Novel Particle Swarm Optimization Research Articles

Optimal pattern synthesis of circular antenna arrays with improved effective aperture and beam area

Abstract Circular antenna array (CAA) is one of the most widely used antenna array designs. This paper addresses the design challenges of the CAA with the non-uniform single ring, which is placed in an X-Y plane with the best sidelobe level (SLL) and improved first null beamwidth (FNBW). It has been solved using differential evolution, craziness-based particle swarm optimization (CRPSO), and novel particle swarm optimization (NPSO) techniques. An optimal combination of feeding current and inter-element spacing provides an array pattern with the best SLL and improved FNBW, as well as some other parameter calculations of the antenna array like maximum directivity, maximum effective aperture, total effective aperture, maximum beam area, total beam area, circumference, and radius of the CAAs using these techniques. There are six designs of CAAs with different antenna elements (i.e., 10-, 12-, 16-, 20-, 36-, and 64-elements) which have been taken into account. Simulations are done in MATLAB. Based on various simulation results, we can analyze the performance of SLL and FNBW with other parameters using NPSO and compare them with different techniques of CAAs, as shown in the numerical analysis and simulation result section.

Moth Flame Optimization Algorithm for Optimal FIR Filter Design

This paper presents the application of Moth Flame optimization (MFO) algorithm to determine the best impulse response coefficients of FIR low pass, high pass, band pass and band stop filters. MFO was inspired by observing the navigation strategy of moths in nature called transverse orientation composed of three mathematical sub-models. The performance of the proposed technique was compared to those of other well-known high performing optimization techniques like techniques like Particle Swarm Optimization (PSO), Novel Particle Swarm Optimization (NPSO), Improved Novel Particle Swarm Optimization (INPSO), Genetic Algorithm (GA), Parks and McClellan (PM) Algorithm. The performances of the MFO based designed optimized FIR filters have proved to be superior as compared to those obtained by PSO, NPSO, INPSO, GA, and PM Algorithm. Simulation results indicated that the maximum stop band ripples 0.057326, transition width 0.079 and fitness value 1.3682 obtained by MFO is better than that of PSO, NPSO, INPSO, GA, and PM Algorithms. The value of stop band ripples indicated the ripples or fluctuations obtained at the range which signals are attenuated is very low. The reduced value of transition width is the rate at which a signal changes from either stop band to pass band of a filter or vice versa is very good. Also, small fitness value in an indication that the values of the control variable of MFO are very near to its optimum solutions. The proposed design technique in this work generates excellent solution with high computational efficiency. This shows that MFO algorithm is an outstanding technique for FIR filter design.

EHPSO: An Enhanced Hybrid Particle Swarm Optimization Algorithm for Internet of Things

Internet of Things (IOT) has found broad applications and has drawn more and more attention from researchers. At the same time, IOT also presents many challenges, one of which is node localization, i.e. how to determine the geographical position of each sensor node. Algorithms have been proposed to solve the problem. A popular algorithm is Particle Swarm Optimization (PSO) because it is simple to implement and needs relatively less computation. However, PSO is easily trapped into local optima and gives premature results. In order to improve the PSO algorithm, this paper proposes the EHPSO algorithm based on Novel Particle Swarm Optimization (NPSO) and Hybrid Particle Swarm Optimization (HPSO). The EHPSO algorithm applies the principle of best neighbor of each particle to the HPSO algorithm. Simulation results indicate that EHPSO outperforms HPSO and NPSO in evaluating accurate node positions and improves convergence by avoiding being trapped into local optima.

Reentry guidance with constrained impact for hypersonic weapon by novel particle swarm optimization

The novel reentry guidance law is proposed for hypersonic weapons to strike a stationary ground target at a constrained impact angle. This guidance law is based on proportional navigation guidance (PNG) whose gain is set as the function of range-to-go. To be resistant to disturbances and adaptive to various missions, the gain is refreshed in every guidance cycle. To lessen the control effort, the gain is optimized by a novel particle swarm optimization (NPSO) algorithm. NPSO needs a small number of particles for it can convert infeasible particles into feasible ones. Moreover, a mutation mechanism is introduced to accelerate convergence. In addition, there is only one single terminal constraint, that is, the impact angle in the optimization problem, because the terminal position is automatically identified using PNG. Compared with existing guidance laws, the proposed one needs neither complex derivation nor prior assumption. It also takes into consideration the constraints in lateral acceleration and look angle, which are often neglected in PNG-based laws. The adaptability under different scenarios, the robustness under disturbances and the potential for online application are demonstrated by simulation results. Numerical examples also show the superiority of NPSO when compared with the GPOPS.

HYBRID INTELLIGENT LEARNING METHOD FOR TRAINING THE ANFIS MODEL WITH NOVEL PSO OPTIMIZATION TECHNIQUE

Ponte Academic JournalJan 2017, Volume 73, Issue 1 HYBRID INTELLIGENT LEARNING METHOD FOR TRAINING THE ANFIS MODEL WITH NOVEL PSO OPTIMIZATION TECHNIQUEAuthor(s): vidyullatha Pellakuri ,D. Rajeswara RaoJ. Ponte - Jan 2017 - Volume 73 - Issue 1 doi: 10.21506/j.ponte.2017.1.21 Abstract:In information driven modeling strategies the Adaptive neuro-fuzzy inference system is one of the hybrid intelligent systems which comprises fuzzy logic and neural network methods. This paper presents a new hybrid intelligent model on multivariate data to enhance the prediction accuracy by combining the adaptive neuro-fuzzy inference system (ANFIS) and Novel Particle Swarm Optimization (NPSO) to build up the hybrid intelligent model. The ANFIS has the advantages of expert knowledge of fuzzy inference system and learning capability of neural networks. The PSO algorithm is modified with some change for training of all parameters of ANFIS structure. The performance of the proposed prediction method has been assessed through exploratory outcomes. The proposed method is assessed in light of evaluation for prediction accuracy by using Mean Squared Error (MSE) and computing time. Download full text:Check if you have access through your login credentials or your institution Username Password

A novel identification method based on frequency response analysis

Motivated by the fact that many classical identification methods cannot be directly used for a closed-loop control system or have to meet high requirements, a novel method is proposed for application to both open- and closed-loop identification by introducing a damping factor in the frequency response domain identification. Based on the mathematical analysis of the system, we proposed a novel particle swarm optimization (NPSO) algorithm to obtain an optimal estimate of the model by searching for the optimal damping factor. In addition, considering that many methods are confined to some special test signals as system inputs, the method presented here is proved to obtain the optimal estimated model by choosing the optimal damping factor for any continuous signals. With deep analysis and evidence, the method is improved so that the input signal can maintain output convergence and a second-order derivative in the s-domain is available. Simulations of the identification of open- and closed-loop systems are given to demonstrate the effectiveness and merits of the proposed algorithm in the paper.

Path planning method for UUV homing and docking in movement disorders environment.

Path planning method for unmanned underwater vehicles (UUV) homing and docking in movement disorders environment is proposed in this paper. Firstly, cost function is proposed for path planning. Then, a novel particle swarm optimization (NPSO) is proposed and applied to find the waypoint with minimum value of cost function. Then, a strategy for UUV enters into the mother vessel with a fixed angle being proposed. Finally, the test function is introduced to analyze the performance of NPSO and compare with basic particle swarm optimization (BPSO), inertia weight particle swarm optimization (LWPSO, EPSO), and time-varying acceleration coefficient (TVAC). It has turned out that, for unimodal functions, NPSO performed better searching accuracy and stability than other algorithms, and, for multimodal functions, the performance of NPSO is similar to TVAC. Then, the simulation of UUV path planning is presented, and it showed that, with the strategy proposed in this paper, UUV can dodge obstacles and threats, and search for the efficiency path.

Wide null control of symmetric linear antenna array using novel particle swarm optimization

In this article, the method of nullifying the radiation pattern of a symmetric linear antenna array in a particular direction is propounded using an evolutionary swarm intelligence technique, Novel particle swarm optimization (NPSO). Particle swarm optimization with constriction factor approach (PSOCFA) is also adopted to compare the NPSO based results. Single or multiple wide nulls are achieved by optimum perturbations of elements current amplitude weights to have symmetric nulls about the main beam. Different numerical examples are presented to illustrate the capability of NPSO for pattern synthesis with a prescribed wide nulls locations and depths. Further, the peak Sidelobe Levels are also reduces when compared to a uniformly excited array having equal number of elements. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011.

Particle swarm optimization with dynamic change of inertia weights

A novel particle swarm optimization (NPSO) with modified fitness function and dynamic change of inertia weights was proposed for solving complex high-dimensional optimization problems. In this algorithm, both the function value at the searching point and the function change rate at the point were combined into fitness function. This new approach could balance the local searching and the global searching by adopting inertia weight matrix to adaptively and dynamically adjust inertia weights. The convergence degree of every dimension was calculated and the dimension of minimal convergent degree was mutated according to some probability. Experiments on five high-dimension test functions indicate that NPSO can enhance the performance of global searching.

HEURISTIC PARTICLE SWARM OPTIMIZATION LEARNING-BASED IMAGE COMPRESSION SYSTEM

Traditional LBG algorithm is a pure iterative optimization procedure to achieve the vector quantization (VQ) codebook, where an initial codebook is continually refined at every iteration to reduce the distortion between code-vectors and a given training data set. However, such interactive type learning algorithms will easily direct final results converging toward the local optimization while the high quality of the initial codebook is not available. In this article, an efficient heuristic-based learning method, called novel particle swarm optimization (NPSO), is proposed to design the proper codebook of VQ scheme that can develop the image compression system. To improve the performance of the basic PSO, the centroid updating machine applies the one step-size gradient descent learning step in the heuristic learning procedure. Additionally, the presented NPSO with advantages of the centroid updating machine is proposed to quickly achieve the near-optimal reconstructive image. For demonstrating the proposed NPSO learning scheme, the image with several horizontal grey bars is first applied to present the efficiency of the NPSO learning mechanism. LBG and NPSO learning methods are also applied to test the reconstructing performance in several type images “Lena,” “Airplane,” “Cameraman”, and “peppers.” In our experiments, the NPSO learning algorithm provides the higher performance than conventional LBG methods in the application of building image compression system.

A novel particle swarm optimization algorithm for permutation flow-shop scheduling to minimize makespan

It is well known that the flow-shop scheduling problem (FSSP) is a branch of production scheduling and is NP-hard. Now, many different approaches have been applied for permutation flow-shop scheduling to minimize makespan, but current algorithms even for moderate size problems cannot be solved to guarantee optimality. Some literatures searching PSO for continuous optimization problems are reported, but papers searching PSO for discrete scheduling problems are few. In this paper, according to the discrete characteristic of FSSP, a novel particle swarm optimization (NPSO) algorithm is presented and successfully applied to permutation flow-shop scheduling to minimize makespan. Computation experiments of seven representative instances (Taillard) based on practical data were made, and comparing the NPSO with standard GA, we obtain that the NPSO is clearly more efficacious than standard GA for FSSP to minimize makespan.