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Image segmentation is considered one of the most important tasks in image processing, which has several applications in different areas such as; industry agriculture, medicine, etc. In this paper, we develop the electromagnetic optimization (EMO) algorithm based on levy function, EMO-levy, to enhance the EMO performance for determining the optimal multi-level thresholding of image segmentation. In general, EMO simulates the mechanism of attraction and repulsion between charges to develop the individuals of a population. EMO takes random samples from search space within the histogram of image, where, each sample represents each particle in EMO. The quality of each particle is assessed based on Otsu's or Kapur objective function value. The solutions are updated using EMO operators until determine the optimal objective functions. Finally, this approach produces segmented images with optimal values for the threshold and a few number of iterations. The proposed technique is validated using different standard test images. Experimental results prove the effectiveness and superiority of the proposed algorithm for image segmentation compared with well-known optimization methods.

The robust design problem in a flow network is defined as search optimal node capacity that can be assigned such that the network still survived even under the node’s failure. This problem is considered as an NP-hard. So, this paper proposes a genetic algorithm-based approach to solve it for a flow network with node failure. The proposed based genetic approach is used to assign the optimal capacity for each node to minimize the total capacities and maximize the network reliability. The proposed approach takes the capacity for each critical node should have the maximum capacity (usually equals to the demand value) to alleviate that the reliability to drop to zero. Three network examples are used to show the efficiency of our algorithm. Also, the results obtained by our approach are compared with those obtained by the previous approximate algorithm.

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This paper presents the identical parallel machine's scheduling problem when the jobs are submitted over time. This problem consists of assigning N various jobs to M identical parallel machines to reduce the workload imponderables among the different machines. We generalized the mixed-integer linear programming approach to decrease the workload imbalance between the different machines, and that is done by converting the problem to the mathematical model. The studied cases are presented for different problems, and it indicates to an online system, and this system does not know the arrival times of the jobs before and reduce Makespan criterion is not well appropriate to describe the utilization for this online problem. The obtained results proved good solutions for the scheduling problem compared with standard algorithms.