Performance of Multi-chaotic PSO on a shifted benchmark functions set

In this paper the performance of Multi-chaotic PSO algorithm is investigated using two shifted benchmark functions. The purpose of shifted benchmark functions is to simulate the time-variant real-world problems. The results of chaotic PSO are compared with canonical version of the algorithm. It is concluded that using the multi-chaotic approach can lead to better results in optimization of shifted functions

PSO algorithm enhanced with Lozi Chaotic Map - Tuning experiment

In this paper it is investigated the effect of tuning of control parameters of the Lozi Chaotic Map employed as a chaotic pseudo-random number generator for the particle swarm optimization algorithm. Three different benchmark functions are selected from the IEEE CEC 2013 competition benchmark set. The Lozi map is extensively tuned and the performance of PSO is evaluated

A novel intelligent adaptive control of laser-based ground thermal test

AbstractLaser heating technology is a type of potential and attractive space heat flux simulation technology, which is characterized by high heating rate, controlled spatial intensity distribution and rapid response. However, the controlled plant is nonlinear, time-varying and uncertainty when implementing the laser-based heat flux simulation. In this paper, a novel intelligent adaptive controller based on proportion–integration–differentiation (PID) type fuzzy logic is proposed to improve the performance of laser-based ground thermal test. The temperature range of thermal cycles is more than 200K in many instances. In order to improve the adaptability of controller, output scaling factors are real time adjusted while the thermal test is underway. The initial values of scaling factors are optimized using a stochastic hybrid particle swarm optimization (H-PSO) algorithm. A validating system has been established in the laboratory. The performance of the proposed controller is evaluated through extensive experiments under different operating conditions (reference and load disturbance). The results show that the proposed adaptive controller performs remarkably better compared to the conventional PID (PID) controller and the conventional PID type fuzzy (F-PID) controller considering performance indicators of overshoot, settling time and steady state error for laser-based ground thermal test. It is a reliable tool for effective temperature control of laser-based ground thermal test

Flexible Fuzzy Rule Bases Evolution with Swarm Intelligence for Meta-Scheduling in Grid Computing

Fuzzy rule-based systems are expert systems whose performance is strongly related to the quality of their knowledge and the associated knowledge acquisition processes and thus, the design of effective learning techniques is considered a critical and major problem of these systems. Knowledge acquisition with a swarm intelligence approach is a recent learning strategy for the evolution of fuzzy rule bases founded on swarm intelligence showing improvement over classical knowledge acquisition strategies in fuzzy rule based systems such as Pittsburgh and Michigan approaches in terms of convergence behaviour and accuracy. In this work, a generalization of this method is proposed to allow the simultaneous consideration of diversely configured knowledge bases and this way to accelerate the learning process of the original algorithm. In order to test the suggested strategy, a problem of practical importance nowadays, the design of expert meta-schedulers systems for grid computing is considered. Simulations results show the fact that the suggested adaptation improves the functionality of knowledge acquisition with a swarm intelligence approach and it reduces computational effort; at the same time it keeps the quality of the canonical strategy

A New Fuzzy System Based on Rectangular Pyramid

A new fuzzy system is proposed in this paper. The novelty of the proposed system is mainly in the compound of the antecedents, which is based on the proposed rectangular pyramid membership function instead of t-norm. It is proved that the system is capable of approximating any continuous function of two variables to arbitrary degree on a compact domain. Moreover, this paper provides one sufficient condition of approximating function so that the new fuzzy system can approximate any continuous function of two variables with bounded partial derivatives. Finally, simulation examples are given to show how the proposed fuzzy system can be effectively used for function approximation

Training ANFIS Model with an Improved Quantum-Behaved Particle Swarm Optimization Algorithm

This paper proposes a novel method of training the parameters of adaptive-network-based fuzzy inference system (ANFIS). Different from the previous works which emphasized on gradient descent (GD) method, we present an approach to train the parameters of ANFIS by using an improved version of quantum-behaved particle swarm optimization (QPSO). This novel variant of QPSO employs an adaptive dynamical controlling method for the contraction-expansion (CE) coefficient which is the most influential algorithmic parameter for the performance of the QPSO algorithm. The ANFIS trained by the proposed QPSO with adaptive dynamical CE coefficient (QPSO-ADCEC) is applied to five example systems. The simulation results show that the ANFIS-QPSO-ADCEC method performs much better than the original ANFIS, ANFIS-PSO, and ANFIS-QPSO methods

Particle swarm approaches using Lozi map chaotic sequences to fuzzy modelling of an experimental thermal-vacuum system

Particle Swarm Optimization (PSO) approach intertwined with Lozi map chaotic sequences to obtain Takagi-Sugeno (TS) fuzzy model for representing dynamical behaviours are proposed in this paper. the proposed method is an alternative for nonlinear identification approaches especially when dealing with complex systems that cannot always be modelled using first principles to determine their dynamical behaviour. Since modelling nonlinear systems is normally a difficult task, fuzzy models have been employed in many identification problems due its inherent nonlinear characteristics and simple structure, as well. This proposed chaotic PSO (CPSO) approach is employed here for optimizing the premise part of the IF-THEN rules of TS fuzzy model; for the consequent part, least mean squares technique is used. the proposed method is utilized in an experimental application; a thermal-vacuum system which is employed for space environmental emulation and satellite qualification. Results obtained with a variety of CPSO's are compared with traditional PSO approach. Numerical results indicate that the chaotic PSO approach succeeded in eliciting a TS fuzzy model for this nonlinear and time-delay application. (C) 2007 Elsevier B.V. All rights reserved.Inst Nacl Pesquisas Espaciais, Integrat & Testing Lab, BR-227010 Sao Jose Dos Campos, SP, BrazilUniversidade Federal de São Paulo, Comp Sci Hlth Informat Dept DIS, BR-04023062 São Paulo, BrazilPontifical Catholic Univ Parana PUC PR, Lab Automat & Systems LAS PPGEPS, BR-80215901 Curitiba, Parana, BrazilUniversidade Federal de São Paulo, Comp Sci Hlth Informat Dept DIS, BR-04023062 São Paulo, BrazilWeb of Scienc