Differential evolution (DE) has recently emerged as simple and efficient algorithm for global optimization over continuous spaces. DE shares many features of the classical genetic algorithms (GA). But it is much easier to implement than GA and applies a kind of differential mutation operator on parent chromosomes to generate the offspring. Grid computing aims to allow unified access to data, co...

In this work differential evolution strategies are applied in neural networks with integer weights training. These strategies have been introduced by Storn and Price [Journal of Global Optimization, 11, pp. 341–359, 1997]. Integer weight neural networks are better suited for hardware implementation as compared with their real weight analogous. Our intention is to give a broad picture of the beh...

Differential evolution [1] has gained a lot of attention from the global optimization research community. It has proved to be a very robust algorithm for solving non-differentiable and nonconvex global optimization problems. In this paper, we propose some modifications to the original algorithm. Specifically, we use the attraction-repulsion concept of electromagnetismlike algorithm [2, 3] to bo...

Since the introduction of Ant Colony Optimization (ACO) technique in 1992, the algorithm starts to gain popularity due to its attractive features. However, several shortcomings such as slow convergence and stagnation motivate many researchers to stop further implementation of ACO. Therefore, in order to overcome these drawbacks, ACO is proposed to be combined with Differential Evolution (DE) an...

Differential evolution (DE) is a popular computational method used to solve optimization problems with several variants available in the literature. Here, the use of a similarity-based surrogate model is proposed in order to improve DE’s overall performance in computationally expensive problems. The offspring are generated by means of different variants, and only the best one, according to the ...

Differential Evolution (DE) is a simple and efficient optimizer, especially for continuous global optimization. Over the last few decades, DE has often been employed for solving various engineering problems. At the same time, the DE structure has some limitations in the complicated problems. This fact has inspired many researchers to improve on DE by proposing modifications to the original algo...

Differential Evolution (DE) is a simple and efficient optimizer, especially for continuous global optimization. Over the last few decades, DE has often been employed for solving various engineering problems. At the same time, the DE structure has some limitations in the complicated problems. This fact has inspired many researchers to improve on DE by proposing modifications to the original algo...