Hybrid evolutionary algorithms (EAs) are effective optimization methods that combine multiple EAs. We propose several hybrid EAs by combining some recently-developed EAs with a biogeography-based hybridization strategy. We test our hybrid EAs on the continuous optimization benchmarks from the 2013 Congress on Evolutionary Computation (CEC) and on some real-world traveling salesman problems. The...

Evolutionary computation has become an important problem solving methodology among the set of search and optimization techniques. Recently, more and more different evolutionary techniques have been developed, especially hybrid evolutionary algorithms. This paper proposes an island based hybrid evolutionary algorithm (IHEA) for optimization, which is based on Particle swarm optimization (PSO), F...

Based on the problems caused by today conventional vehicles, much attention has been put on the fuel cell vehicles researches. However, using a fuel cell system is not adequate alone in transportation applications, because the load power profile includes transient that is not compatible with the fuel cell dynamic. To resolve this problem, hybridization of the fuel cell and energy storage device...

Mining Sequential Patterns in large databases has become an important data mining task with broad applications. It is an important task in data mining field, which describes potential sequenced relationships among items in a database. There are many different algorithms introduced for this task. Conventional algorithms can find the exact optimal Sequential Pattern rule but it takes a long time,...

Cellular manufacturing system, an application of group technology, has been considered as an effective method to obtain productivity in a factory. For design of manufacturing cells, several mathematical models and various algorithms have been proposed in literature. In the present research, we propose an improved version of discrete particle swarm optimization (PSO) to solve manufacturing cell ...

ABSTRACT In this paper we are analyzing the performance of feedforward neural networks with hybrid evolutionary algorithms for the recognition of hand written English alphabets after their scaling and rotation in the test pattern set whereas in the training set straight handwritten alphabets have used. In this performance analysis the neural network is trained with hybrid evolutionary algorithm...

When the standard genetic algorithm is used to solve the fuzzy programming problem, poor convergence occurs. In order to overcome this defect, this paper presents a hybrid intelligent evolutionary algorithm based on nonlinear support vector machine (SVM) to solve the fuzzy programming problem. Firstly, based on the research of genetic algorithm, evolutionary strategy and genetic algorithm are c...

In this paper we propose a novel approach to the problem of microscrew thread parameter estimation based on a hybrid evolutionary algorithm that combines a stochastic evolutionary algorithm with the deterministic inverse parabolic interpolation. The proposed method uses a machine vision system utilizing a single web camera. The hybrid evolutionary algorithm was tested on a specially created ima...

Evolutionary Algorithms are powerful optimization techniques which have been applied to many different problems, from complex mathematical functions to real-world applications. Some studies report performance improvements through the combination of different evolutionary approaches within the same hybrid algorithm. However, the mechanisms used to control this combination of evolutionary approac...