This paper aims to solve railway crew scheduling problem that is concerned with building the work schedules of crews needed to cover a planned timetable. The proposed model is a two-phase procedure. For the FIRST phase, the pairing generation, a DEPTH FIRST SEARCH approach is employed to generate a set of round trips starting and ending at home base. The second phase deals with the selection of a subset of the generated pairings with minimal cost, covering all trips that have to carry out during a predefined time period. This problem, which is modeled by set covering formulation, is solved with a genetic ALGORITHM. In order to check for quality and validity of the suggested method, we compare its results with the final solutions that are produced over the 6 test problems of Beasley and Cao (1996). Comparison of the results indicates good quality and solutions.

In this study, free version of MEDLINE database in the web which is PubMed and three Commercial versions which are Ebsco, ISI and FIRST SEARCH were Chosen. These Versions were gathered in five different features including: general information, SEARCHing features, display options, retrieval options and unique features. Then by use of comparative survey they were compared. The results showed that Ebsco has the best user interface features, followed by, PubMed, ISI and FIRST SEARCH. Of course when choosing a database one should have in mind the level of end users, their information needs and the budget of the library.

Graphs are powerful data representations used in enormous computational domains. In graph-based applications, a systematic exploration of graph such as a breath FIRST SEARCH often is a fundamental component in the processing of the vast data sets. In this paper we presented a hybrid method that in each level of processing of graph chooses the best implementation of ALGORITHMs implemented on CPU or GPU, while avoid poor performance on low and high degree graphs. Our method shows improved performance over the current state-of-the-art implementation and our results proves it.

Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence ALGORITHM based on the binary ALGORITHM of particle swarm optimization and a novel local SEARCH strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the local SEARCH strategy to increase the convergence speed. The FIRST group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a local operator is added to the particle swarm optimization ALGORITHM to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed ALGORITHM compared to other ALGORITHMs presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

Random based inventive ALGORITHMs are being widely used for optimization. An important category of these ALGORITHMs comes from the idea of physical processes or the behavior of beings. A new method for achieving quasi-optimal solutions related to optimization problems in various sciences is proposed in this paper. The proposed ALGORITHM for optimizing the orientation game is a series of optimization ALGORITHMs that are formed with the idea of an old game and the SEARCH operators are an arrangement of players. These players are displaced in a certain space, under the influence of the referee's orders. The best position would be achieved by following the game laws. In this paper, the real version of the ALGORITHM is presented. The optimization results of a set of standard functions confirm the optimal efficiency of the proposed method, as well as the superiority of the proposed method over the other well-known metaheuristic ALGORITHMs.

In recent years, Online Social Network (OSN) has been rapidly evolving and attracted many users. In OSN, users share sensitive information,therefore, effective access control models are needed to protect information from unauthorized users. Currently, Relational Based Access Control (ReBAC) is used to protect user’s private information. The authorization policy in ReBAC is based on the relationship type and DEPTH among users,however, it is not sufficient to protect private information such as location, time, and age. In this paper, attributes are added to the social graph to establish an efficient access control in OSN, then a policy model is proposed for the new Attribute Relation Based Access Control model (A-ReBAC), and unambiguous Hybrid Logic (HL) policy language is used to formulate the access control policy model. To evaluate the proposed policy model two path-checking ALGORITHMs (DEPTH-FIRST SEARCH ((DFS)) and breadth-FIRST SEARCH (BFS)) are applied to real datasets, and the time spent on access requests is calculated in the social graph of these datasets. The results showed (DFS) takes less time than BFS to do the task defined.

In this paper, a new ALGORITHM called Motion Coding Gravitational SEARCH ALGORITHM (MGSA) is proposed to find a moving target using a unmanned aerial vehicles (UAVs). Using the laws of physics and the properties of the earth, each dimension has its own equation of motion based on the type of variable. Many traditional exploratory methods can not achieve the desired solution in high-dimensional spaces to SEARCH for a moving target. The optimization process of the gravitational SEARCH ALGORITHM, which is based on the gravitational interaction between particles, the dependence on the distance and the relationship between mass values, and the fit calculation, make this ALGORITHM unique. In this paper, the proposed MGSA ALGORITHM is proposed to solve the path complexity challenge problem in order to find the moving target through motion coding using UAVs. A set of particles in the path of SEARCH for the target will reach a near-optimal solution through the gravity constant, weight factor, force and distance, which evolved with many SEARCH scenarios in a GSA ALGORITHM. This coded method of motion makes it possible to preserve important particle properties, including the optimum global motion. The results of the existing simulation show that the proposed MGSA improves the detection performance by 12% and the time performance by 1. 71 times compared to APSO. It works better.

This paper presents a compact neural architecture SEARCH method for image classification using the Gravitational SEARCH ALGORITHM (GSA). Deep learning, through multi-layer computational models, enables automatic feature extraction from raw data at various levels of abstraction, playing a key role in complex tasks such as image classification. Neural Architecture SEARCH (NAS), which automatically discovers new architectures for Convolutional Neural Networks (CNNs), faces challenges such as high computational complexity and costs. To address these issues, a GSA-based approach has been developed, employing a bi-level variable-length optimization technique to design both micro and macro architectures of CNNs. This approach, leveraging a compact SEARCH space and modified convolutional bottlenecks, demonstrates superior performance compared to state-of-the-art methods. Experimental results on CIFAR-10, CIFAR-100, and ImageNet datasets reveal that the proposed method achieves a classification accuracy of 98.48% with a SEARCH cost of 1.05 GPU days, outperforming existing ALGORITHMs in terms of accuracy, SEARCH efficiency, and architectural complexity.