This paper presents a new simulated annealing algorithm to solve constrained multi-global optimization problems. To compute all global solutions in a sequential manner, we combine the function stretching technique with the adaptive simulated annealing variant. Constraint-handling is carried out through a nondifferentiable penalty function. To benchmark our penalty stretched simulated annealing ...

In this paper, the author proposes the application of a genetic algorithm and simulated annealing to solve the network planning problem. Compared with other optimisation methods, genetic algorithm and simulated annealing are suitable for traversing large search spaces since they can do this relatively rapidly and because the use of mutation diverts the method away from local minima, which will ...

A genetic algorithm implemented in Matlab is presented. Matlab is used for the following reasons: it provides many built in auxiliary functions useful for function optimization; it is completely portable; and it is eecient for numerical computations. The genetic algorithm toolbox developed is tested on a series of non-linear, multi-modal, non-convex test problems and compared with results using...

With increasing interest in human-computer interaction, emotion recognition is becoming a hot spot. Support vector machine(SVM) is a machine learning algorithm based on statistic learning theory, finding the optimal separating hyperplane in the high dimensional feature space, having good classification and generalization ability. According to SVM method becoming very difficult for large data sc...

Two well known stochastic optimization algorithms, simulated annealing and genetic algorithm are compared when using a sample to minimize an objective function which is the expectation of a random variable. Since they lead to minimum depending on the sample, a weighted version of simulated annealing is proposed in order to reduce this kind of overt bias. The algorithms are implemented on an opt...

The paper attempts to solve the generalized “Assignment problem” through genetic algorithm and simulated annealing. The generalized assignment problem is basically the “N menN jobs” problem where a single job can be assigned to only one person in such a way that the overall cost of assignment is minimized. While solving this problem through genetic algorithm (GA), a unique encoding scheme is us...

Automatic text summarization is a process to reduce the volume of text documents using computer programs to create a text summary with keeping the key terms of the documents. Due to cumulative growth of information and data, automatic text summarization technique needs to be applied in various domains. The approach helps in decreasing the quantity of the document without changing the context of...