ABSTRACT This paper introduces a new method for identifying the best planning option for Intermodal Freight Transportation Terminals (IFTTs) under uncertain conditions, utilizing Timed Colored Petri Nets (TCPNs) and cross‐efficiency Fuzzy Data Envelopment Analysis (DEA). The possible planning options for congestion are modeled with TCPN to obtain the behavior of the IFTT in its operational conditions. The performance indicators are calculated as fuzzy triples. Then, cross‐efficiency Fuzzy DEA is employed to compare and rate the planning options. The results show that this approach obtains the best planning option under uncertainty by considering fuzzy solutions to represent each performance indicator's possible range of values. The method allows IFTT decision‐makers to see the full range of possible outcomes compared to the deterministic method and make the best decisions.

Feature selection is a real-world problem that finds a minimal feature subset from an original feature set. A good feature selection method, in addition to selecting the most relevant features with less redundancy, can also reduce computational costs and increase classification performance. One of the feature selection approaches is using meta-heuristic algorithms. This work provides a summary of some meta-heuristic feature selection methods proposed from 2018 to 2022 that were designed and implemented on a wide range of different data for solving feature selection problem. Evaluation criteria, fitness functions and classifiers used and the time complexity of each method are also depicted. The results of the study showed that some meta-heuristic algorithms alone cannot perfectly solve the feature selection problem on all types of datasets with an acceptable speed. In other words, depending on dataset, a special meta-heuristic algorithm should be used. The results of this study and the identified research gaps can be used by researchers in this field.

Abstract The target coverage problem is considered as one of the major issues in directional sensor networks (DSNs), which is caused by the nature of these networks, including their limited angle of view. Due to the fault tolerance characteristic of some coverage applications, the target coverage is required to be performed using multiple sensors. This challenge is discussed in the literature under the title of k ‐coverage problem. Under certain conditions, the number of sensors may suffer some changes due to various factors such as power depletion of the sensors, sensors' malfunctioning, and harshness of the environment. This can result in unavailability of adequate sensors for providing k ‐coverage for all targets. The network suffering from such problem is referred to as under‐provisioned network. This paper was aimed at studying such networks by adopting the network conditions to the real environments. To solve this problem, the present paper proposes a hybrid model integrating the genetic algorithm (GA) and Tabu search (TS). The proposed algorithm generally aimed to identify a subset of sensors with appropriate working directions in order to provide a balanced coverage for all the targets available in the network. In order to evaluate the performance of the algorithm several experiments were conducted and the results have been compared with greedy and learning automat‐abased algorithms. . The results of the experiments show the superiority of the algorithm.

Abstract Today, ‘security challenge’ is considered a commonly‐used catchword when it comes to emerging technologies such as the internet of things (IoT). Turning a blind eye to this challenge can sometimes lead to irreparable human and financial damage in everyday life. Threat modelling, despite being time consuming, complex, and error‐prone, is still recognized as a preventative solution in the design phase. The method proposed in this paper, called automatic generation of threat paths, is suggested as an automated solution to the problem of identifying and determining potential threats. The proposed method is the improved and robust development of automated modelling and the introduction of the integration of new features (such as conditional probability and security) with the techniques of previous generations (such as Petri Nets (PNs)). The proposed method is evaluated using different security scenarios, and the results show that the proposed method outperforms other manual methods in this domain in terms of both time and cost (99.9% and 98.05%, respectively).

One of the important issues under discussion connected with traffic on the roads is improving transportation. In this regard, spatial information, including the shortest path, is of particular importance due to the reduction of economic and environmental costs. Here, the constrained shortest path (CSP) problem which has an important application in location-based online services is considered. The aim of this problem is to find a path with the lowest cost where the traversal time of the path does not exceed from a predetermined time bound. Since precise prediction of cost and time of the paths is not possible due to traffic and weather conditions, this paper discusses the CSP problems with fuzzy cost and fuzzy time. After formulating the CSP problem an efficient algorithm for finding the constrained optimal path is designed. The application of the proposed model is presented on a location-based online service called Snap.

Regardless of the performance of gravitational search algorithm (GSA), it is nearly incapable of avoiding local optima in high-dimension problems. To improve the accuracy of GSA, it is necessary to fine tune its parameters. This study introduces a gravitational search algorithm based on learning automata (GSA-LA) for optimisation of continuous problems. Gravitational constant G(t) is a significant parameter that is used to adjust the accuracy of the search. In this work, learning capability is utilised to select G(t) based on spontaneous reactions. To measure the performance of the introduced algorithm, numerical analysis is conducted on several well-designed test functions, and the results are compared with the original GSA and other evolutionary-based algorithms. Simulation results demonstrate that the learning automata-based gravitational search algorithm is more efficient in finding optimum solutions and outperforms the existing algorithms.

Abstract One of the fundamental challenges of the robotics field is robot's movement. That is, why route planning is an eminent issue of robotics research and it is used to enhance autonomy of moving robots in complex environments. The objective of route planning problem is to find the shortest route without collide from initiation point to destination point so that the amount of energy consumption by robot would not exceed a predefined amount. Because neither the amount of energy consumption nor the robot's passed distance index cannot be measured precisely due to environmental conditions, and fuzzy data is used for modeling the problem and the problem would be called “Robot Fuzzy Constrained shortest Route” problem. The main contributions of this study are fivefold: (i) The mathematical model of fuzzy constrained shortest route problem (FCSRP) is formulated; (ii) An elite artificial bees' colony (EABC) algorithm is used to solve the robot's FSCRP; (iii) The proposed EABC algorithm is simulated with two fuzzy networks; (iv) The performance of the proposed approach is compared with the performance of genetic algorithm and particle swarm optimization algorithm; and (v) The results show the convergence speed of the EABC algorithm is higher than the existing algorithms.

Software rejuvenation is an effective technique to counteract software ageing in continuously‐running applications such as web‐service‐based systems. In a client‐server application, where the server is intended to run perpetually, rejuvenation of the server process periodically during the server idle times increases the availability of that service. In these systems, web services are allocated based on the receiver's requirements and server's facilities. Since the selection of a server among candidates while maintaining the optimal quality of service is an NP‐hard problem, meta‐heuristics seems to be suitable. In this study, the proposed dynamic software rejuvenation as a proactive fault‐tolerance technique based on a combination of ant colony optimisation (ACO) and gravitational emulation local search (GELS) so as to determine the optimal times when rejuvenation can be performed and failure rate can be minimised. The newly proposed method combined the public search capabilities of ACO with local search of GELS algorithm in an effort to create a stable algorithm, which can make reaching the global optimum largely possible in the proposed work. The simulation results revealed that the proposed strategy can decrease the failure rate of web services averagely by 28% in comparison with genetic algorithm and decision‐tree strategies.

User stories play an important role in agile development systems. In this study, a method of summarising user stories is proposed to reuse them in the future. To enhance the results, quality improvement should be made on user stories. It would help developers build better results, and it may also lead to omitting some essential information. To avoid such issues, user stories are duplicated in two exact similar groups, and quality improvement is made on one set while the other set remains unattained. With the help of a modified bag of words and a verb parser, a collection of keywords and key verbs are extracted for both groups. Afterwards, automatic user stories are made, and then an expert improves them. Next, some experts choose between the results and select the better ones. The result is evaluated by applying different experiments on the framework and prototype implementation on 14 data sets of a user story from industry and a fake data set from Duke University. The result showed 97% of micro F-measure and 93% of macro F-measure, which are promising. These new user stories can be used as the base user stories in future similar projects.

A number of software programms are developed in several releases. Before developing any new release, a set of requirements is suggested for inclusion in the release. Having multiple constraints, it is impossible to develop all the requirements proposed in the next release. The presence of competing companies, replication of product ideas, shortening of the development time and lack of project funding will reduce the cost of developing a release. Developer teams should select a subset of the proposed requirements for development that would provide their clients with the highest amount of satisfaction despite the deadline limitations or cost constraints. The existence of conflicting goals and other constraints makes this choice very complicated. In this paper, an algorithm is introduced which is based on a fuzzy inference system to determine the suitability of each requirement for development in the next release. The proposed algorithm, rather than the developer team, takes the responsibility to select the optimal subset of requirements for the development of the next release. Experimental results of the proposed algorithm are then compared with the results of the genetic algorithm. The subset selected by the proposed algorithm provides much more satisfaction than the genetic algorithm.

An uncertain Business Process Management System (BPMS) capability is Business Processes (BPs) management in the presence of uncertain factors. This ability should be defined by different uncertain computer-based components inside the classic BPMSs operations. This study proposed autonomous and combinatorial optimal process management architecture to increase the ability, flexibility, and accuracy of uncertain processes management. The autonomous architecture based on the bi-level optimization approach has been constructed inward a meta-model of multi-agent system technology, optimal Neural Network and Cellular Learning Automata in different agents. A case study of an uncertain business process evolving the closed loop supply chain was studied. The results of the simulated case and the statistical evaluation of it, have been demonstrated the robustness and accuracy of this new proposed architecture.