Search results for "Particle swarm optimization"
showing 10 items of 44 documents
Comparison of different cooperation strategies in the prey-predator problem
2006
The paper describes two cooperating strategies among several homogeneous agents to reach a given target. In our case we used the prey-predators paradigm in which a set of agents (predators) have the purpose to reach a target (prey). The problem is addressed as an optimization problem that has been faced with two different algorithms (a genetic algorithm and a particle swam optimization algorithm). The two approaches are evaluated by using a simulator for each strategy and the results show that the strategies are very different in terms of prey-predator successes. Genetic algorithm can be used by the prey to solve at the best the problem to reach the lair, otherwise the Particle Swarm Optimi…
Passive congregation based particle swam optimization (pso) with self-organizing hierarchical approach for non-convex economic dispatch
2017
This paper proposes a passive congregation based PSO with self-organizing hierarchical algorithm approach for solving the economic dispatch problem of power system, where some of the units have prohibited operating zones. This Algorithm is known to perform better than conventional gradient based optimization methods for non-convex optimization problems. Conventional PSO algorithm is a population based heuristic search, employing problem of premature convergence. In this work, an innovative approach based on the concept of passive congregation based PSO with self-organizing hierarchical approach is employed to overcome the problem of premature convergence in classical PSO method.
Training Artificial Neural Networks With Improved Particle Swarm Optimization
2020
Particle Swarm Optimization (PSO) is popular for solving complex optimization problems. However, it easily traps in local minima. Authors modify the traditional PSO algorithm by adding an extra step called PSO-Shock. The PSO-Shock algorithm initiates similar to the PSO algorithm. Once it traps in a local minimum, it is detected by counting stall generations. When stall generation accumulates to a prespecified value, particles are perturbed. This helps particles to find better solutions than the current local minimum they found. The behavior of PSO-Shock algorithm is studied using a known: Schwefel's function. With promising performance on the Schwefel's function, PSO-Shock algorithm is util…
Memetic Differential Evolution Frameworks in Filter Design for Defect Detection in Paper Production
2009
This chapter studies and analyzes Memetic Differential Evolution (MDE) Frameworks for designing digital filters, which aim at detecting paper defects produced during an industrial process. MDE Frameworks employ the Differential Evolution (DE) as an evolutionary framework and a list of local searchers adaptively coordinated by a control scheme. Here, three different variants of MDE are taken into account and their features and performance are compared. The binomial explorative features of the DE framework in contraposition to the exploitative features of the local searcher are analyzed in detail in light of the stagnation prevention problem, typical for the DE. Much emphasis in this chapter …
A computational proposal for a robust estimation of the Pareto tail index: An application to emerging markets
2022
Abstract In this work, we backtest and compare, under the VaR risk measure, the fitting performances of three classes of density distributions (Gaussian, Stable and Pareto) with respect to three different types of emerging markets: Egypt, Qatar and Mexico. We also propose a new technique for the estimation of the Pareto tail index by means of the Threshold Accepting (TAVaR) and the Hybrid Particle Swarm Optimization algorithm (H-PSOVaR). Furthermore, we test the accuracy and robustness of our estimates demonstrating the effectiveness of the proposed approach.
Nonlinear model based particle swarm optimization of PID shimmy damping control
2016
The present study aims to investigate the shimmy stability behavior of a single wheeled nose landing gear system. The system is supposed to be equipped with an electromechanical actuator capable to control the shimmy vibrations. A Proportional-Integrative-Derivative (PID) controller, tuned by using the Particle Swarm Optimization (PSO) procedure, is here proposed to actively damp the shimmy vibration. Time-history results for some test cases are reported and commented. Stochastic analysis is last presented to assess the robustness of the control system.
A Comparison of Multi-objective Algorithms for the Automatic Design Space Exploration of a Superscalar System
2013
In today’s computer architectures the design spaces are huge, thus making it very difficult to find optimal configurations. One way to cope with this problem is to use Automatic Design Space Exploration (ADSE) techniques. We developed the Framework for Automatic Design Space Exploration (FADSE) which is focused on microarchitectural optimizations. This framework includes several state-of-the art heuristic algorithms.
An iterative based approach for hysteresis parameters estimation in Magnetorheological dampers
2012
The following work entails the problem of regenerating the hysteresis loop in the Magnetorheological (MR) dampers. The collected data from tests are not sufficient neither efficient for designing optimal controls compensating for the hysteresis in the dampers. This work presents an iterative based approach for estimating the hysteresis parameters, the method however can be generalized for different kind of dampers or actuators hence the hysteresis loop can be generalized using available test data. Some assumptions can be introduced in order to facilitate the underlines of the parameters estimation, one of the assumptions in this work is to use predetermined hysteresis parameters and regener…
A particle swarm approach for tuning washout algorithms in vehicle simulators
2018
Abstract The MCA tuning problem involves finding the most appropriate values for the parameters (or coefficients) of Motion Cueing Algorithms (MCA), also known as washout algorithms. These algorithms are designed to control the movements of the robotic mechanisms, referred to as motion platforms, employed to generate inertial cues in vehicle simulators. This problem can be approached in several different ways. The traditional approach is to perform a manual pilot-in-the-loop subjective tuning, using the opinion of several pilots/drivers to guide the process. A more systematic approach is to use optimization techniques to explore the vast parameter space of the MCA, using objective motion fi…
Particle Swarm Optimization as a New Measure of Machine Translation Efficiency
2018
The present work proposes a new approach to measuring efficiency of evolutionary algorithm-based Machine Translation. We implement some attributes of evolutionary algorithms performing cosine similarity objective function of a Particle Swarm Optimization (PSO) algorithm then, we evaluate an English text set for translation precision into the Spanish text as a simulated benchmark, and explore the backward process. Our results show that PSO algorithm can be used for translation of multiple language sentences with one identifier only, in other words the technology presented is language-pair independent. Specifically, we indicate that our cosine similarity objective function improves the veloci…