Particle Swarm Optimization For Problems Research Articles

A new method of smoothness‐constrained magnetotelluric modelling with the utility of Pareto‐optimal multi‐objective particle swarm optimization

AbstractParticle swarm optimization, one of the modern global optimization methods, is attracting widespread interest because it overcomes the difficulties of conventional inversion techniques, such as trapping at a local minimum and/or initial model dependence. The main characteristic of particle swarm optimization is the large search space of parameters, which in a sense allows the exploration of the entire objective function space if the input parameters are properly chosen. However, in the case of a high‐dimensional model space, the numerical instability of the solution may increase and lead to unrealistic models and misinterpretations due to the sampling problem of particle swarm optimization. Therefore, smoothness‐constrained regularization techniques used for the objective function or model reduction techniques are required to stabilize the solution. However, weighting and combining objective function terms is partly a subjective process, as the regularization parameter is generally chosen based on some kind of criteria of how the smoothing constraints affect the data misfits. This means that it cannot be completely predefined but needs to be adjusted during the inversion process, which begins with the response of an initial model. In this paper, a new modelling approach is proposed to obtain a smoothness‐constrained model from magnetotelluric data utilizing multi‐objective particle swarm optimization based on the Pareto optimality approach without using a regularization parameter and combining several objective function terms. The presented approach was verified on synthetic models and an application with field data set from the Çanakkale–Tuzla geothermal field in Turkey. Findings from these analyses confirm the usefulness of the method as a new approach for all constrained inversions of geophysical data without the need to combine the objective function terms weighted by a regularization parameter.

Risk Assessment of Deep Coal and Gas Outbursts Based on IQPSO-SVM.

Coal and gas outbursts seriously threaten the mining safety of deep coal mines. The evaluation of the risk grade of these events can effectively prevent the occurrence of safety accidents in deep coal mines. Characterized as a high-dimensional, nonlinear, and small-sample problem, a risk evaluation method for deep coal and gas outbursts based on an improved quantum particle swarm optimization support vector machine (IQPSO-SVM) was constructed by leveraging the unique advantages of a support vector machine (SVM) in solving small-sample, high-dimension, and nonlinear problems. Improved quantum particle swarm optimization (IQPSO) is used to optimize the penalty and kernel function parameters of SVM, which can solve the optimal local risk and premature convergence problems of particle swarm optimization (PSO) and quantum particle swarm optimization (QPSO) in the training process. The proposed algorithm can also balance the relationship between the global search and local search in the algorithm design to improve the parallelism, stability, robustness, global optimum, and model generalization ability of data fitting. The experimental results prove that, compared with the test results of the standard SVM, particle swarm optimization support vector machine (PSO-SVM), and quantum particle swarm optimization support vector machine (QPSO-SVM) models, IQPSO-SVM significantly improves the risk assessment accuracy of coal and gas outbursts in deep coal mines. Therefore, this study provides a new idea for the prevention of deep coal and gas outburst accidents based on risk prediction and also provides an essential reference for the scientific evaluation of other high-dimensional and nonlinear problems in other fields. This study can also provide a theoretical basis for preventing coal and gas outburst accidents in deep coal mines and help coal mining enterprises improve their safety management ability.

Path planning of mobile robot based on particle swarm optimization algorithm

Aiming at the premature convergence problem of particle swarm optimization (PSO) due to the loss of population diversity in the late stage of search, this paper improves the traditional PSO and proposes the elite mean deviation induction strategy, which improves the convergence speed and accuracy of PSO. The experimental results show that the convergence speed and accuracy of the improved PSO are improved. The effectiveness of the improved algorithm is proved.

Optimization Design of Wind Turbine Blade Based on an Improved Particle Swarm Optimization Algorithm Combined with Non-Gaussian Distribution

To overcome the problem of particle swarm optimization (PSO) being trapped in local minima, a particle swarm optimization algorithm combined with non-Gaussian stochastic distribution is presented for optimization design of wind turbine blade. Before updating the particle velocity, a limited test was performed for every particle to search for the global best solution. Taking the maximum wind turbine annual power generation as the final objective, a 1.3 MW wind turbine blade was optimized. The results were compared with those of the original wind turbine blades and traditional particle swarm optimization. Compared with the original output power, the year output power increased by 5.3% with non-Gaussian stochastic distribution combined with PSO, whereas the computation time was 65% of the traditional PSO. As time step increased, residuals of non-Gaussian stochastic distribution combined with PSO greatly diminished, with improved computation efficiency. It is shown that the non-Gaussian distribution combined with PSO ensures the global best solution. The non-Gaussian distribution combined with PSO provides a more reliable theoretical basis for the design of wind turbine blades.

A Novel Particle Swarm Optimization with Genetic Operator and Its Application to TSP

To solve some problems of particle swarm optimization, such as the premature convergence and falling into a sub-optimal solution easily, we introduce the probability initialization strategy and genetic operator into the particle swarm optimization algorithm. Based on the hybrid strategies, we propose a improved hybrid particle swarm optimization, namely IHPSO, for solving the traveling salesman problem. In the IHPSO algorithm, the probability strategy is utilized into population initialization. It can save much more computing resources during the iteration procedure of the algorithm. Furthermore, genetic operators, including two kinds of crossover operator and a directional mutation operator, are used for improving the algorithm’s convergence accuracy and population diversity. At last, the proposed method is benchmarked on 9 benchmark problems in TSPLIB and the results are compared with 4 competitors. From the results, it is observed that the proposed approach significantly outperforms others on most the 9 datasets.

A Novel Particle Swarm Optimization With Genetic Operator and Its Application to TSP

To solve some problems of particle swarm optimization, such as the premature convergence and falling into a sub-optimal solution easily, we introduce the probability initialization strategy and genetic operator into the particle swarm optimization algorithm. Based on the hybrid strategies, we propose a improved hybrid particle swarm optimization, namely IHPSO, for solving the traveling salesman problem. In the IHPSO algorithm, the probability strategy is utilized into population initialization. It can save much more computing resources during the iteration procedure of the algorithm. Furthermore, genetic operators, including two kinds of crossover operator and a directional mutation operator, are used for improving the algorithm’s convergence accuracy and population diversity. At last, the proposed method is benchmarked on 9 benchmark problems in TSPLIB and the results are compared with 4 competitors. From the results, it is observed that the proposed approach significantly outperforms others on most the 9 datasets.

Research on Logistics Distribution in E-commerce Environment Based on Particle Swarm Optimization Algorithm

In order to improve the efficiency of logistics distribution under electronic commerce environment and reduce the logistics cost of distribution path length and time delay, to solve the problem of logistics distribution method at present larger and better convergence, a logistics distribution method in e-commerce environment is proposed based on particle swarm optimization algorithm, the characteristics of construction behavior the characteristics of logistics distribution under the e-commerce environment, the logistics distribution problem into a multi-objective optimization problem of particle swarm optimization. Based on the IOT technology, to establish a stable and efficient logistics supply chain structure, according to the interval distribution characteristics of e-commerce products to the distribution of the time interval of extraction group behavior of particles, the adaptive path characterized the amount of logistics distribution optimization and implementation under the environment of e-commerce logistics distribution path optimization, improve the coverage level of logistics and distribution of the simulation nodes. The results show that the method has stronger spatial search ability, it has higher convergence accuracy and faster search speed, and reduces the time cost and path overhead of logistics delivery.

Порівняння ефективності підходів MAP-REDUCE і акторної моделі при розв’язанні задач з високою зв’язністю вхідних даних на прикладі задачі оптимізації рою часток

The paper defines the notion of distributed problems with bounded input components. Particle Swarm Optimization problem is shown to be an example of such a class. Such a problem's implementation based on the Map-Reduce model (implemented on the Spark framework) and an implementation based on an actor model with shared memory support (implemented on Strumok DSL) is provided. Both versions' performance assessment is conducted. The hybrid actor model is shown to be an order of magnitude more effective in time and memory efficiency than Map-Reduce implementation. Additional optimization for the hybrid actor model solution is proposed. The prospects of using the hybrid actor model for other similar problems are given

Research on Large-Scale Bi-Level Particle Swarm Optimization Algorithm

Targeting at the slow convergence and the local optimum problems of particle swarm optimization (PSO), a large-scale bi-level particle swarm optimization algorithm is proposed in this paper, which enlarges the particle swarm scale and enhances the initial population diversity on the basis of multi-particle swarms. On the other hand, this algorithm also improves the running efficiency of the particle swarms by the structural advantages of bi-level particle swarms, for which, the upper-level particle swarm provides decision-making information while the lower level working particle swarms run at the same time, enhancing the operation efficiency of particle swarms. The two levels of particle swarms collaborate and work well with each other. In order to prevent population precocity and slow convergence in the later stage, an accelerated factor based on increasing exponential function is applied at the same time to control the coupling among particle swarms. And the simulation results show that the large-scale bi-level particle swarm optimization algorithm is featured in better superiority and stability.

An Enhanced and Automatic Skin Cancer Detection using K-Mean and PSO Technique

Scientists have been trying to implement traditional methods around the world, particularly in developing countries, to reduce the death rate of skin cancer in humans. The scientific term is named as melanoma. But this effort always working hard as the system is costly, the low availability of experts and the conventional telemedicine. There are three types of skin cancer: basal cell cancer (BCC), squamous cell cancer, and melanoma. More than 90% of human is affected by ultraviolet (UV) radiation exposed to the sun. In this research, a skin cancer detection system (BCC) is designed in MATLAB. The images going to different processes such as Pre processing, feature extraction and classification. In pre-processing K-mean clustering is applied to determine the foreground and background of an image, since some part of background appear in the image after K-mean. Therefore, to resolve this problem Particle Swarm optimization (PSO) is applied. The segmented image features are extracted using Speed Up Robust Features (SURF), this helps to enhance the quality of the image. The Artificial neural network (ANN) is trained on the basis of these extracted features. To determine the efficiency of the system, the images are tested and performance parameters are measured. The detection accuracy determined by this model is about 98.7 5 is obtained.

A simplified and efficient particle swarm optimization algorithm considering particle diversity

In this paper, a dynamic self-adapting and simple particle swarm optimization algorithm with the disturbed extremum and crossover is proposed in order to improve the problem of particle swarm optimization in dealing with high-dimensional multi-extremum problem which is easy to fall into the local extremum and the accuracy of search and speed of the rapid decline problem in the late evolution. The dynamic self-adapting inertia weight and simplified speed equation strategy reduce the computational difficulty of the algorithm and improve the problem of slow convergence and low precision of the evolutionary algorithm due to the particle divergence caused by the velocity term; Extreme value perturbation and hybridization strategies are used to adjust the global extremes and individual positions of the particles to ensure the diversity and vigor of the particles in the late evolutionary period, and improve the ability of the particles to get rid of the local extremes. Three sets of computational experiments are carried out to compare and evaluate the search speed, convergence accuracy and population diversity of the improved algorithm, the results show that the improved algorithm has obtained a very good optimization effect and improved the practicability of the particle swarm optimization algorithm. It shows that the improved algorithm has improved the search speed, precision and population diversity of the optimization algorithm which improves the practicability of the particle swarm algorithm and achieves the expected effect.

Particle swarm optimization for problems with variable number of dimensions

ABSTRACTSome real-life optimization problems, apart from dependence on the combination of state variables, also show dependence on the complexity of the model describing the problem. Changing model complexity implies changing the number of decision space dimensions.A new method called Particle Swarm Optimization for Variable Number of Dimensions is developed here. The well-known particle swarm optimization procedure is modified to handle spaces with a variable number of dimensions within a single run. Some well-known benchmark problems are modified to depend on the number of dimensions. Novel performance metrics are defined in the article to evaluate convergence properties of the method. Some recommendations for setting the optimization are made according to results of the method on the proposed benchmark test suite. The method is compared with conventional swarm strategies able to solve problems with variable number of dimensions.

Analysis on the Key Parameters of Aerospace Microminiaturization Decelerator

A high efficiency special transmission decelerator is designed by scheme design, structural design, optimization design, and return difference analysis and computer simulation. Small teeth difference transmission with the first level bevel gear pair and the second level beveloid gear pair is adopted as the form of transmission in the decelerating system. Great torque and big bending moment are available by transmission ratio formula deduction, force analysis and strength analysis. The particle swarm optimization and the genetic algorithm have been combined and the mutation operator optimization model has been proposed to optimize the design of decelerator. Then the transmission with large transmission ratio, high torque, high power and high precision will be realized in small space. This model has solved the problems of particle swarm optimization in mechanical design. For example, there are more variables, it is constrained and it is easily precocious. This model has provided the theoretical basis for the optimum design of microminiaturization gear system. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.3399

Approaches based on particle swarm optimization for problems of vibration reduction of suspended mobile robot with a manipulator

Mobile robots with suspension systems can absorb vibration induced by rough roads, but due to center-of-gravity (CG) shift, the suspended platform is subject to vibration when the platform moves with acceleration. This paper presents approaches based on the particle swarm optimization (PSO) algorithm to overcome the following vibration problems: (1) when the suspended platform moves with a static manipulator, vibration of the suspended platform occurs due to CG shift, (2) when the suspended platform and the manipulator move simultaneously, the vibration is caused by the dynamic manipulator. For the first problem, a method for the optimization of multi-input shapers using PSO is adopted with chaos to reduce the residual vibration. For the second problem, an approach based on PSO with chaos is developed to suppress the vibration by searching for the time-jerk synthetic optimal trajectories of the manipulator. Finally, the authors perform the resulting shapers and optimal trajectories on the presented models and demonstrate the vibration can be controlled to a desired level effectively in both problems.

Particle swarm optimization algorithm based on adaptive Tent chaos search

To solve the premature convergence problem of Particle Swarm Optimization(PSO),a new PSO algorithm based on adaptive chaos search was proposed.The uniform particles were produced by Tent mapping so as to improve the quality of the initial solutions.Tent chaotic sequence based an optimal location was produced,and the adaptive adjustment of search scopes can avoid the redundant computation and accelerate the convergence speed of the evolutionary process.The experimental results show that the new introduced algorithm outperforms several other famous improved PSO algorithms on many well-known benchmark problems.

Model to study the effect of workforce on a safety equipment and its optimization

Many industrial sectors are concerned on developing optimal maintenance planning because of the importance of maintenance on the economy and safety. Traditionally, the maintenance planning is formulated in terms of a multi-objective optimization problem where reliability, availability, maintainability and cost act as decision criteria and surveillance tests and maintenance strategies act as decision variables. However, the appropriate development of each maintenance strategy depends not only on the maintenance intervals but also on the resources available to implement such strategies. To solve the multi-objective optimization problem Particle Swarm Optimization (PSO) can be used. PSO is a stochastic global optimization technique inspired by social behavior of bird flocking or fish schooling. In this paper the multi-objective optimization of the maintenance of a nuclear power plant safety equipment using PSO is presented.

Cooperative particle swarm optimization

To solve the premature convergence problem of Particle Swarm Optimization (PSO),two new methods were introduced to improve the performance cooperatively:When particles' fitness values were worse than the average,the dynamic Zaslavskii chaotic map formula was devised to modify the inertia weight and velocity,which can make particles break away from the local best and search the global best dynamically; On the contrary,when fitness values were better than or equal to the average,the introduced dynamic nonlinear functions were used to modify the inertia weight and velocity,which can make particles retain favorable conditions and converge to the global best continually. Two methods coordinate dynamically,and make two dynamic swarms cooperate to evolve. Experimental results demonstrate that the new introduced algorithm outperforms several other famous improved PSO algorithms on many well-known benchmark problems.