Aboul Ella Hassanien (Abo)

Both simulated annealing (SA) and the genetic algorithms (GA) are stochastic and derivative-free optimization technique. SA operates on one solution at a time, while the GA maintains a large population of solutions, which are optimized simultaneously. Thus, the genetic algorithm takes advantage of the experience gained in the past exploration of the solution space. Since SA operates on one solution at a time, it has very little history to use in learning from past trials. SA has the ability to escape from any local point; even it is a global optimization technique. On the other hand, there is no guarantee that the GA algorithm will succeeded in escaping from any local minima, thus it makes sense to hybridize the genetic algorithm and the simulated annealing technique. In this paper, a novel genetically annealed algorithm is proposed and is tested against multidimensional and highly nonlinear cases; Fed-batch fermentor for Penicillin production, and isothermal continuous stirred tank reactor CSTR. It is evident from the results that the proposed algorithm gives good performance.

Due to their excellent performance in solving combinatorial optimization problems, metaheuristics algorithms such as Genetic Algorithms GA [35], [18], [5], Simulated Annealing SA [34], [13] and Tabu Search TS make up another class of search methods that has been adopted to efficiently solve dynamic optimization problem. Most of these methods are confined to the population space and in addition the solutions of nonlinear problems become quite difficult especially when they are heavily constrained. They do not make full use of the historical information and lack prediction about the search space. Besides the knowledge that individuals inherited "genetic code" from their ancestors, there is another component called Culture. In this paper, a novel culture-based GA algorithm is proposed and is tested against multidimensional and highly nonlinear real world applications.

While most existing virtual reality-based surgical simulators in the literature use linear deformation models, soft-tissues exhibit geometric and material nonlinearities that should be taken into account for realistic modelling of the deformations. In this paper, an interactive soft tissue model (ISTM) which enables flexible, accurate and robust simulation of surgical interventions on virtual patients is proposed. In ISTM, simulating the tool-tissue interactions using nonlinear dynamic analysis is formulated within a total Lagrangian framework, and the energy function is modified by adding a term in order to achieve material incompressibility. The simulation results show that ISTM increases the stability and eliminates integration errors in the dynamic solution, decreases calculation costs by a factor of 5-7, and leads to very stable and sufficiently accurate results. From the simulation results it can be concluded that the proposed model can successfully create acceptable soft tissue models and generate realistically visual effects of surgical simulation.

In the past few years, many wireless sensor networks (WSN) had been deployed. It has proved its usage in the future distributed computing environment. Some of its specific applications are habitat monitoring, object tracking, nuclear reactor controlling, fire detection, traffic monitoring, and health care. The main goals of this paper is to describe the major challenges and open research problems of using WSN in healthcare and survey advancements in using WSN to build a chronically implanted artificial retina for visually impaired people. Using WSN in vision repairing addresses two retinal diseases: Age-related Macular Degeneration (severe vision loss at the center of the retina in over 60) and Retinitis Pigmentosa (photoreceptor dysfunction → loss of peripheral vision). The use of WSN in artificial retina provides new features that have the potential to be an economically viable to assist people with visual impairments.

Wireless sensor networks are battery-powered ad hoc networks in which sensor nodes that are scattered over a region connect to each other and form multi-hop networks. Since these networks consist of sensors that are battery operated, care has to be taken so that these sensors use energy efficiently. This paper proposes an optimized hierarchical routing technique which aims to reduce the energy consumption and prolong network lifetime. In this technique, the selection of optimal cluster head (CHs) locations is based on artificial fish swarm algorithm that applies various behaviors such as preying, swarming, and following to the formulated clusters and then uses a fitness function to compare the outputs of these behaviors to select the best CHs locations. To prove the efficiency of the proposed technique, its performance is analyzed and compared to two other well-known energy efficient routing techniques: low-energy adaptive clustering hierarchy (LEACH) technique and particle swarm optimized (PSO) routing technique. Simulation results show the stability and efficiency of the proposed technique. Simulation results show that the proposed method outperforms both LEACH and PSO in terms of energy consumption, number of alive nodes, first node die, network lifetime, and total data packets received by the base station. This may be due to considering residual energies of nodes and their distance from base station , and alternating the CH role among cluster’s members. Alternating the CH role balances energy consumption and saves more energy in nodes.

The minimum number attribute reduction problem is an important issue when dealing with huge amounts of data. The problem of minimum attribute reduction is formally known to be as an NP complete nonlinearly constrained optimization problem. Social spider optimization algorithm is a new meta-heuristic algorithm of the swarm intelligence field to global solution. The social spider optimization algorithm is emulates the behavior of cooperation between spiders based on the biological laws of the cooperative colony. Inspired by the social spiders, in this paper, an improved social spider algorithm for the minimal reduction problem was proposed. In the proposed algorithm, the fitness function depends on the rough sets dependency degree and it takes into a consideration the number of selected features. For each spider, the fitness function is computed and compared with the global best fitness value. If the current value is better, then the global best fitness is replaced with it and its position became the reduct set. Then, the position of each spider is updated according to its type. This process is repeated until the stopping criterion is satisfied. To validate the proposed algorithm, several real clinical medical datasets which are available from the UCI Machine Learning Repository were used to compute the performance of the proposed algorithm. The experimental results illustrate that the proposed algorithm is superior to state-of-the-art swarm-based in terms of classification accuracy while limiting number of features.

The global prevalence of hepatitis C Virus (HCV) is approximately 3% and one-fifth of all HCV carriers live in the Middle East, where Egypt has the highest global incidence of HCV infection. Quantitative structure-activity relationship (QSAR) models were used in many applications for predicting the potential effects of chemicals on human health and environment. The adaptive neuro-fuzzy inference system (ANFIS) is one of the most popular regression methods for building a nonlinear QSAR model. However, the quality of ANFIS is influenced by the size of the descriptors, so descriptor selection methods have been proposed, although these methods are affected by slow convergence and high time complexity. To avoid these limitations, the antlion optimizer was used to select relevant descriptors, before constructing a nonlinear QSAR model based on the PIC50 and these descriptors using ANFIS. In our experiments, 1029 compounds were used, which comprised 579 HCVNS5B inhibitors (PIC50 < ~14) and 450 non-HCVNS5B inhibitors (PIC50 > ~14). The experimental results showed that the proposed QSAR model obtained acceptable accuracy according to different measures, where R2 was 0.952 and 0.923 for the training and testing sets, respectively, using cross-validation, while R2 LOO
was 0.8822 using leave-one-out (LOO).

In this paper, a modified cuckoo search algorithm with rough sets is presented to deal with high dimensionality data through feature selection. The modified cuckoo search algorithm imitates the obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds. The modified cuckoo search uses the rough sets theory to build the fitness function that takes the number of features in reduct set and the classification quality into account. The proposed algorithm is tested and validated benchmark on several benchmark datasets drawn from the UCI repository and using different evaluation criteria as well as a further analysis is carried out by means of the Analysis of Variance test. In addition, the proposed algorithm is experimentally compared with the existing algorithms on discrete datasets. Finally, two learning algorithms, namely K-nearest neighbors and support vector machines are used to evaluate the performance of the proposed approach. The results show that the proposed algorithm can significantly improve the classification performance.

In the recent years, there are massive digital images collections in many fields of our life, which led the technology to find methods to search and retrieve these images efficiently. The content-based is one of the popular methods used to retrieve images, which depends on the color, texture and shape descriptors to extract features from images. However, the performance of the content-based image retrieval methods depends on the size of features that are extracted from images and the classification accuracy. Therefore, this problem is considered as a multi-objective and there are several methods that used to manipulate it such as NSGA-II and NSMOPSO. However, these methods have drawbacks such as their time and space complexity are large since they used traditional non-dominated sorting methods. In this paper, a new non-dominated sorting based on multi-objective whale optimization algorithm is proposed for content-based image retrieval (NSMOWOA). The proposed method avoids the drawbacks in other non-dominated sorting multi-objective methods that have been used for content-based image retrieval through reducing the space and time complexity. The results of the NSMOWOA showed a good performance in content-based image retrieval problem in terms of recall and precision.