Classified Particle Swarm Optimization Based Algorithm for Cooperative Localization

The classical Particle swarm optimization (PSO) algorithm is a powerful method to find the minimum of a numerical function, on a continuous definition domain. With more importance being attached to PSO, the application of PSO algorithm to a great deal of different scientific fields have been introduced in many literature. Here, a PSO algorithm is applied to multilevel lot-sizing (MLLS) problems with series structure aiming at extending the research scope of PSO algorithm. Experiments showed the feasibility and effectiveness of PSO algorithm for solving this kind of MLLS problems.

Hybridization of Constriction Coefficient Based Particle Swarm Optimization and Gravitational Search Algorithm for Function Optimization

In this study, a nonlinear Archimedes wave swing (AWS) energy conversion system was employed to enable the use of irregular sea waves to provide useful electricity. Instead of the conventional PI controllers used in prior research, this study employed fractional-order PID (FOPID) controllers to control the back-to-back configuration of AWS. The aim was to maximize the energy yield from waves and maintain the grid voltage and the capacitor DC link voltage at predetermined values. In this study, six FOPID controllers were used to accomplish the control goals, leading to an array of thirty parameters required to be fine-tuned. In this regard, a hybrid jellyfish search optimizer and particle swarm optimization (HJSPSO) algorithm was adopted to select the optimal control gains. Verification of the performance of the proposed FOPID control system was achieved by comparing the system results to two conventional PID controllers and one FOPID controller. The conventional PID controllers were tuned using a recently presented metaheuristic algorithm called the Coot optimization algorithm (COOT) and the classical particle swarm optimization algorithm (PSO). Moreover, the FOPID was also tuned using the well-known genetic algorithm (GA). The system investigated in this study was subjected to various unsymmetrical and symmetrical fault disturbances. When compared with the standard COOT-PID, PSO-PID, and GA-FOPID controllers, the HJSPSO-FOPID results show a significant improvement in terms of performance and preserving control goals during system instability

High-performance beamforming incorporating multiple objectives for large-scale antenna arrays becomes increasingly important to improve the capacity and efficiency of wireless communication systems. The speed of synthesizing a desired beam pattern is critical in wireless communications systems to adapt to highly dynamic wireless channels. A modified particle swarm optimization (PSO) algorithm for synthesizing array beam patterns is proposed in this study. The initial positions of particles in PSO are designated following a Taylor distribution instead of being given uniformly distributed random values as in the classical PSO algorithm. The fitness functions are defined to include multiple objectives represented by producing multiple main lobes with customized deep and broadened nulls. Several scenarios have been established to examine the feasibility of the proposed algorithm. Moreover, the performance of the proposed algorithm is compared with those of the ones based on the classical PSO. A significant performance improvement for obtaining beamforming coefficients has been achieved. The robustness of the proposed algorithm is demonstrated further by applying it to a finite array on a curved surface for beamforming.

This paper presents an efficient approach for solving economic dispatch(ED) problem with non-convex cost functions using particle swarm optimization with time varying acceleration coefficients. PSO_TVAC is applied to 3, 13, & 14 unit test system with and without valve point loading effects. The results of PSO_TVAC are compared with PSO From simulation results, it is clear that PSO_TVAC performance is better than many well settled nature inspired algorithms.Economic dispatch is one of the key functions of the modern energy management system. Classical particle swarm optimization (PSO) algorithm is capable of achieving near global solutions for such problems but it tends to converge prematurely. The practical Non Convex Economic Dispatch problem is solved here using PSO with a novel parameter automation strategy in which time varying acceleration coefficients (TVAC) are employed to efficiently control the local and global search, such that premature convergence is avoided and global solutions are achieved.

A new gradient based particle swarm optimization algorithm for accurate computation of global minimum

In this paper a novel differential particle swarm optimization (DPSO) algorithm is proposed. The proposed algorithm includes an additional term in the velocity equation of the classical particle swarm optimization (PSO) algorithm. The additional term in DPSO is the experience of a particle selected randomly from the swarm for a target particle. The difference of the selected and the target particle is scaled and added to the velocity equation which try the particle to escape from trapping into local minima. The proposed algorithm has been used to solve economic load dispatch (ELD) problem of the IEEE 10 unit system. The results obtained by DPSO have been validated by comparing the results obtained by PSO.

Minimizing thermal residual stresses in C/SiC functionally graded material coating of C/C composites by using particle swarm optimization algorithm

Due to the parameters of traditional support vector machine are difficult to determine in huge information data, so it is seriously limiting its application field. On the basis of classical particle swarm optimization algorithm, this paper proposed an adaptive particle swarm optimization algorithm which can filter the redundant information and adaptive to choose the best parameters in the process of training support vector machine, and the support vector machine using the optimal parameter to establish identification model. In this work, it is found that different insect-resistant genes (Bt-Cry2, Bt-Cry4, Bt-Cry10) are recognized by using this optimization model. Our study provides a precise, fast, convenient and nondestructive detection method for genetically modified organism.

Particle swarm optimizer (PSO) is an efficient algorithm to find the best solution in engineering. However, the classical particle swarm optimization algorithm and the existing variational algorithm still have the problems of prematurity and slow convergence speed in the late iteration in different application problems. To make up the above demerits, we proposed a hybrid algorithm PSO to combine an improved PSO (IPSO) with an improved Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. The hybrid algorithm introduced the convergence direction of the RL-BFGS to correct the evolution direction and enhance global search capability. And the initial point of the RL-BFGS is decided by the IPSO. For the sake of the effectiveness evaluation of IB-PSO, The performance of eight improved PSO algorithms was compared by optimizing six benchmark functions with different characteristics, such as single mode, multi-mode and rotation. The results show that compared with the other eight algorithms, the proposed IB-PSO has good performance in solving multimodal problems. Furthermore, the proposed approach can be used in engineering problems to obtain high-quality solutions.

Hyperspectral images contain one-dimensional spectral data and two-dimensional spatial data. A novel automatic clustering algorithm using qutrit exponential decomposition based particle swarm optimization is proposed for automatically determining the number of optimized cluster segments in hyper-spectral images. The proposed algorithm is compared with the classical particle swarm optimization and exponential decay particle swarm optimization algorithms for establishing its efficiency. The quality of segmentation is determined using F score.

We propose an inter-disciplinary approach to particle swarm optimization (PSO) by establishing a molecular dynamics (MD) formulation of the algorithm, leading to a physical theory for the swarm environment. The physical theory provides new insights on the operational mechanism of the PSO method. In particular, a thermodynamic analysis, which is based on the MD formulation, is introduced to provide deeper understanding of the convergence behavior of the basic classical PSO algorithm. The thermodynamic theory is used to propose a new acceleration technique for the PSO. This technique is applied to the problem of synthesis of linear array antennas and very good improvement in the convergence performance is observed. A macroscopic study of the PSO is conducted by formulating a diffusion model for the swarm environment. The Einstein’s diffusion equation is solved for the corresponding probability density function (pdf) of the particles trajectory. The diffusion model for the classical PSO is used, in conjunction with Schrodinger’s equation for the quantum PSO, to propose a generalized version of the PSO algorithm based on the theory of Markov chains. This unifies the two versions of the PSO, classical and quantum, by eliminating the velocity and introducing position-only update equations based on the probability law of the method.

Particle swarm optimization (PSO) is a stochastic population-based algorithm which was originally introduced by Kennedy and Eberhart [1]. This optimization algorithm is motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish. As in most of the metaheuristic optimization algorithms, in PSO, a population of individuals, known as particles, are evolved through successive iterations. The most important advantages of PSO, compared to other optimization strategies, are its easy implementation and few parameters requiring adjustment. Since the initial development of PSO by Kennedy and Eberhart, several variants of this algorithm have been proposed by researchers. The first modification was introducing an inertia weight parameter in the velocity update equation of the initial PSO-a PSO model which is now accepted as the global best PSO algorithm [2]. The goal of the inertia weight parameter is to balance the exploration and exploitation characteristics of PSO. Generally, large inertia weight values are expected to increase the velocity of the particles and improve the long-range exploration of the PSO algorithm, while low inertia values increase the short-range exploration. Due to the strong effect of the inertia weight on the performance of PSO, many researchers have investigated different inertia weight control approaches during the past decades, and many different strategies have been proposed. Various inertia weighting strategies can be categorized into three main classes (Figure 7.1): (1) constant or random, (2) time-varying, and (3) adaptive inertia weights. The first class contains strategies in which the value of the inertia weight is constant during the search or is determined randomly. In the second class, the inertia weight is defined as a function of time (iteration number), and hence these strategies are referred to as time-varying inertia weight strategies. These methods are not considered adaptive since they do not monitor the situation of the particles in the search space, and the value of the inertia weight in each iteration is known before the execution of the algorithm. The third class of the inertia weight strategies consists of those methods which use a feedback parameter to monitor the state of the algorithm and adjust the value of the inertia weight based on the feedback parameter's value. This chapter presents the major existing inertia weight control strategies in the above three categories and discusses examples of each category in detail. The remainder of this chapter is organized as follows. The next section gives a short review of the PSO algorithm, and the role ofthe inertia weight parameter in the velocity update equation. Sections 7.2-7.4 review the three groups of inertia weight strategies, constant or random, time-varying, and adaptive models. Section 7.5 reports some experimental evaluations of different inertia weight models. Finally, Section 7.6 concludes this chapter. A short description of the symbols frequently used in this chapter is shown in Table 7.1.

In this paper, we improved the particle swarm optimization (PSO) algorithm with inertial weight by adopting the sigmoid function. The IPSO is applied to structural damage identification in frame structures. Firstly the theoretical background of the improved PSO (IPSO) algorithm is presented. Secondly, we use a suite of four famous benchmark functions to evaluate the algorithm performance. Some comparative studies on single and multi-damage of a 2-story rigid frame structure have been carried out. The results of the numerical simulation demonstrate that the approach can detect the structural damage with higher accuracy.

Neural Network system for SoC is one topic in the world. Recently, SoC began to combine AI technique by Python. When developers consider Intelligent system on SoC, the co-design software and hardware are significant. In this case, small devices and embedded system are target system. For IoT, those techniques will grow up in the world. Those cases, avoiding premature convergence while keeping performance is a challenge in training the neural network, especially in a case of large NNs or a large number of training data. In this case, one problem is a huge memory and processing power. For tiny memory and processing system as SoC, we propose an improved particle swarm optimization (PSO) algorithm called the PSOseed2 algorithm for training NN. The PSOseed2 algorithm solves the premature convergence of the standard PSO (SPSO) algorithm by slightly modifying the velocity update function without adding many computational tasks to the SPSO algorithm. We evaluated this algorithm on field programmable gate array (FPGA)-based NN and software-based NN and trained these NNs with different PSO algorithms that are SPSO, PSOseed, PSOseed2, and dissipative PSO. Experimental results with different datasets confirmed that the NNs trained by the proposed PSOseed2 algorithm had better recognition rates and lower global learning errors than the NN trained by other PSO algorithms. In this talk, I would like to talk those relational research in the world and our results and discussion.