Marine radar-based oil spill detection method utilizing particle swarm optimization algorithm

In order to study Particle Swarm Optimization (PSO) algorithm and structural damage diagnosis, a method based on PSO algorithm and Evidence theory is presented in this paper. First, structural frequency and modal strain energy are considered as two kinds of information sources, and frequency change method and modal strain energy method are utilized to extract damage information. Then, evidence theory is utilized to integrate the two information sources and preliminarily detect structural damage locations. Finally, the PSO algorithm is used to identify structural damage extent. An improved PSO algorithm is also presented. Simulation results show that the evidence theory can identify the suspected damage locations, and the PSO algorithm can precisely detect the damage extent. It can also be observed that the improved PSO algorithm is obviously better than the simple PSO algorithm.

Multi unmanned combat aerial vehicle (UCAV) cooperative task assignment ,which plays a very important role in improving the efficiency UCAV gross operational utility, is a complex multiple objective optimization problem. In order to get the Pareto optimal solution, a novel method for solving the UCAVs' cooperative task assignment problem was proposed using an improved particle swarm optimization(PSO) algorithm with parallelism, high resolution and high efficiency . The improved PSO splits the whole particle swarm into several sub-swarms of which each sub-swarm evolves respectively in the first stage, afterwards each sub-swarm was emerged into one swarm and evolves in the second stage. Simulation results show that the algorithm improved the capability got better and more precise UCAVs cooperative attack path than the basic PSO algorithm .

With the development towards high-speed, high-capacity, high-density networking of high-speed railway, harmonic problems caused by high-speed locomotives are getting more and more serious, which has been a great threaten to the safety and stability of power system operation. The way of elimination on the influence of railway becomes increasingly important. Due to the diseconomy to install passive filters around the whole power grid, this paper presents a harmonic suppression method using the optimal configuration of passive power filter based on particle swarm optimization (PSO) method in the power grid affected by the electrified railways. At first, this paper makes an evaluation on the impacts on power system with railway both in theory and the data measured by PQView software. It makes an introduction and analysis on the methods of harmonic suppression affected by electrified railways on the power grid, and the importance of harmonic governance affected by electrified railways on the power grid was emphasized. Then, it makes a detail analysis on the theory of harmonic suppression using passive power filter based on PSO method affected by electrified railways on the power grid. Besides, according to the characteristic of Taotang power grid (Taotang power grid is an actual area network affected by the electrified railways) and the influence of harmonics on the Taotang power grid, a mathematical model is built considering the economy and effect of passive filters as the optimal objective. Then, an optimal configuration of passive filters in the Taotang power grid is given through optimizing by the PSO algorithm. Besides, the simulations and analysis are provided, and by comparing the harmonic data before the harmonic suppression using passive filters based on PSO method with the harmonic data after the optimal configuration of passive filters based on PSO algorithm, it is showed that the optimal configuration of passive power filter based on particle swarm optimization (PSO) algorithm has obvious effect on the harmonic suppression and the proposed method is validity, which provides a theoretical and engineering reference for the improvement of the harmonic in power network affected by electrified railways.

The path-planning in radioactive environment of nuclear facilities using an improved particle swarm optimization algorithm

The particle swarm optimisation (PSO) algorithm has been widely used in hull form optimisation owing to its feasibility and fast convergence. However, similar to other intelligent algorithms, PSO also has the disadvantages of local premature convergence and low convergence performance. Moreover, optimization data are not used to analyse and reduce the range of values for relevant design variables. Our study aimed to solve these existing problems in the PSO algorithm and improve PSO from four aspects, namely data processing of particle swarm population initialisation, data processing of iterative optimisation, particle velocity adjustment, and particle cross-boundary configuration, in combination with space reduction technology. The improved PSO algorithm was used to optimise the hull form of an engineering vessel at Fn = 0.24 to reduce the wave-making resistance coefficient under static constraints. The results showed that the improved PSO algorithm could effectively improve the optimisation efficiency and reliability of PSO and effectively overcome the drawbacks of the PSO algorithm.

The set of permissions required by any Android app during installation time is considered as the feature set which are used in permission based detection of Android malwares. Those high dimensional feature set should be reduced to minimize computational overhead by choosing an optimal sub set of features. In recent times, selection of meaningful attributes is an inevitable step for mining of large dimensional data and the application of heuristic feature selection algorithms are the main research directions in this field. “Quality of classification” measure is inspired by rough set theory and can be combined with bio inspired heuristic search techniques (Particle swarm optimization, Genetic Algorithm etc.) in selecting optimal or near optimal subsets of features. In this work, a feature selection technique based on rough set and improvised particle swarm optimization (PSO) algorithm is proposed for selection of features in the permission based detection of Android malwares. The main contribution of this work is to recommend a new random key encoding method which is used in the proposed work (PSORS-FS) to convert classical PSO algorithm in discrete domain. It also reduces the issues related to maximum velocity of particles as well as sigmoid function which is related with binary PSO. PSORS-FS ensures diversity in the search process and it also reduces the tendency of premature convergence. Datasets of UCI, KEEL machine learning repository and two Android permission datasets have been used to evaluate the performance of the proposed method. Better classification performance has been yielded by proposed method over conventional filters and wrapper methods for most of the machine learning classifiers.

Position control of nonlinear hydraulic system using an improved PSO based PID controller

The application of 3D visualization technology in building construction has also increased. The study used Revit software to construct a 3D building information model (BIM) for the exhibition space of Chuzhou Higher Education City Development Collaborative Innovation Center to achieve a 3D visualization display; based on the 3D visualization, a particle swarm optimization (PSO) algorithm was used to find the optimal path for the exhibition space, so as to achieve the layout design of the exhibition space. The PSO algorithm was optimized in terms of inertia weight, acceleration coefficient, and initial population to obtain the improved PSO (IPSO) algorithm. The experimental results showed that the optimal path found by the IPSO algorithm was 78.56 meters in distance, 98.2 seconds in time consumption, and 50.11% in smoothness, which were better than the other two algorithms. Meanwhile, the IPSO algorithm had a lower value of particle fitness function, indicating that the IPSO algorithm had the highest performance and the strongest path finding ability among the three algorithms. It is confirmed that it is feasible to use the IPSO algorithm for optimal visit path finding in 3D environment. It is effective to visualize the exhibition space in 3D by constructing a BIM.

This paper briefly introduces the optimal threshold calculation model and particle swarm optimization (PSO) algorithm for image segmentation and improves the PSO algorithm. Then the standard PSO algorithm and improved PSO algorithm were used in MATLAB software to make simulation analysis on image segmentation. The results show that the improved PSO algorithm converges faster and has higher fitness value; after the calculation of the two algorithms, it is found that the improved PSO algorithm is better in the subjective perspective, and the image obtained by the improved PSO segmentation has higher regional consistency and takes shorter time in the perspective of quantitative objective data. In conclusion, the improved PSO algorithm is effective in image segmentation.

Based on the relationship between parameter speed \( v_{\hbox{max} } \) and inertia factor \( \omega \) in the particle swarm optimization (PSO) algorithm, the improved PSO algorithm that is time-varying nonlinear trigonometric function to control PSO algorithm parameters is proposed to prevent the particles fall into local optimum, and the improved PSO algorithm is used to optimize parameters of PID controller which is used in course control for underactuated surface vessels (USV). Two different PSO algorithms for optimizing parameters of PID controller are compared in this paper, and the results of simulation experiments show that, according to dynamic characteristics changes of USV, the controller which used improved PSO algorithm can optimize the adaptive parameters well and automatically, and has a high tracking speed, small overshoot and strong immunity etc.

The finite-difference (FD) scheme is extensively applied in seismic modelling, imaging and inversion due to its advantages of large-scale parallel computing and programming. However, numerical dispersion caused by using a difference operator in substitution for the differential operator is non-negligible, which reduces the accuracy of the modelling and can lead to some misinterpretations. In addition, the computing resources required by the FD scheme is highly demanding when dealing with large models, which limits its applicability. In this paper, a new optimised FD scheme is proposed, which is based on an improved particle swarm optimisation (PSO) algorithm. We improve the conventional PSO algorithm by introducing strategies related to local learning and global learning, which contribute to accelerating the convergence rate and effectively avoiding getting trapped in local extrema. Then, the improved PSO algorithm is used to improve the conventional FD scheme. Dispersion analysis and numerical modelling demonstrate that the low-order optimised FD scheme can achieve higher accuracy than a high-order conventional operator. Compared with the conventional FD scheme and a FD scheme based on the Remez exchange algorithm, the optimised FD scheme based on the improved PSO algorithm can more efficiently suppress numerical dispersion and increase computational efficiency.

According to the validation that the random selection of the gray neural network parameters random selection is similar to initial the space position of the particle in the particle swarm algorithm, the gray neural network based on the modified particle swarm optimization (PSO) algorithm is established to improve the robustness and the precision of the net model with applying a improved PSO algorithm to instead of gradient correction method, updating the network parameter and searching the best individual in this algorithm. There are several methods to forecast the short-term orders, including BP, the gray network, the original PSO algorithm and the improved PSO algorithm. Comparing with these methods, the results demonstrated the grey network based on the improved PSO algorithm has better approximation ability and prediction accuracy.

In the area of cloud computing load balancing, the Particle Swarm Optimization (PSO) algorithm is neoteric and now praised highly, but recently a more neoteric algorithm which deploys the classifier into load balancing is presented. Besides, an algorithm called red-black tree which is aiming at improving the efficiency of resource dispatching is also praised. But the 3 algorithms all have different disadvantages which cannot be ignored. For example, the dispatch efficiency of PSO algorithm is not satisfying; although classifier and red-black tree algorithm improve the efficiency of dispatching tasks, the performance in load balancing is not that good, as a result the improved PSO algorithm is presented. Some researches are designed to get the advantages of new algorithm. First of all, the time complexity and performance for each algorithm in theory are computed; and then actual data which are generated in experiments are given to demonstrate the performance. And from the experiment result, it can be found that for the speed of algorithm itself PSO is the lowest, and the improved PSO solve this problem in some degree; improved PSO algorithm has the best performance in task solving and PSO is the second one, the red-black and Naive Bayes algorithm are much slower; PSO and improved PSO algorithm perform well in load balancing, while the other two algorithms do not do well.

This paper proposes a new practical optimization method applied to the economic dispatch (ED) in a power system. The proposed method is based on an improved particle swarm optimization algorithm and considers some restrict conditions of ED in a practical power system. By reinitializing them with some currently optimal values during every cycle of iteration, this proposed method can make some inactivity particles to be always within a very small area having an optimal solution. The proposed method can avoid effectively the “premature” of the classic particle swarm optimization (PSO) algorithm due to improve the cognized capacity of the classic PSO, thereby it is beneficial to obtain some optimal global solutions. The simulation results show that proposed method has some excellent characteristics of higher quality calculation precision and better computation efficiency, compared with some other PSO methods.

Objectives: Dynamic assignment of tasks to different VM in cloud datacenter and Load-Balancing by migration of tasks from an Overloaded VM to Candidate VM. Methods/Statistical Analysis: Particle Swarm Optimization (PSO) algorithm is used finding each iteration the P_BEST (Personal Best Solution) i.e. current solution is compared with the G_BEST (Global Best Solution) i.e. previous best solution and the G_BEST value is updated at each iteration for calculating the minimum execution time. The parameters being calculated are Global_BEST Solution Function, Personal_BEST Solution Function and Average Utilization for each processor. Findings: A Dynamic Approach for Task-Scheduling using the load-balancing technique is implemented in this paper. Two algorithms namely - Particle Swarm Optimization (PSO) and Improved PSO are used and a comparison is made between them based on number of performance parameters like Scheduling Length (Make Span), Total Execution Time and Total Transfer or Migration Time. A Utilization Graph is plotted to show this comparison which compares these algorithms based on their Cloudlet Length (Scheduling Length) and Total Execution Time. Improved PSO algorithm has lesser or minimum execution time as compared to the PSO algorithm because in the Improved PSO Algorithm two parameters are being considered namely- Cloudlet Length and MIPS (Million Instructions Per Second) which leads to maximum utilization of available resources by all VM. Application/Improvements: This approach is used for dynamically assigning tasks to VM and checking maximum utilization of available resources through load balancing and minimizing the overall execution time and migration time. Keywords: Candidate VM, Cloud Computing, Cloudlet, PSO, Task Migration, Transfer Time, Virtual Machine