the imperialist competitive algorithm (ica) that was recently introduced has shown its good performance in optimization problems. this algorithm is inspired by competition mechanism among imperialists and colonies, in contrast to evolutionary algorithms. this paper presents optimization of bead geometry in welding process using of ica. therefore, two case studies from literature are presented t...

Amultitude of heuristic stochastic optimization algorithms have been described in literature to obtain good solutions of the box-constrained global optimization problem often with a limit on the number of used function evaluations. In the larger question of which algorithms behave well on which type of instances, our focus is here on the benchmarking of the behavior of algorithms by applying ex...

Multiview representation learning is very popular for latent factor analysis. It naturally arises in many data analysis, machine learning, and information retrieval applications to model dependent structures among multiple data sources. For computational convenience, existing approaches usually formulate the multiview representation learning as convex optimization problems, where global optima ...

In rigid plastic analysis one of the most widely applicable methods that is based on the minimum principle, is the combination of elementary mechanisms which uses the upper bound theorem. In this method a mechanism is searched which corresponds to the smallest load factor. Mathematical programming can be used to optimize this search process for simple fra...

This paper discusses an approach to generate test data for path coverage based testing using Genetic Algorithms, Differential Evolution and Artificial Bee Colony optimization algorithms. Control flow graph and cyclomatic complexity of the example program has been used to find out the number of feasible paths present in the program and it is compared with the actual no of paths covered by the ev...

Augmented Downhill Simplex Method (ADSM) is introduced here, that is a heuristic combination of Downhill Simplex Method (DSM) with Random Search algorithm. In fact, DSM is an interpretable nonlinear local optimization method. However, it is a local exploitation algorithm; so, it can be trapped in a local minimum. In contrast, random search is a global exploration, but less efficient. Here, rand...

This paper summaries our recent work on combining estimation of distribution algorithms (EDA) and other techniques for solving hard search and optimization problems: a) guided mutation, an offspring generator in which the ideas from EDAs and genetic algorithms are combined together, we have shown that an evolutionary algorithm with guided mutation outperforms the best GA for the maximum clique ...

This paper proposes three novel techniques for improving the accuracy of the heuristic sinusoidal tracking algorithm proposed in [1]. When applied to audio coding these techniques extend the traditionally speech coding approach into true wideband audio coding. These techniques provide proper multiresolution sinusoidal tracking, matching across a number of variables instead of frequency alone, a...