This paper introduces hybridization of particle swarm optimization (PSO) with genetic algorithm (GA) denoted as PSO+GA provides an efficient approach which is used to solve non linear chaotic datasets. The proposed algorithm employed in probabilistic neural network(PNN) which is a variant of radial basic function artificial neural network (RBFANN) for finding precise value spread factor for acc...

The majority of outage events experienced by customers are due to electrical distribution failures. Increasing network reliability is a necessity in order to reduce interruption events. Distribution network automation can trim down outage events and increase system reliability. Network automation has to be done using optimization approaches. Genetic Algorithm (GA) is a relatively new technique ...

Combinatorial optimization problems arise in many scientific and practical applications. Therefore many researchers try to find or improve different methods to solve these problems with high quality results and in less time. Genetic Algorithm (GA) and Simulated Annealing (SA) have been used to solve optimization problems. Both GA and SA search a solution space throughout a sequence of iterative...

Combinatorial optimization problems arise in many scientific and practical applications. Therefore many researchers try to find or improve different methods to solve these problems with high quality results and in less time. Genetic Algorithm (GA) and Simulated Annealing (SA) have been used to solve optimization problems. Both GA and SA search a solution space throughout a sequence of iterative...

Genetic Algorithm are capable of handling a large number of design parameters and work for optimization problems that have discontinues or non-differentiable multidimensional solution spaces, making them ideal for optimization of machining parameters. Current paper is based on Genetic Algorithm (GA) for optimization of process parameters (e.g. feed and speed) for multi-objective multi pass end ...

An optimization method based on a genetic algorithm (GA) and a boundary element method is applied to solve an electrical impedance tomography problem. The scheme is applied to reconstruct highly irregular shapes and to image and count objects inside a host medium of different impedance. A Pareto multiobjective optimization method is applied to improve the performance of the GA. Comparisons betw...

We describe the use of a Genetic Algorithm (GA) for the Unit Selection problem, which is essentially a search/optimization problem. The various operators for the GA have been defined and comparison with optimization reached by hill climbing approaches is presented.

The solutions used to solve bi-level congestion pricing problems are usually based on heuristic network optimization methods which may not be able to find the best solution for these type of problems. The application of meta-heuristic methods can be seen as viable alternative solutions but so far, it has not received enough attention by researchers in this field. Therefore, the objective of thi...

Genetic Algorithms (GAs) have become a popular optimization tool for many areas of research and topology optimization an effective design tool for obtaining efficient and lighter structures. In this paper, a versatile, robust and enhanced genetic algorithm (GA) is proposed for structural topology optimization by using problem-specific knowledge. The original discrete black-and-white (0-1) probl...

: nowadays, organizations are faced with a multitude of project and investment opportunities. despite the importance of various criteria, complexity of multi-objective models and weakness of optimization algorithms often compelled manager to limit the selection criteria or only suffice to financial objects. in this paper, it is endeavored to extend selection criteria by using an efficient optim...