A novel ACO–GA hybrid algorithm for feature selection in protein function prediction

Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. Feature selection (FS) techniques are used to deal with this high dimensional space of features. In this paper, we propose a novel feature selection algorithm that combines genetic algorithms (GA) and ant colony optimization (ACO) for faster and better search capability. The hybrid algorithm makes use of advantages of both ACO and GA methods. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of two prominent population-based algorithms, ACO and genetic algorithms. Experimentation is carried out using two challenging biological datasets, involving the hierarchical functional classification of GPCRs and enzymes. The criteria used for comparison are maximizing predictive accuracy, and finding the smallest subset of features. The results of experiments indicate the superiority of proposed algorithm.     

Determination of number of kanbans in a supply chain system via Memetic algorithm

In this research multi-stage supply chain system which is controlled by kanban system, is evaluated. In kanban system, decision making is based on determination of the number of kanbans as well as batch sizes. This paper attempts to model supply chain system with regard to costs under just-in-time (JIT) production philosophy. Since adopted model is of mixed integer non-linear programming (MINLP) type and solving it by exact algorithm such as branch and bound (B&B) takes a lot of time, a heuristic method via Memetic algorithm (MA) is presented. Some problems are solved by our proposed MA to illustrate its performance.     

The discrete collocation method for Fredholm–Hammerstein integral equations based on moving least squares method

The purpose of this paper is to investigate the discrete collocation method based on moving least squares (MLS) approximation for Fredholm–Hammerstein integral equations. The scheme utilizes the shape functions of the MLS approximation constructed on scattered points as a basis in the discrete collocation method. The proposed method is meshless, since it does not require any background mesh or domain elements. Error analysis of this method is also investigated. Some numerical examples are provided to illustrate the accuracy and computational efficiency of the method.     

A new combination of artificial neural network and <Emphasis Type="Italic">K</Emphasis>-nearest neighbors models to predict blast-induced ground vibration and air-overpressure

Blasting operation is widely used method for rock excavation in mining and civil works. Ground vibration and air-overpressure (AOp) are two of the most detrimental effects induced by blasting. So, evaluation and prediction of ground vibration and AOp are essential. This paper presents a new combination of artificial neural network (ANN) and K-nearest neighbors (KNN) models to predict blast-induced ground vibration and AOp. Here, this combination is abbreviated using ANN-KNN. To indicate performance of the ANN-KNN model in predicting ground vibration and AOp, a pre-developed ANN as well as two empirical equations, presented by United States Bureau of Mines (USBM), were developed. To construct the mentioned models, maximum charge per delay (MC) and distance between blast face and monitoring station (D) were set as input parameters, whereas AOp and peak particle velocity (PPV), as a vibration index, were considered as output parameters. A database consisting of 75 datasets, obtained from the Shur river dam, Iran, was utilized to develop the mentioned models. In terms of using three performance indices, namely coefficient correlation (R ²), root mean square error and variance account for, the superiority of the ANN-KNN model was proved in comparison with the ANN and USBM equations.     

Disassembly Sequencing Using Tabu Search

End-of-life disassembly has developed into a major research area within the sustainability paradigm, resulting in the emergence of several algorithms and structures proposing heuristics techniques such as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Neural Networks (NN). The performance of the proposed methodologies heavily depends on the accuracy and the flexibility of the algorithms to accommodate several factors such as preserving the precedence relationships during disassembly while obtaining near- optimal and optimal solutions. This paper improves a previously proposed Genetic Algorithm model for disassembly sequencing by utilizing a faster metaheuristic algorithm, Tabu search, to obtain the optimal solution. The objectives of the proposed algorithm are to minimize (1) the traveled distance by the robotic arm, (2) the number of disassembly method changes, and (3) the number of robotic arm travels by combining the identical-material components together and hence eliminating unnecessary disassembly operations. In addition to improving the quality of optimum sequence generation, a comprehensive statistical analysis comparing the previous Genetic Algorithm and the proposed Tabu Search Algorithm is also included     

Multi-Verse Optimizer: a nature-inspired algorithm for global optimization

This paper proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search, respectively. The MVO algorithm is first benchmarked on 19 challenging test problems. It is then applied to five real engineering problems to further confirm its performance. To validate the results, MVO is compared with four well-known algorithms: Grey Wolf Optimizer, Particle Swarm Optimization, Genetic Algorithm, and Gravitational Search Algorithm. The results prove that the proposed algorithm is able to provide very competitive results and outperforms the best algorithms in the literature on the majority of the test beds. The results of the real case studies also demonstrate the potential of MVO in solving real problems with unknown search spaces. Note that the source codes of the proposed MVO algorithm are publicly available at http://www.alimirjalili.com/MVO.html.     

Static,eigenvalue problem and bifurcation analysis of MEMS arches actuated by electrostatic fringing-fields

In this work, we investigate the structural behavior of a micro-electromechanical system arch microbeam actuated by electric fringing-fields where the electrodes are located at both side of the microbeam. In this particular configuration, the electrostatic actuating force is caused by the asymmetry of the fringing electric fields acting in a direction opposite to the relative deflection of the microbeam. A reduced-order model is derived for the considered system using the so-called Galerkin decomposition and assuming linear undamped mode shapes of a straight beam as basis functions in the decomposition process. A static analysis is performed to investigate the occurrence of any structural instability. The eigenvalue problem is then investigated to calculate the fundamental as well as higher natural frequencies variation of the microbeam with the applied DC load. A bifurcation analysis is then implemented to derive a criterion for whether symmetric or asymmetric bifurcation is occurring during the static structural instability. The results show elimination of the so-called pull-in instability in this kind of systems as compared to the regular case of parallel-plates electrostatic actuation. The bifurcation analysis shows that the arch goes for asymmetric bifurcation (symmetry breaking) with increase in initial elevation without the occurrence of symmetric bifurcation (snap-through) for any initial elevation.     

Clarifying the interaction between ideas and architectural works in the Achaemenid era

The circle formed in the course of surveying the movement from idea to work—which makes the act of creation—and from work to idea—which is concerned with the sphere of thinking, cognition and perception—leads to the formation of culture and civilization. Formation of any civilization is a direct result of interaction between ideas and forms. If there is no idea, there will be no form, and if there is no form, promotion of idea will be meaningless. From the ancient time, the religious-mythical idea, as a whole idea, has been leading to other ideas including philosophical, mystical and political ideas. Architecture, as a historical form, has been the outcome of interactions between the triple ideas proposed under the religious idea. In this study, an attempt is made to explain the role of each idea in the creation of architectural works of Achaemenid civilization by defining them. It also aims to review the historical form of the Achaemenid era, which was based on a philosophical-political idea, in order to investigate the role of this period in relation to architectural form which is addressed with a kind of pluralism. In other words, the main objective was to clarify the interaction between the prevailing notion of time and architectural works.     

Development of broadband forward‐wave directional couplers loaded by periodic patterned ground structure

In this paper two wideband Forward‐Wave Directional Couplers (FWDCs) with 0 dB and 3 dB coupling level are proposed. Using periodic patterned ground structure in a microstrip coupled lines by a new unit cell; even‐ and odd‐mode characteristic impedances of the couplers are equal over a wide frequency range. Moreover, it provides a constant phase difference between even and odd‐modes. The proposed cell is modeled using the equivalent circuit model and a design procedure is introduced for designing FWDCs for an arbitrary value of coupling level. The introduced couplers are numerically investigated and a prototype of both couplers is made. It is shown that for 0 dB coupling level, the measured coupling is 0.85 dB with 1 dB flatness over fractional bandwidth of 96% bandwidth. In case of 3 dB coupling, the measured coupling level is 3.5 dB at 7.42 GHz with 1 dB flatness over fractional bandwidth of 67.1%.     

Mobile-cloud assisted framework for selective encryption of medical images with steganography for resource-constrained devices

In this paper, the problem of outsourcing the selective encryption of a medical image to cloud by resource-constrained devices such as smart phone is addressed, without revealing the cover image to cloud using steganography. In the proposed framework, the region of interest of the medical image is first detected using a visual saliency model. The detected important data is then embedded in a host image, producing a stego image which is outsourced to cloud for encryption. The cloud which has powerful resources, encrypts the image and sent back the encrypted marked image to the client. The client can then extract the selectively encrypted region of interest and can combine it with the region of non-interest to form a selectively encrypted image, which can be sent to medical specialists and healthcare centers. Experimental results and analysis validate the effectiveness of the proposed framework in terms of security, image quality, and computational complexity and verify its applicability in remote patient monitoring centers.