due to the fact that the error surface of adaptive infinite impulse response (iir) systems is generally nonlinear and multimodal, the conventional derivative based techniques fail when used in adaptive identification of such systems. in this case, global optimization techniques are required in order to avoid the local minima. harmony search (hs), a musical inspired metaheuristic, is a recently ...

This paper presents a novel approach for designing a linear phase digital high pass FIR filter using Improved Particle Swarm Optimization (IPSO) algorithm. Design of FIR f ilter is a multi-modal optimization problem. The conservative gradient based optimization techniques are not efficient for digital filter design. Given the specifications for the filters to be realized, IPSO algorithm generat...

In this study we compare the performance of three evolutionary algorithms such as Genetic Algorithm (GA) Particle Swarm Optimization (PSO) and Ant-Colony Optimization (ACO) which are used to optimize the Artificial Neural Network (ANN). Optimization of Neural Networks improves speed of recall and may also improve the efficiency of training. Here we have used the Ant colony optimization, Particl...

Flexible job-shop scheduling problem (FJSP) has been well known as one of the most difficult NP-hard combinatorial optimization problems. This problem FJSP is an extension of job-shop scheduling problem (JSP) which is to create a sequence of operations satisfying the precedence relationship together with assignment of time and resources for each operation. Thus, the FJSP model is closer to the ...

Artificial bee colony (ABC) algorithm invented recently by Karaboga is a biological-inspired optimization algorithm, which has been shown to be competitive with some conventional biological-inspired algorithms, such as genetic algorithm (GA), differential evolution (DE) and particle swarm optimization (PSO). However, there is still an insufficiency in ABC algorithm regarding its solution search...

Several biology inspired optimization algorithms such as Genetic Algorithms, Ant Colony Optimization (ACO)and Particle Swarm Optimization (PSO) have previously been proposed by researchers. Recent approaches in numerical optimization have shifted to motivate from complex human social behaviors. In this paper, a new optimization algorithm, namely parliamentary optimization algorithm (POA) is pro...

This paper presents a novel learning algorithm for training and constructing a Radial Basis Function Neural Network (RBFNN), called MuPSORBFNN algorithm. This algorithm combines Particle Swarm Optimization algorithm (PSO) with mutation operation to train RBFNN. PSO with mutation operation and genetic algorithm are respectively used to train weights and spreads of oRBFNN, which is traditional RB...

In this paper we propose a novel hybrid algorithm (GA/PSO) combining the strengths of particle swarm optimization with genetic algorithms to evolve the weights of recurrent neural networks. Particle swarm optimization and genetic algorithms are two optimization techniques that have proven to be successful in solving difficult problems, in particular both can successfully evolve recurrent neural...

Developing artificial neural network (ANN), a model to make a correct prediction of required force and torque in ring rolling process is developed for the first time. Moreover, an optimal state of process for specific range of input parameters is obtained using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods. Radii of main roll and mandrel, rotational speed of main roll, pr...