This paper presents a novel and efficient method to estimate the equivalent circuit parameters of three-phase induction motor from its manufacturer data for steady state analysis using improved particle swarm optimization (IPSO). The IPSO integrates the particle swarm optimization (PSO) with the chaotic sequences. The optimization problem is based on minimizing the error between the computed pe...

Aim at the class electromagnetic algorithm (EM) late for “mining” ability is insufficient, solution precision is not higher, and easy in premature problem, this paper proposes a combination of chaotic map and confined quasi-Newton (L-BFGS) local optimization operator of the chaotic class electromagnetism algorithm. Its main idea is in the late class electromagnetism algorithm using limit domain...

In this study the performance of two popular evolutionary computational techniques (particle swarm optimization and differential evolution) is compared in the task of batch reactor geometry optimization. Both algorithms are enhanced with chaotic pseudo-random number generator (CPRNG) based on Lozi chaotic map. It is depicted the course of the reactor processes dynamical parameters for the best ...

The problem of finding appropriate representations for various is a subject of continued research in the field of artificial intelligence and related fields. In some practical situations, mathematical and computational tools for faithfully modeling or representing systems with uncertainties, inaccuracies or variability in computation should be provided; and it is preferable to develop models th...

Feature selection is a useful technique for increasing classification accuracy. The primary objective is to remove irrelevant features in the feature space and identify relevant features. Binary particle swarm optimization (BPSO) has been applied successfully in solving feature selection problem. In this paper, chaotic binary particle swarm optimization (CBPSO) with logistic map for determining...

The proposed approach brings up a manner of cognitiveness that inherits a paradigm in particle swarm optimization to implement a chaotic mapping and enhanced by K-means clustering algorithm. In this work, named KCPSO, chaotic mapping with ergodicity, irregularity and the stochastic properties in PSO contributes to global search while K-means with clustering properties in PSO results in rapid co...

Extended-range electric vehicle is considered to be the ideal transition type for electric vehicle. The optimal operation curve control strategy was proposed for a 12 meter-long range extended electric bus. With exponential function inertia weight adjustment and local chaos substitution, an improved chaotic particle swarm optimization algorithm was applied to optimize the key parameters of ener...

Based on the research of complexity and non-linearity of aero-engine exhaust gas temperature (EGT) system, a regularization adaptive chaotic prediction model applied in short time forecasting of EGT was proposed. In this research, we develop a new hybrid particle swarm optimization (HPSO) arithmetic in order to improve the accuracy of the forecasting model. This arithmetic enhanced the ability ...

this paper proposes a method to solve multi-objective problems using improved particle swarm optimization. we propose leader particles which guide other particles inside the problem domain. two techniques are suggested for selection and deletion of such particles to improve the optimal solutions. the first one is based on the mean of the m optimal particles and the second one is based on appoin...