An intelligent scheduling method based on improved particle swarm optimization algorithm for drainage pipe network

According to the remarkable characteristics of milling force, an innovative method of milling force modeling using improved particle swarm optimization (PSO) fuzzy system based on support vector machine (SVM) is proposed in this paper. The experiment of titanium alloy milling is designed and implemented. The advanced tester is used to measure the milling force. The training data and test data based on the fuzzy system are obtained. The gradient descent algorithm is embedded in the ordinary particle swarm optimization algorithm to obtain the improved particle swarm optimization algorithm. The convergence effect of the improved particle swarm optimization algorithm is obviously better than that of the ordinary particle swarm optimization algorithm. The improved particle swarm optimization (IPSO) based on fuzzy system is applied to the milling force modeling. Finally, the improved particle swarm optimization (PSO), gradient descent algorithm and improved particle swarm optimization (IPSO) are used to train the fuzzy system, and the conclusion that the final training error of the improved particle swarm optimization (IPSO) is the smallest is obtained.

Making full use of abundant renewable solar energy through the development of photovoltaic (PV) technology is an effective means to solve the problems such as difficulty in electricity supply and energy shortages in remote rural areas. In order to improve the electricity generating efficiency of PV cells, it is necessary to track the maximum power point of PV array, which is difficult to make under partially shaded conditions due to the odds of the appearance of two or more local maximum power points., In this paper, a control algorithm of maximum power point tracking (MPPT) based on improved particle swarm optimization (IPSO) algorithm is presented for PV systems. Firstly, the current in maximum power point is searched with the IPSO algorithm, and then the real maximum power point is tracked through controlling the output current of PV array.,. The MPPT method based on IPSO algorithm is established and simulated with Matlab / Simulink, and meanwhile, the comparison between IPSO MPPT algorithm and traditional MPPT algorithm is also performed in this paper. It is proved through simulation and experimental results that the IPSO algorithm has good performances and very fast response even to partial shaded PV modules, , which ensures the stability of PV system. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.3965

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 .

In this paper the synthesis of linear array geometry with minimum side lobe level using a new class of Particle Swarm Optimization technique namely Improved Particle Swarm Optimization (IPSO) is described. The IPSO algorithm is a newly proposed, high-performance evolutionary algorithm capable of solving general N-dimensional, linear and nonlinear optimization problems. Compared to other evolutionary methods such as genetic algorithms and simulated annealing, the IPSO algorithm is much easier to understand and implement and requires the least of mathematical pre-processing. The array geometry synthesis is first formulated as an optimization problem with the goal of side lobe level (SLL) suppression, and then solved by the IPSO algorithm for the optimum element locations and current excitations. Five design examples are presented that illustrate the use of the IPSO algorithm, and the optimization goal in each example is easily achieved.

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.

An improved particle swarm optimization (IMPSO) which synthesizes the existing models of constriction factor approach (CFA PSO) is proposed. In the proposed method, an adaptive algorithm based on the search space adjustable is applied to solve the problem that conventional particle swarm optimization (PSO) algorithm easily falls into local optimal and occur premature convergence. Then, the IMPSO is used to optimize the parameters of RBF neural network. The new training algorithm is used to approximate polynomial function and predict chaotic time series, compared with PSO, and CFA PSO, the algorithm speed up the speed of convergence, and has much greater accuracy.

Improved particle swarm optimization (PSO) algorithm is proposed to deal with the data association problem for multi-sensor multi-target tracking. The tracking gate is used to confirm the effective measurements, and the association relation between measurements and targets are described by the likelihood function of filter innovation to establish the model of the optimal combination. When solving the optimal combination problem, Lagrange relaxation technology is used to reduce the combination to two dimensions firstly, and then the improved PSO algorithm, which based on the cross and mutation rules, is used to obtain the optimal solution, and get the optimal association pairs for measurements and targets. At last, the simulation shows the superiority of the method in accuracy and speed of the data association.

This paper proposes a new methodology to optimize trajectory of the path for multi-robots using Improved particle swarm optimization Algorithm (IPSO) in clutter Environment. IPSO technique is incorporated into the multi-robot system in a dynamic framework, which will provide robust performance, self-deterministic cooperation, and coping with an inhospitable environment. The robots on the team make independent decisions, coordinate, and cooperate with each other to accomplish a common goal using the developed IPSO. A path planning scheme has been developed using IPSO to optimally obtain the succeeding positions of the robots from the existing position in the proposed environment. Finally, the analytical and experimental result of the multi-robot path planning were compared with those obtained by IPSO, PSO and DE (Differential Evolution) in a similar environment. The simulation and the Khepera environment result show outperforms of IPSO as compared to PSO and DE with respect to the average total trajectory path deviation and average uncovered trajectory target distance.

Aiming at the problem of too many measuring points and difficult optimization in the process of I-beam structure stress state analysis, an improved particle swarm optimization algorithm based on simulated annealing and genetic algorithm is proposed, which considers the identification error of stress state and the optimization of measuring points comprehensively to screen the measuring points. Firstly, the initialization, selection, crossover and mutation of genetic algorithm are integrated into particle swarm optimization; secondly, the idea of simulated annealing is introduced into the mutation part. The improved particle swarm optimization algorithm improves the premature and poor local optimization of the standard particle swarm optimization algorithm. Compared with the standard particle swarm optimization, the improved particle swarm optimization has better stability, stronger anti-premature ability and 60% higher accuracy. The simulation results of measuring point selection and stress state identification of I-beam show that the improved particle swarm optimization algorithm has high efficiency in the process of point selection of stress state identification. The error of force state identification to select points is less than 3%. In engineering application, it provides a better method for stress state identification.

Standard particle swarm optimization (PSO) algorithm is a kind of stochastic optimization algorithm. Its convergence, based on probability theory, is analyzed in detail. We prove that the standard PSO algorithm is convergence with probability 1 under certain condition. Then, a new improved particle swarm optimization (IPSO) algorithm is proposed to ensure that IPSO algorithm is convergence with probability 1. In order to balance the exploration and exploitation abilities of IPSO algorithm, we propose the exploration and exploitation operators and weight the two operators in IPSO algorithm. Finally, IPSO algorithm is tested on 13 benchmark test functions and compared with the other algorithms published in the recent literature. The numerical results confirm that IPSO algorithm has the better performance in solving nonlinear functions.

In order to overcome the problem of increasing fluctuation of Intelligent Tourism data and fuzzy optimal solution, the improved particle swarm optimisation algorithm is introduced to design intelligent tourism path recommendation method. The gray Markov model is used to predict the number of tourist attractions, and the scoring mechanism of tourist attractions is constructed based on multiple perspectives. The constraints are the distance estimation, number prediction, scoring and user preference identification of tourist attractions. The improved particle swarm optimisation algorithm is used to find the optimal solution of recommendation and recommend the tourist path for users. The experimental results show that the average absolute error value of the proposed intelligent travel path recommendation method is 8.13, the inverse relationship between the accuracy and recall rate is clear, and it has better recommendation effect.

Coverage-Enhancing algorithm for video sensor network based on improved particle swarm optimization

Based on the analysis of the basic particle swarm optimization (PSO) algorithm, an improved particle swarm optimization (IPSO) algorithm was proposed to solve the problem with missile-target assignment in coordinated air combat (MTACAC). There were three improvements: 1. Adaptive adjustment of inertia weight; 2. Amelioration of particle velocity and position; 3. Better optimization strategy. Based on the principles of coordinated air combat efficiency and operational research, a missile-target assignment mathematical model was established. The IPSO algorithm was applied to seek the optimal missile assignment scheme for multi-target coordinated air-to-air combat. The simulation result indicated that the model of MTACAC was practical and feasible, and the IPSO algorithm was fast, simple, and more effective in finding out the global optimum assignment, when compared with the basic PSO algorithm and the genetic algorithm (GA).

In order to select the site of the construction industrialization base scientifically and rationally, the METROPOLIS sampling and membrane computing methods are adopted to improve the particle swarm optimization algorithm, and the realization of the improved algorithm in the optimization of the site selection of the construction industrialization base is introduced. In this study, the quantity of the construction base is determined in the actual terrain, and the optimization results before and after the improvement of particle swarm optimization algorithm are compared and analyzed. The result shows that the terrain here is suitable for the establishment of 7 construction industrialization bases. In the optimization of site selection, the improved particle swarm optimization algorithm can help to obtain better site selection optimization results, whose fitness is improved from 1.75 to nearly 1.87. It proves that the improved particle swarm optimization algorithm via METROPOLIS sampling and membrane computing is more efficient than the traditional one. This study is of very important reference value for the application of improved particle swarm optimization in construction industrialization.

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.