This paper introduces an automatic strategy for the segmentation of medical images from Magnetic Resonance Imaging (MRI) and Computed Topography (CT). A new segmentation technique is proposed to combine a new evolutionary algorithm, called the Immune System Programming (ISP) algorithm, with the Region Growing (RG) technique. The ISP algorithm with a tree data structure has the ability to create new mathematical threshold functions, and RG can use these functions to achieve an efficient segmentation process for medical images. Several MRI images with different levels of Radio Frequency (RF) and noise are used to test the proposed segmentation technique. In different experiments, the proposed technique showed promising performance and produced a new set of efficient threshold functions.

Wireless ad hoc and sensor networks play an important role in providing flexible deployment and mobile connectivity for next generation network. Since there is no fixed physical backbone infrastructure, some of the nodes are selected to form a virtual backbone. Efficient algorithms for identifying the Minimum Connected Dominating Set (MCDS) have many practical applications in wireless sensor networks deployment and management. We propose two algorithms in this paper for solving the MCDS problem. The first algorithm called Memetic Algorithm for the MCDS problem, or MA-MCDS shortly. This is a new hybrid algorithm based on genetic algorithm in addition to local search strategies for the MCDS problem. In order to achieve fast performance, MA-MCDS algorithm uses local search and intensification procedures in addition to genetic operations. In the second algorithm, simulated annealing is used to enhance a stochastic local search with the ability to of run away from local solutions. In addition, we present a new objective function that effectively measure the quality of the solutions of our proposed algorithms. Both algorithms are tested using different benchmark test graph sets available in the literature, and shows good results in terms of solution quality.

In this paper, we target the problems of finding a global minimum of nonlinear and stochastic programming problems. To solve this type of problem, we propose new approaches based on combining direct search methods with Evolution Strategies (ESs) and Scatter Search (SS) metaheuristics approaches. First, we suggest new designs of ESs and SS with a memory-based element called Gene Matrix (GM) to deal with those type of problems. These methods are called Directed Evolution Strategies (DES) and Directed Scatter Search (DSS), respectively, and they are able to search for a global minima. Moreover, a faster convergence can be achieved by accelerating the evolutionary search process using GM, and in the final stage we apply the Nelder-Mead algorithm to find the global minimum from the solutions found so far. Then, the variable-sample method is invoked in the DES and DSS to compose new stochastic programming techniques. Extensive numerical experiments have been applied on some well-known functions to test the performance of the proposed methods.

Metaheuristics are powerful and generic global search methods. Most metaheuristics methods are not fully equipped with learning processes. Therefore, most of the search history is not reused in further steps of metaheuristics. The main aim of this research is to develop a general framework for automating and enhancing the search process and procedures in metaheuristics. The proposed framework, called Global Sensing Search (GSS), utilizes search memories to equip the search with applicable sensing features and adaptive learning elements to find a better solution and explore more diverse ones. Moreover, the GSS framework applies different search conditions to check the need for using suitable intensification and/or diversification strategies and also for terminating the search. An implementation of the GSS framework is proposed to alter the structure of standard genetic algorithms (GAs). Therefore, a new GA-based method called Genetic Sensing Algorithm is presented. The computational experiments show the efficiency of the proposed methods.

Simulated annealing is a well-known search algorithm used with success history in many search problems. However, the random walk of the simulated annealing does not benefit from the memory of visited states, causing excessive random search with no diversification history. Unlike memory-based search algorithms such as the tabu search, the search in simulated annealing is dependent on the choice of the initial temperature to explore the search space, which has little indications of how much exploration has been carried out. The lack of exploration eye can affect the quality of the found solutions while the nature of the search in simulated annealing is mainly local. In this work, a methodology of two phases using an automatic diversification and intensification based on memory and sensing tools is proposed. The proposed method is called Simulated Annealing with Exploratory Sensing. The computational experiments show the efficiency of the proposed method in ensuring a good exploration while finding good solutions within a similar number of iterations. 

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.