other conditions are fixed, and the fuel consumption would not be increased. However, the flight time would no longer decrease when the amplitude of the thrust reaches a certain value. In the last section of this paper, cooperative rendezvous and active–passive rendezvous were compared and analyzed, showing the advantages of cooperative rendezvous when the initial conditions are the same.

Feed-Forward Neural Network Soft-Sensor Modeling of Flotation Process Based on Particle Swarm Optimization and Gravitational Search Algorithm.

For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, a feed-forward neural network (FNN) based soft-sensor model optimized by the hybrid algorithm combining particle swarm optimization (PSO) algorithm and gravitational search algorithm (GSA) is proposed. Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum. So in this paper, the velocity vector and position vector of GSA are adjusted by PSO algorithm in order to improve its convergence speed and prediction accuracy. Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model. Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the flotation process.

Hybrid particle swarm optimization with chaotic search for solving integer and mixed integer programming problems

A novel chaotic search method is proposed, and a hybrid algorithm combining particle swarm optimization (PSO) with this new method, called CLSPSO, is put forward to solve 14 integer and mixed integer programming problems. The performances of CLSPSO are compared with those of other five hybrid algorithms combining PSO with chaotic search methods. Experimental results indicate that in terms of robustness and final convergence speed, CLSPSO is better than other five algorithms in solving many of these problems. Furthermore, CLSPSO exhibits good performance in solving two high-dimensional problems, and it finds better solutions than the known ones. A performance index (PI) is introduced to fairly compare the above six algorithms, and the obtained values of (PI) in three cases demonstrate that CLSPSO is superior to all the other five algorithms under the same conditions.

Time-optimal trajectory planning for underactuated spacecraft using a hybrid particle swarm optimization algorithm

A hybrid algorithm combining particle swarm optimization (PSO) algorithm with the Legendre pseudospectral method (LPM) is proposed for solving time-optimal trajectory planning problem of underactuated spacecrafts. At the beginning phase of the searching process, an initialization generator is constructed by the PSO algorithm due to its strong global searching ability and robustness to random initial values, however, PSO algorithm has a disadvantage that its convergence rate around the global optimum is slow. Then, when the change in fitness function is smaller than a predefined value, the searching algorithm is switched to the LPM to accelerate the searching process. Thus, with the obtained solutions by the PSO algorithm as a set of proper initial guesses, the hybrid algorithm can find a global optimum more quickly and accurately. 200 Monte Carlo simulations results demonstrate that the proposed hybrid PSO–LPM algorithm has greater advantages in terms of global searching capability and convergence rate than both single PSO algorithm and LPM algorithm. Moreover, the PSO–LPM algorithm is also robust to random initial values.

A hybrid discrete particle swarm optimization algorithm for solving fuzzy job shop scheduling problem

In this paper, a hybrid algorithm combining particle swarm optimization (PSO) and tabu search (TS) is proposed to solve the job shop scheduling problem with fuzzy processing time. The object is to minimize the maximum fuzzy completion time, i.e., the fuzzy makespan. In the proposed algorithm, PSO performs the global search, i.e., the exploration phase, while TS conducts the local search, i.e., the exploitation process. The global best particle is used to direct other particles to optimal search space. Therefore, in the proposed algorithm, TS-based local search approach is applied to the global best particle to conduct find-grained exploitation. In order to share information among particles, one-point crossover operator is embedded in the hybrid algorithm. The proposed algorithm is tested on sets of the well-known benchmark instances. Through the analysis of experimental results, the highly effective performance of the proposed algorithm is shown against the best performing algorithms from the literature.

A combination of Genetic Algorithm, Particle Swarm Optimization and Neural Network for palmprint recognition

In this study, a new approach to the palmprint recognition phase is presented. 2D Gabor filters are used for feature extraction of palmprints. After Gabor filtering, standard deviations are computed in order to generate the palmprint feature vector. Genetic Algorithm-based feature selection is used to select the best feature subset from the palmprint feature set. An Artificial Neural Network (ANN) based on hybrid algorithm combining Particle Swarm Optimization (PSO) algorithm with back-propagation algorithms has been applied to the selected feature vectors for recognition of the persons. Network architecture and connection weights of ANN are evolved by a PSO method, and then, the appropriate network architecture and connection weights are fed into ANN. Recognition rate equal to 96% is obtained by using conjugate gradient descent algorithm.

An Improved Hybrid Algorithm Based on PSO and BP for Feedforward Neural Networks

In this paper, an improved hybrid algorithm combining particle swarm optimization (PSO) with backpropagation algorithm (BP) is proposed to train feedforward neural networks (FNN). PSO is a global search algorithm, but the swarm in PSO is easy to lose its diversity, which results in premature convergence. On the other hand, BP algorithm is a gradient-descent-based method which has good local search ability around the global minima. Hence, the presented algorithm in this study combines PSO with BP to perform double search. Moreover, in order to improve the diversity of the swarm in the PSO, each particle in the swarm and its best position are disturbed by a random function, and the best position of all particles are reset as the optimum weights of FNN obtained by BP. The proposed algorithm improves the diversity of the swarm as well as reduces the likelihood of the swarm being trapped into local minima on the error surface. Compared with the traditional learning algorithms, the improved learning algorithm has much better convergence accuracy and rate. Finally, the experimental results are given to verify the efficiency and effectiveness of the proposed algorithm.