Feature extraction and accurate classification of the emotion-related EEG-characteristics have a key role in success of emotion recognition systems. In this paper, an optimal EEG-based emotion recognition algorithm based on spectral features and neural network classifiers is proposed. In this algorithm, spectral, spatial and temporal features are selected from the emotion-related EEG signals by...

Vehicle Routing Problem (VRP) is addressed to a class of problems for determining a set of vehicle routes, in which each vehicle departs from a given depot, serves a given set of customers, and returns back to the same depot. On the other hand, simultaneous delivery and pickup problems have drawn much attention in the past few years due to its high usage in real world cases. This study, therefo...

Unit commitment problem is a large scale nonlinear hybrid integer programming problem. Optimal unit commitment scheduling involves determining on/off states of units and determining generations of units. This paper proposes an improved particle swarm optimization (IPSO) for the solution of optimal unit commitment problem (UCP). In the proposed approach, the on/off states of units are limited in...

⎯ This paper proposed an improved particle swarm optimization algorithm (IPSO) to solve continuous function optimization problems. Two improvement strategies named “Vector correction strategy” and “Jump out of local optimum strategy” were employed in our improved algorithm. The algorithm was tested using 25 newly proposed benchmark instances in Congress on Evolutionary Computation 2005 (CEC200...

In this paper, an integration of Improve Particle Swarm Optimization (IPSO) in combination with Successive Quadratic programming (SQP) so called IPSO-SQP algorithm is proposed to solve time optimal bang-bang control problems. The procedure is found not sensitive to the initial guess of the solution. Due to random selection in the first stage of the search process, the chance of converging to th...

An improved particle swarm optimization (IPSO) is used to solve economic dispatch problem (EDP). The IPSO has two position updating strategies. In the early stage of iteration, the individual in the population updates the position according to its own best experience with a large probability. In the later stage of iteration, the individual updates the position according to the best experience i...

In this paper, economic load dispatch (ELD) problem is solved by applying a suggested improved particle swarm optimization (IPSO) for reaching the lowest total power generation cost from wind farms (WFs) and thermal units (TUs). The IPSO modified version of Particle (PSO) changing velocity position updates. five best solutions are employed to replace so-far each in update mechanism used previou...

The brain–computer interface (BCI) is a system that transforms the brain activity of different mental tasks into a control signal. The system provides an augmentative communication method for patients with severe motor disabilities. In this paper, a neural classifier based on improved particle swarm optimization (IPSO) is proposed to classify an electroencephalogram (EEG) of mental tasks for le...

Based on the life cycle cost (LCC) approach, this paper presents an integral mathematical model and particle swarm optimization (PSO) algorithm for the heating system planning (HSP) problem. The proposed mathematical model minimizes the cost of heating system as the objective for a given life cycle time. For the particularity of HSP problem, the general particle swarm optimization algorithm was...

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...