neural network is one of the most widely used algorithms in the field of machine learning, on the other hand, neural network training is a complicated and important process. supervised learning needs to be organized to reach the goal as soon as possible. a supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.  hen...

  The clustering problem under the criterion of minimum sum of squares is a non-convex and non-linear program, which possesses many locally optimal values, resulting that its solution often falls into these trap and therefore cannot converge to global optima solution. In this paper, an efficient hybrid optimization algorithm is developed for solving this problem, called Tabu-KM. It gathers the ...

This paper investigates the ability of variously designed & trained Artificial Neural Network (ANN) to predict the probability of occurrence of Hypertension (HT) in a mixed (healthy + hypertensive, both sexes) patient population. To do this a multi layer feed-forward neural network with 13 inputs and 1 output was created with multiple hidden layers. Network parameters such as count of hidden la...

This paper aims to prove the efficiency of an adapted computationally intelligence-based behavior of cats called the cat swarm optimization algorithm, that solves the open shop scheduling problem, classified as NP-hard which its importance appears in several industrial and manufacturing applications. The cat swarm optimization algorithm was applied to solve some benchmark instances from the lit...

Recently, many meta-heuristic algorithms are proposed for optimization of various problems. Some of them originally are presented for continuous optimization problems and some others are just applicable for discrete ones. In the literature, sizing optimization of truss structures is one of the discrete optimization problems which is solved by many meta-heuristic algorithms. In this paper, in or...

in this paper, a method based on binary-coded genetic algorithm is proposed to explore an optimization method, for obtaining an optimal elliptical tank. this optimization method enhances the rollover threshold of a tank vehicle, especially under partial filling conditions. minimizing the overturning moment imposed on the vehicle due to c.g. height of the liquid load, lateral acceleration and ca...

The cuckoo search algorithm is a recently developedmeta-heuristic optimization algorithm, which is suitable forsolving optimization problems. To enhance the accuracy andconvergence rate of this algorithm, an improved cuckoo searchalgorithm is proposed in this paper. Normally, the parametersof the cuckoo search are kept constant. This may lead todecreasing the efficiency of the algorithm. To cop...

in this paper we introduce an improvement in the path planning algorithm for the humanoid soccer playing robot which uses ferguson splines and pso (particle swarm optimization). the objective of the algorithm is to find a path through other playing robots to the ball, which should be as short as possible and also safe enough. ferguson splines create preliminary paths using random generated para...

In this paper proposes a fuzzy multi-objective hybrid Genetic and Bee colony optimization algorithm(GA-BCO) to find the optimal restoration of loads of power distribution network under fault.Restoration of distribution systems is a complex combinatorial optimization problem that should beefficiently restored in reasonable time. To improve the efficiency of restoration and facilitate theactivity...

Meta-heuristic algorithms inspired by the natural processes are part of the optimization algorithms that they have been considered in recent years, such as genetic algorithm, particle swarm optimization, ant colony optimization, Firefly algorithm. Recently, a new kind of evolutionary algorithm has been proposed that it is inspired by the human sociopolitical evolution process. This new algorith...