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

Controller design for synchronization of an array of delayed neural networks using a controllable

This is the post-print version of the Article - Copyright @ 2011 ElsevierIn this paper, a controllable probabilistic particle swarm optimization (CPPSO) algorithm is introduced based on Bernoulli stochastic variables and a competitive penalized method. The CPPSO algorithm is proposed to solve optimization problems and is then applied to design the memoryless feedback controller, which is used in the synchronization of an array of delayed neural networks (DNNs). The learning strategies occur in a random way governed by Bernoulli stochastic variables. The expectations of Bernoulli stochastic variables are automatically updated by the search environment. The proposed method not only keeps the diversity of the swarm, but also maintains the rapid convergence of the CPPSO algorithm according to the competitive penalized mechanism. In addition, the convergence rate is improved because the inertia weight of each particle is automatically computed according to the feedback of fitness value. The efficiency of the proposed CPPSO algorithm is demonstrated by comparing it with some well-known PSO algorithms on benchmark test functions with and without rotations. In the end, the proposed CPPSO algorithm is used to design the controller for the synchronization of an array of continuous-time delayed neural networks.This research was partially supported by the National Natural Science Foundation of PR China (Grant No 60874113), the Research Fund for the Doctoral Program of Higher Education (Grant No 200802550007), the Key Creative Project of Shanghai Education Community (Grant No 09ZZ66), the Key Foundation Project of Shanghai(Grant No 09JC1400700), the Engineering and Physical Sciences Research Council EPSRC of the U.K. under Grant No. GR/S27658/01, an International Joint Project sponsored by the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany

Educational Simulator for Teaching of Particle Swarm Optimization in LabVIEW

This paper presents an educational software tool for aid the teaching of Particle Swarm Optimization (PSO) fundamentals with friendly design interface. This software were developed in the platform of LabVIEW (Laboratory Virtual Intrumentation Engineering Workbench). The software‟s best qualities are users can select many different version of the PSO algorithm, a lot of the benchmarks test functions for optimization and set the parameters that have an influence on the PSO performance. Through visualization of particle distribution in the searching, the simulator is particularly effective in providing users with an intuitive feel for the PSO algorithm

Action Generalization in Humanoid Robots Through Artificial Intelligence With Learning From Demonstration

Mención Internacional en el título de doctorAction Generalization is the ability to adapt an action to different contexts and environments. In humans, this ability is taken for granted. Robots are yet far from achieving the human level of Action Generalization. Current robotic frameworks are limited frameworks that are only able to work in the small range of contexts and environments for which they were programmed. One of the reasons why we do not have a robot in our house yet is because every house is different. In this thesis, two different approaches to improve the Action Generalization capabilities of robots are proposed. First, a study of different methods to improve the performance of the Continuous Goal-Directed Actions framework within highly dynamic real world environments is presented. Continuous Goal-Directed Actions is a Learning from Demonstration framework based on the idea of encoding actions as the effects these actions produce on the environment. No robot kinematic information is required for the encoding of actions. This improves the generalization capabilities of robots by solving the correspondence problem. This problem is related to the execution of the same action with different kinematics. The second approach is the proposition of the Neural Policy Style Transfer framework. The goal of this framework is to achieve Action Generalization by providing the robot the ability to introduce Styles within robotic actions. This allows the robot to adapt one action to different contexts with the introduction of different Styles. Neural Style Transfer was originally proposed as a way to perform Style Transfer between images. Neural Policy Style Transfer proposes the introduction of Neural Style Transfer within robotic actions. The structure of this document was designed with the goal of depicting the continuous research work that this thesis has been. Every time a new approach is proposed, the reasons why this was considered the best new step based on the experimental results obtained are provided. Each approach can be studied separately and, at the same time, they are presented as part of the larger research project from which they are part. Solving the problem of Action Generalization is currently a too ambitious goal for any single research project. The goal of this thesis is to make finding this solution one step closer.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Saffiotti Alessandro.- Secretario: Santiago Martínez de la Casa Díaz.- Vocal: Fernando Torres Medin

pythOPT: A problem-solving environment for optimization methods

Optimization is a process of finding the best solutions to problems based on mathematical models. There are numerous methods for solving optimization problems, and there is no method that is superior for all problems. This study focuses on the Particle Swarm Optimization (PSO) family of methods, which is based on the swarm behaviour of biological organisms. These methods are easily adjustable, scalable, and have been proven successful in solving optimization problems. This study examines the performance of nine optimization methods on four sets of problems. The performance analysis of these methods is based on two performance metrics (the win-draw-loss metric and the performance profiles metric) that are used to analyze experimental data. The data are gathered by using each optimization method in multiple configurations to solve four classes of problems. A software package pythOPT was created. It is a problem-solving environment that is comprised of a library, a framework, and a system for benchmarking optimization methods. pythOPT includes code that prepares experiments, executes computations on a distributed system, stores results in a database, analyzes those results, and visualizes analyses. It also includes a framework for building PSO-based methods and a library of benchmark functions used in one of the presented analyses. Using pythOPT, the performance of these nine methods is compared in relation to three parameters: number of available function evaluations, accuracy of solutions, and communication topology. This experiment demonstrates that two methods (SPSO and GCPSO) are superior in finding solutions for the tested classes of problems. Finally, by using pythOPT we can recreate this study and produce similar ones by changing the parameters of an experiment. We can add new methods and evaluate their performances, and this helps in developing new optimization methods