Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evalu...

To explore the effect of spatial locality, crowding differential evolution is incorporated with spatial locality for multimodal optimization. Instead of random trial vector generations, it takes advantages of spatial locality to generate fitter trial vectors. Experiments were conducted to compare the proposed algorithm (CrowdingDE-L) with the state-of-the-art algorithms. Further experiments wer...

Optimization of turning process is a non-linear optimization with constrains and it is difficult for the conventional optimization algorithms to solve this problem. The purpose of present study is to demonstrate the potential of Imperialist Competitive Algorithm (ICA) for optimization of multipass turning process. This algorithm is inspired by competition mechanism among imperialists and coloni...

Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evalu...

Optimization of turning process is a non-linear optimization with constrains and it is difficult for the conventional optimization algorithms to solve this problem. The purpose of present study is to demonstrate the potential of Imperialist Competitive Algorithm (ICA) for optimization of multipass turning process. This algorithm is inspired by competition mechanism among imperialists and coloni...

optimization of cutting conditions is a non-linear optimization with constraint and it is very important to the increase of productivity and the reduction of costs. in recent years, several evolutionary and meta-heuristic optimization algorithms were introduced. the cuckoo optimization algorithm (coa) is one of several recent and powerful meta-heuristics which is inspired by the cuckoos and the...

optimizing the database queries is one of hard research problems. exhaustive search techniques like dynamic programming is suitable for queries with a few relations, but by increasing the number of relations in query, much use of memory and processing is needed, and the use of these methods is not suitable, so we have to use random and evolutionary methods. the use of evolutionary methods, beca...

Differential evolution (DE) has been shown to be a simple and effective evolutionary algorithm for global optimization both in benchmark test functions and many real-world applications. This paper introduces a dynamic differential evolution (D-DE) algorithm to solve constrained optimization problems. In D-DE, a novel mutation operator is firstly designed to prevent premature. Secondly, the scal...

The vast use of Linear Prediction Coefficients (LPC) in speech processing systems has intensified the importance of their accurate computation. This paper is concerned with computing LPC coefficients using evolutionary algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Dif-ferential Evolution (DE) and Particle Swarm Optimization with Differentially perturbed Velocity (PSO-DV...