Adaptive particle swarm optimization

An adaptive particle swarm optimization (APSO) that features better search efficiency than classical particle swarm optimization (PSO) is presented. More importantly, it can perform a global search over the entire search space with faster convergence speed. The APSO consists of two main steps. First, by evaluating the population distribution and particle fitness, a real-time evolutionary state estimation procedure is performed to identify one of the following four defined evolutionary states, including exploration, exploitation, convergence, and jumping out in each generation. It enables the automatic control of inertia weight, acceleration coefficients, and other algorithmic parameters at run time to improve the search efficiency and convergence speed. Then, an elitist learning strategy is performed when the evolutionary state is classified as convergence state. The strategy will act on the globally best particle to jump out of the likely local optima. The APSO has comprehensively been evaluated on 12 unimodal and multimodal benchmark functions. The effects of parameter adaptation and elitist learning will be studied. Results show that APSO substantially enhances the performance of the PSO paradigm in terms of convergence speed, global optimality, solution accuracy, and algorithm reliability. As APSO introduces two new parameters to the PSO paradigm only, it does not introduce an additional design or implementation complexity

Adaptive Particle Swarm Optimization Applied to Aircraft Control

For the longitudinal dynamics of a fixed wing aircraft with rigid frame, a Proportional-Integral (PI) controller for controlling the forward velocity of the aircraft and a gain-scheduled Proportional-Integral-Differential (PID) like controller, with the forward velocity used as the scheduling variable, for controlling the flight path angle of the aircraft are designed. For a set of working PI gains, previously found through an experienced-based design, derivation and tuning of PID gains for a select number of forward velocities is computationally achieved through the use of a stable Adaptive Particle Swarm Optimization algorithm. Several performance measures, normalized so as to suppress differences in scale, are aggregated into the designed cost function.https://ecommons.udayton.edu/stander_posters/1641/thumbnail.jp

Adaptive hybrid optimization strategy for calibration and parameter estimation of physical models

A new adaptive hybrid optimization strategy, entitled squads, is proposed for complex inverse analysis of computationally intensive physical models. The new strategy is designed to be computationally efficient and robust in identification of the global optimum (e.g. maximum or minimum value of an objective function). It integrates a global Adaptive Particle Swarm Optimization (APSO) strategy with a local Levenberg-Marquardt (LM) optimization strategy using adaptive rules based on runtime performance. The global strategy optimizes the location of a set of solutions (particles) in the parameter space. The LM strategy is applied only to a subset of the particles at different stages of the optimization based on the adaptive rules. After the LM adjustment of the subset of particle positions, the updated particles are returned to the APSO strategy. The advantages of coupling APSO and LM in the manner implemented in squads is demonstrated by comparisons of squads performance against Levenberg-Marquardt (LM), Particle Swarm Optimization (PSO), Adaptive Particle Swarm Optimization (APSO; the TRIBES strategy), and an existing hybrid optimization strategy (hPSO). All the strategies are tested on 2D, 5D and 10D Rosenbrock and Griewank polynomial test functions and a synthetic hydrogeologic application to identify the source of a contaminant plume in an aquifer. Tests are performed using a series of runs with random initial guesses for the estimated (function/model) parameters. Squads is observed to have the best performance when both robustness and efficiency are taken into consideration than the other strategies for all test functions and the hydrogeologic application

Fake Account Identification Using Machine Learning Approaches Integrated with Adaptive Particle Swarm Optimization

 It is customary for humans, bots, and other automated systems to generate new user accounts by utilizing pilfered or otherwise deceitful personal information. They are employed in deceitful activities such as phishing and identity theft, as well as in spreading damaging rumors. An somebody with malevolent intent may generate a substantial number of counterfeit accounts, ranging from hundreds to thousands, with the aim of disseminating their harmful actions to as many authentic users as possible. Users can get a wealth of knowledge from social networking networks. Malicious individuals are readily encouraged to take use of this vast collection of social media information. These cybercriminals fabricate fictitious identities and disseminate meaningless stuff. An essential aspect of using social media networks is the process of discerning counterfeit profiles. This study presents a machine learning approach to detect fraudulent Instagram profiles. This strategy employed the attribute-selection technique, adaptive particle swarm optimization, and feature-elimination recursion. The results indicate that the suggested adaptive particle swarm optimization method surpasses RFE in terms of accuracy, recall, and F measure

Nonlinear system identification and control using state transition algorithm

By transforming identification and control for nonlinear system into optimization problems, a novel optimization method named state transition algorithm (STA) is introduced to solve the problems. In the proposed STA, a solution to a optimization problem is considered as a state, and the updating of a solution equates to a state transition, which makes it easy to understand and convenient to implement. First, the STA is applied to identify the optimal parameters of the estimated system with previously known structure. With the accurate estimated model, an off-line PID controller is then designed optimally by using the STA as well. Experimental results have demonstrated the validity of the methodology, and comparisons to STA with other optimization algorithms have testified that STA is a promising alternative method for system identification and control due to its stronger search ability, faster convergence rate and more stable performance.Comment: 20 pages, 18 figure

VOLTAGE-CONTROL BASED ON FUZZY ADAPTIVE PARTICLE SWARM OPTIMIZATION TECHNIQUE

Keeping an acceptable voltage profile at the system buses is a local and a system-wide challenging task. The power flow in the system transmission lines dictates the voltage profile at the system buses. Voltage-control is nonlinear problem and rooted dominantly in rescheduling of the reactive power flow at a certain loading condition. Despite the fact that a number of voltagecontrol techniques are available to electric power system operators, these systems around the world have been subjected to voltage instability problems and in some cases to voltage collapses that cause complete system breakdowns. Several stochastic techniques or a combination of these techniques have been recently introduced to handle the voltage control problem and these techniques have proven to be superior over traditional techniques. This paper introduces a new stochastic voltage–control methodology based on a combination of fuzzy-logic and adaptive particle swarm optimization. The fuzzy logic is used to adapt parameters of theadaptive particle swarm optimization

Load Feasible Region Determination by Using Adaptive Particle Swarm Optimization

The proposed of a method for determination a space of feasible boundary points, by using adaptive particle swarm optimization in order to solve the boundary region which represented by particle swarm points. This paper present method supports the calculation for a large-scale power system. In case of contingency will illustrate the point on the plane x-axis and y-axis dimensional power flow space. In addition, This method not only demonstrates the optimal particle swarm through the boundary tracing method of the feasible region but also present the boundary points are obtained by optimization. Moreover, receding loss function and operational constraints simultaneously are considering. The formulation points of feasible region can also determine the boundary points which is the contingencies are taken into account and the stability of load demand that system allows to execute in the normal requirements. These feasible points defined the limit of control actions and the robustness of operating points. Finally, the test systems shown the impact of system parameters on the load shedding, generator voltage control, and load level