With the rapid growth of simulation software packages, generating practical tools for simulation-based optimization has attracted a lot of interest over the last decades. In this paper, a modified method of Estimation of Distribution Algorithms (EDAs) is constructed by a combination with variable-sample techniques to deal with simulation-based optimization problems. Moreover, a new variable-sample technique is introduced to support the search process whenever the sample sizes are small, especially in the beginning of the search process. The proposed method shows efficient results by simulating several numerical experiments. 

Enhancing Evolutionary Algorithms (EAs) using mathematical elements significantly contribute to their development and control the randomness they are experiencing. Moreover, the automation of the primary process steps of EAs is still one of the hardest problems. Specifically, EAs still have no robust automatic termination criteria. Moreover, the highly random behavior of some evolutionary operations should be controlled, and the methods should invoke advanced learning process and elements. As follows, this research focuses on the problem of automating and controlling the search process of EAs by using sensing and mathematical mechanisms. These mechanisms can provide the search process with the needed memories and conditions to adapt to the diversification and intensification opportunities. Moreover, a new quadratic coding and quadratic search operator are invoked to increase the local search improving possibilities. The suggested quadratic search operator uses both regression and Radial Basis Function (RBF) neural network models. Two evolutionary-based methods are proposed to evaluate the performance of the suggested enhancing elements using genetic algorithms and evolution strategies. Results show that for both the regression, RBFs and quadratic techniques could help in the approximation of high-dimensional functions with the use of a few adjustable parameters for each type of function. Moreover, the automatic termination criteria could allow the search process to stop appropriately.

An efficient routing using a virtual backbone (VB) network is one of the most significant improvements in the wireless sensor network (WSN). One promising method for selecting this subset of network nodes is by finding the minimum connected dominating set (MCDS), where the searching space for finding a route is restricted to nodes in this MCDS. Thus, finding MCDS in a WSN provides a flexible low-cost solution for the problem of event monitoring, particularly in places with limited or dangerous access to humans as is the case for most WSN deployments. In this paper, we proposed an adaptive scatter search (ASS-MCDS) algorithm that finds the near-optimal solution to this problem. The proposed method invokes a composite fitness function that aims to maximize the solution coverness and connectivity and minimize its cardinality. Moreover, the ASS-MCDS methods modified the scatter search framework through new local search and solution update procedures that maintain the search objectives. We tested the performance of our proposed algorithm using different benchmark-test-graph sets available in the literature. Experiments results show that our proposed algorithm gave good results in terms of solution quality.

Generating practical methods for simulation-based optimization has attracted a great deal of attention recently. In this paper, the estimation of distribution algorithms are used to solve nonlinear continuous optimization problems that contain noise. One common approach to dealing with these problems is to combine sampling methods with optimal search methods. Sampling techniques have a serious problem when the sample size is small, so estimating the objective function values with noise is not accurate in this case. In this research, a new sampling technique is proposed based on fuzzy logic to deal with small sample sizes. Then, simulation-based optimization methods are designed by combining the estimation of distribution algorithms with the proposed sampling technique and other sampling techniques to solve the stochastic programming problems. Moreover, additive versions of the proposed methods are developed to optimize functions without noise in order to evaluate different efficiency levels of the proposed methods. In order to test the performance of the proposed methods, different numerical experiments were carried out using several benchmark test functions. Finally, three real-world applications are considered to assess the performance of the proposed methods.

In wireless sensor/ad hoc networks, all wireless nodes frequently flood the network channel by transmitting control messages causing “broadcast storm problem”. Thus, inspired by the physical backbone in wired networks, a Virtual Backbone (VB) in wireless sensor/ad hoc networks can help achieve efficient broadcasting. A well-known and well-researched approach for constructing virtual backbone is solving the Connected Dominating Set (CDS) problem. Furthermore, minimizing the size of the CDS is a significant research issue. We propose a new parallel scatter search algorithm with elite and featured cores for constructing a wireless sensor/ad hoc network virtual backbones based on finding minimum connected dominating sets of wireless nodes. Also, we addressed the problem of VB node/nodes failure by either deploying a previously computed VBs provided by the main pSSEF algorithm that does not contain the failed node/nodes, or by using our proposed FT-pSSEF algorithm repairing the broken VBs. Finally, as nodes in a VB incur extra load of communication and computation, this leads to faster power consumption compared to other nodes in the network. Consequently, we propose the virtual backbone scheduling algorithm SC-pSSEF which aims to find multiple VBs using the VBs provided by the pSSEF algorithm and switch between them periodically to prolong the network life time.

Images are very important forms of data that are widely used nowadays. Every day, millions of grey and color images are transferred via the web. Protecting these images from unauthorized persons is an important issue. Image encryption is one of the image-securing techniques. One advantage of using encryption is that the plain image is converted to an unrecognized one. Also, the plain image is restored without any loss of information. In this paper, a color image encryption technique using blocks scrambling and chaos is presented. First, the plain image is decomposed into three channels: R, G, and B. Then, each channel is divided into sub-images and blocks. Our encryption algorithm depends on two main steps: scrambling and diffusion. First, the pixels’ arrangement in sub-images and blocks is changed, and then scrambling between sub-images is done to get the scrambled image. Then, in diffusion, the scrambled image diffuses using the logistic map to get the encrypted image. The experimental results show that our proposed algorithm has good performance in encrypting color images.

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The single watermarking algorithm for medical images faced several inherent problems like lack of security. In
this paper, a new geometrically invariant multiple zero-watermarking method is proposed to secure medical
images. We proposed a novel set of multi-channels shifted Gegenbauer moments of fractional orders (FrMGMs).
These moments are used to extract the geometrically invariant features from the color medical images. Then we
construct a featured image of the original medical image using the magnitude of the selected precise FrMGMs
moments. Finally, we applied a scrambling method to scramble the watermark image and then the exclusive OR
operation to the images’ feature and scrambled watermark to construct a zero-watermark. Experimental results
proved that the proposed approach effectively provided better robustness to various standard attacks and outperformed the existing single-, dual-, & triple-zero-watermarking algorithms for medical images.

The problem of finding similarity between natural language sentences is crucial for many applications in Natural Language Processing (NLP). Moreover, accurate calculation of similarity between sentences is highly needed. Many approaches depends on word-to-word similarity to measure sentences similarity. This paper proposes a new approach to improve accuracy of sentences similarity calculation. The proposed approach combines different similarity measures in calculation of sentences similarity. In addition to traditional word-to-word similarity measure the proposed approach exploits sentences semantic structure. Discourse representation structure (DRS) which is a semantic representation for natural sentences is generated and used to calculated structure similarity. Furthermore, word order similarity is measured to consider order of words in sentences. Experiments show that exploiting structural information achieves good results. Moreover, the proposed method outperforms the current approaches on Pilot standard benchmark dataset achieving 0.8813 peasron correlation with human similarity.