Sparse Array Optimization Based on Modified Particle Swarm Optimization and Orthogonal Matching Pursuit

This paper introduces the application of particle Swarm optimization techniques in speech enhancement structures. Because of the simple conception, the quick velocity of convergence and easy implementation, Particle swarm optimization (PSO) is used as an effective method in a wide variety of engineering problems. An improved PSO algorithm, called the Modified PSO (MPSO) algorithm, can find optimal or closer-to optimal solution, and has faster search speed. In this paper, we use PSO and MPSO algorithms for speech enhancement and compare the results. Experimental results show that MPSO algorithm outperforms the PSO algorithm in a sense of SNR improvement.

Background/Objectives: PV array being shaded partially by buildings, trees or passing clouds is common. This makes the P-V curve of the PV system complex with more than one peak. MPPT algorithm capable of consistently detecting the global peak within a short duration of time is essential. Methods/Statistical Analysis: Lately Particle Swarm Optimization (PSO) algorithm has been used for Maximum Power Point (MPP) tracking due to its ability to locate the MPP irrespective of its location in the P-V curve. This paper evaluates and compares the performance of the basic PSO algorithm and the modified PSO algorithms for ten different shading patterns. Findings: The basic PSO algorithm is compared with three modified PSO algorithms - PSO algorithm with random numbers eliminated, PSO algorithm with linearly varying constants and PSO algorithm with fixed maximum iterations. The basic PSO algorithm gives good results but random numbers in the algorithm tends to make the convergence time random for the same shading pattern and makes hardware implementation difficult. The PSO algorithm with random numbers eliminated overcomes this disadvantage and is found to give good results. But the convergence time is a little higher and varies with shading pattern. The PSO algorithm with fixed maximum iterations gives good performance with shorter and fixed convergence time. Application/Improvements: PSO algorithm with fixed maximum iterations thus improves the responsiveness of the algorithm to rapidly changing patterns of shading.

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.

The particle swarm optimization (PSO) algorithm presents a new way for finding an optimal solution of complex optimization problems. In this paper a modified particle swarm optimization algorithm is applied to the optimization of the antenna array. We modify the PSO algorithm in some aspects. Firstly, the dynamic parameter is introduced to update the position equation, and the particles are limited in the search region. A new strategy for updating the speed is then adopted, in which the speed is weakened linearly. Based on these strategies, we proposed a new PSO algorithm named the crossed PSO algorithm. Simulation results show that the optimal pattern of the antenna array is able to approach the desired pattern. The results also demonstrate that the modified algorithm is superior to the original algorithm and the nonlinear least-square method.

An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve

An improved PSO (particle swarm optimization) algorithm with stochastic inertia weight and natural selection is proposed. This algorithm effectively avoids the particle swarm easily falling into the local optimal and improves the convergence speed by the strategies of uniform initialization, stochastic inertia weight and natural selection. In order to verify the performance of the proposed algorithm, we apply it to the fast feature extraction of AFMR (ambiguity function main ridge) slice of radar emitter signals. The simulation experiments show that the modified PSO algorithm not only can obtain more accurate AFMR slice, but also can improve the search speed significantly at the same time. Our results confirm the feasibility and effectiveness of the suggested algorithm.

The particle swarm optimization (PSO) algorithm presents a new way for finding optimal solutions of complex optimization problems. In this paper a modified particle swarm optimization algorithm is presented. We modify the PSO algorithm in some aspects. Firstly, a contractive factor is introduced to the position update equation, and the particles are limited in search region. A new strategy for updating velocity is then adopted, in which the velocity is weakened linearly. Thirdly, using an idea of intersecting two modified PSO algorithms. Finally, adding an item of integral control in the modified algorithm can improve its global search ability. Based on these strategies, we proposed a new PSO algorithm named crossed PSO algorithm. Simulation results show that the crossed PSO is superior to the original PSO algorithm and can get overall promising performance over a wide range of problems.

Particle swarm optimization (PSO) is a relatively new global optimization algorithm. Benefitting from its simple concept, fast convergence speed and strong ability of optimization, it has gained much attention in recent years. However, PSO suffers from premature convergence problem because of the quick loss of diversity in solution search. In order to improve the optimization capability of PSO, design of experiment method, which spreads the initial particles across a design domain, and data mining technique, which is used to identify the promising optimization regions, are studied in this research to initialize the particle swarm. From the test results, the modified PSO algorithm initialized by OLHD (Optimal Latin Hypercube Design) technique successfully enhances the efficiency of the basic version but has no obvious advantage compared with other modified PSO algorithms. An extension algorithm, namely OLCPSO (Optimal Latin hypercube design and Classification and Regression tree techniques for improving basic PSO), is developed by consciously distributing more particles into potential optimal regions. The proposed method is tested and validated by benchmark functions in contrast with the basic PSO algorithm and five PSO variants. It is found from the test studies that the OLCPSO algorithm successfully enhances the efficiency of the basic PSO and possesses competitive optimization ability and algorithm stability in contrast to the existing initialization PSO methods.

To solve the strong randomicity and slow convergence of the Particle Swarm Optimization(PSO) algorithms, two new particle’s position renewal formulas were analyzed on the basis of extrapolation in mathematics. A new modified PSO algorithms (called Leading PSO algorithms) was put forward. The Direct Torque Control(DTC) System was built in the environment of Matlab(Simulink). The weight and threshold values of BP Neural Network were trained using the modified PSO algorithms. Some disadvantages such as slow convergence speed and easily plunging into the local solution were avoided effectively. The simulation result shows that the system works well, and the rotor speed identifier has great static and dynamic performance.

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.

This paper develops a new design scheme for the phase response of an all-pass recursive digital filter. A variant of particle swarm optimization (PSO) algorithm will be utilized for solving this kind of filter design problem. It is here called the modified PSO (MPSO) algorithm in which another adjusting factor is more introduced in the velocity updating formula of the algorithm in order to improve the searching ability. In the proposed method, all of the designed filter coefficients are firstly collected to be a parameter vector and this vector is regarded as a particle of the algorithm. The MPSO with a modified velocity formula will force all particles into moving toward the optimal or near optimal solution by minimizing some defined objective function of the optimization problem. To show the effectiveness of the proposed method, two different kinds of linear phase response design examples are illustrated and the general PSO algorithm is compared as well. The obtained results show that the MPSO is superior to the general PSO for the phase response design of digital recursive all-pass filter.

Recently Particle Swarm Optimization (PSO) algorithm gained popularity and employed in many engineering applications because of its simplicity and efficiency. The performance of the PSO algorithm can further be improved by using hybrid techniques. There are various hybrid PSO algorithms published in the literature where researchers combine the benefits of PSO with other heuristics algorithms. In this paper, we propose a cooperative line search particle swarm optimization (CLS-PSO) algorithm by integrating local line search technique and basic PSO (B-PSO). The performance of the proposed hybrid algorithm, examined through four typically nonlinear optimization problems, is reported. Our experimental results show that CLS-PSO outperforms basic PSO.

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 .

Collaborative beamforming in wireless sensor networks using a novel particle swarm optimization algorithm variant

Multi-robot is a robotic system consisting of several robots that are interconnected and can communicate and collaborate with each other to complete a goal. With physical similarities, they have two controlled wheels and one free wheel that moves at the same speed. In this Problem, there is a main problem remaining in controlling the movement of the multi robot formation in searching the target. It occurs because the robots have to create dynamic geometric shapes towards the target. In its movement, it requires a control system in order to move the position as desired. For multi-robot movement formations, they have their own predetermined trajectories which are relatively constant in varying speeds and accelerations even in sudden stops. Based on these weaknesses, the robots must be able to avoid obstacles and reach the target. This research used Fuzzy Logic type 2 – Particle Swarm Optimization algorithm which was compared with Fuzzy Logic type 2 – Modified Particle Swarm Optimization and Fuzzy Logic type 2 – Dynamic Particle Swarm Optimization. Based on the experiments that had been carried out in each environment, it was found that Fuzzy Logic type 2 - Modified Particle Swarm Optimization had better iteration, time and resource and also smoother robot movement than Fuzzy Logic type 2 – Particle Swarm Optimization and Fuzzy Logic Type 2 - Dynamic Particle Swarm Optimization.