Ali Asghar Rahmani Hosseinabadi | Islamic Azad University, Science and Research Branch (original) (raw)

Papers by Ali Asghar Rahmani Hosseinabadi

The Journal of Supercomputing, 2017

In wireless sensor networks (WSNs), nodes have limited energy and cannot be recharged. In order t... more In wireless sensor networks (WSNs), nodes have limited energy and cannot be recharged. In order to tackle this problem, clustering methods are employed to optimize energy consumption, gather data and also enhance the effective lifetime of the network. In spite of the clustering methods advantages, there are still some important challenges such as choosing a sensor as a cluster head (CH), which has a significant effect in energy efficiency. In clustering phase, nodes are divided into some clusters and then some nodes, named CH, are selected to be the head of each cluster. In typical clustered WSNs, nodes sense the field and send the sensed data to the CH, then, after gathering and aggregating data, CH transmits them to the Base Station. Node clustering in WSNs has many advantages, such as scalability, energy efficiency, and reducing routing delay. In this paper, several clustering methods are studied to 123 278 A. S. Rostami et al. demonstrate advantages and disadvantages of them. Among them, some methods deal with homogenous network, whereas some deals with heterogeneous. In this paper, homogenous and heterogeneous methods of clustering are specifically investigated and compared to each other.

Computers and Electrical Engineering, 2017

Wireless Sensor Networks (WSNs) are used for environmental monitoring. In recent years, energy co... more Wireless Sensor Networks (WSNs) are used for environmental monitoring. In recent years, energy constraints have led us to develop sensor nodes that harvest energy from the environment. WSNs improve performances by using techniques such as routing and clustering, by harvesting energy from the environment. These networks are called Energy Harvesting Wireless Sensor Network (EH-WSN). Due to the unique features of EH-WSNs, typical WSN clustering and routing methods are inefficient for EH-WSN. In this paper, we propose a novel hybrid methodology involving static and dynamic clustering operations. It uses a distributed-centralized approach and multi-hop routing and considers criteria, such as the energy level, the amount of harvested energy and the number of neighbors in the clustering process. Simulation results show that the proposed method improves the network stability and efficiency, comparing to other methods.

Applied Mathematical Modelling

Capacitated Vehicle Routing Problem (CVRP) is one of the most famous specialized forms of the VRP... more Capacitated Vehicle Routing Problem (CVRP) is one of the most famous specialized forms of the VRP, which has attracted considerable attention from scientists and researchers. Therefore, many accurate, heuristic, and meta-heuristic methods have been introduced to solve this problem in recent decades. In this paper, a new meta-heuristic optimization
algorithm is introduced to solve the CVRP, which is based on the law of gravity and group interactions. The proposed algorithm uses two of the four basic parameters of velocity and gravitational force in physics based on the concepts of random search and searching agents,
which are a collection of masses that interact with each other based on Newtonian gravity and the laws of motion. The introduced method was quantitatively compared with the Stateof-the-Art algorithms in terms of execution time and number of optimal solutions achieved in four well-known benchmark problems. Our experiments illustrated that the proposed method could be a very efficient method in solving CVRP and the results are comparable with the results using state-of-the-art computational methods. Moreover, in some cases our method could produce solutions with less number of required vehicles compared to the Best Known Solution (BKS) in a very efficient manner, which is another advantage of the proposed algorithm.

Neural Computing and Applications

In open vehicle routing problem (OVRP), after delivering service to the last customer, the vehicl... more In open vehicle routing problem (OVRP), after delivering service to the last customer, the vehicle does not necessarily return to the initial depot. This type of problem originally defined about thirty years ago and still is an open issue. In real life, the OVRP is similar to the delivering newspapers and consignments. The problem of service delivering to a set of customers is a particular open VRP with an identical fleet for transporting vehicles that do not necessarily return to the initial depot. Contractors which are not the employee of the delivery company use their own vehicles and do not return to the depot. Solving the OVRP means to optimize the number of vehicles, the traveling distance and the traveling time of a vehicle. In time, several algorithms such as tabu search, deterministic annealing and neighborhood search were used for solving the OVRP. In this paper, a new combinatorial algorithm named OVRP_GELS based on gravitational emulation local search algorithm for solving the OVRP is proposed. We also used record-to-record algorithm to improve the results of the GELS. Several numerical experiments show a good performance of the proposed method for solving the OVRP when compared with existing techniques. Keywords Open vehicle routing problem Á Gravitational emulation local search algorithm (GELS) Á Optimization Á Velocity Á Newton's law Á Record-to-record algorithm

Traditional OpenVehicle Routing Problem (OVRP) methods take account to definite responding to the... more Traditional OpenVehicle Routing Problem (OVRP) methods take account to definite responding to the all requests of customers whiles the main goal of proposed approach in OVRP is decreasing the vehicle numbers time and path traveled
by vehicles. Therefore, in the present paper, a new optimization algorithm based on Gravity law and mass interactions is introduced to solve the problem. This algorithm being proposed based on random search concepts utilizes two of the four major parameters in physics including speed and Gravity and its researcher agents are a set of masses which are in connection with each other based on Newton’s Gravity and
motion laws. The proposed approach is compared with various algorithms and results approve its high effectiveness in solving the above problem.

In Cloud computing environments, computing resources are available for users, and they only pay f... more In Cloud computing environments, computing resources are available for users, and they only pay for used resources The most important issues in cloud computing are scheduling and energy consumption which many researchers worked
on them. In these systems a scheduling mechanism has two phases: task prioritization and processor selection. Different priorities may cause to different makespan and for each processor which assigned to the task, the energy consumption is different. So a good scheduling algorithm must assign priority to each task and select the best processor for them, in such a way that makespan and energy consumption
be minimized. In this paper, we proposed a two phase’s algorithm for scheduling, named TETS, the first phase is task prioritization and the second phase is processor assignment. We use three prioritization methods for prioritize the tasks
and produce optimized initial chromosomes and assign the tasks to processors which is an energy-aware model. Simulation results indicate that our algorithm is better than previous algorithms in terms of energy consumption and makespan. It can improve the energy consumption by 20 % and makespan by 4 %.

A wireless sensor network consists hundreds or thousands of sensors with limited computing power ... more A wireless sensor network consists hundreds or thousands of sensors with limited computing power and memory, which give the information from the environment and then analyze and process the data and also send the sensed data to other nodes or basic stations. In these networks, sensing nodes have with a limited battery to provide the energy. Since in these networks, energy is considered as a challenging problem, we decided to propose a new algorithm based on the gravitational search algorithm to prolong the network lifetime and achieve maximum coverage of target area. Performance of the proposed algorithm is evaluated through simulations and compared to GA algorithm. Experimental results show that the proposed algorithm has more appropriate sensor selection to compared algorithm. In fact, total coverage increased by 2 percentage and we have 5 percentages more alive sensors in network when reached to coverage threshold.

The scheduling problem of N independent works on M machines in the environment of permutation wor... more The scheduling problem of N independent works on M machines in the environment of permutation workflow workshop along with processing time and the desired delivery date are types of static problems, and except the machinery limitation as resources, no other limitation governs on it. Due to the importance of on time completion of work and minimizing delivery time of work, checking them is necessary in real word situations and will be properly effective on orders expedition and customer satisfaction. In this paper, one purpose of minimizing the sum of delays and on times (ΣE/T), is factor of recourses in advance reservation. The other main purpose is to present a new method called TIME_GSA using the Gravitational Search Algorithm (GSA) in order to solve the scheduling problem of works in workflow workshop environment, based on minimizing criteria of the sum of works delays and on times for resources in advance reservation.
In this article, a novel optimization algorithm in the basis of gravitation law and mass interactions is presented to resolve
the above problem. The suggested algorithm utilizes two of four basic factors connected to velocity and gravitational force in physics, based on random search concepts. The searcher agents are a group of masses which interact with each other based on the Newtonian gravitation and the laws of motion. The suggested procedure is compared with GA algorithm; the results approve the excellent performance of the suggested procedure to resolve the above problem.

Open vehicle routing problem (OVRP) is one of the most important problems in vehicle routing, whi... more Open vehicle routing problem (OVRP) is one of the most important problems in vehicle routing, which has attracted great interest in several recent applications in industries. The purpose in solving the OVRP is to decrease the number of vehicles and to reduce travel distance and time of the vehicles. In this article, a new meta-heuristic algorithm called OVRP_ICA is presented for the above-mentioned problem. This is a kind of combinatorial optimization problem that can use a homogeneous fleet of vehicles that do not necessarily return to the initial depot to solve the problem of offering services to a set of customers exploiting the imperialist competitive algorithm. OVRP_ICA is compared with some well-known state-of-the-art algorithms and the results confirmed that it has
high efficiency in solving the above-mentioned problem.

The problem of open-shop scheduling includes a set of activities which must be performed on a lim... more The problem of open-shop scheduling includes a set of activities which must be performed on a limited set of machines. The goal of scheduling in open-shop is the
presentation of a scheduled program for performance of the whole operation, so that the ending performance time of all job operations will be minimised. The open-shop scheduling
problem can be solved in polynomial time when all nonzero processing times are equal, becoming equivalent to edge coloring that has the jobs andworkstations as its vertices and that has an edge for every job-workstation pair with a nonzero processing time. For three or more workstations, or three or more jobs, with varying processing times, open-shop scheduling is NP-hard. Different algorithms have been presented for open-shop scheduling so far.However,
most of these algorithms have not considered the machine maintenance problem. Whilst in production level, each machine needs maintenance, and this directly influences the assurance reliability of the system. In this paper, a new genetic-based algorithm to solve the open-shop
scheduling problem, namely OSGA, is developed. OSGA considers machine maintenance.To confirm the performance of OSGA, it is compared with DGA, SAGA and TSGA algorithms.
It is observed that OSGA performs quite well in terms of solution quality and efficiency in small and medium enterprises (SMEs). The results support the efficiency of the proposed
method for solving the open-shop scheduling problem, particularly considering machine maintenance especially in SMEs’.

ADhoc and sensor networks may have a large number of nodes deployed over wide areas, and nodes ty... more ADhoc and sensor networks may have a large number of nodes deployed over wide areas, and nodes typically have limited battery and computational capabilities. They are often employed in environmental monitoring applications, which require their topology to be either fixed or slowly varying in a controllable manner, and their operational lifetime is of the order of weeks or months. However, this technology faces many challenges, one of the most challenging aspects Of WSNs is the energy constraint in the sensor nodes. The energy required by the sensor nodes is often provided by an exhaustible source of energy (e.g., batteries), which necessitates the usage of methods that can reduce the energy consumption in the network. In addition to electronic-based methods for adjusting the energy consumption of the sensors (e.g., better IC designs), many works have proposed better architecture designs to overcome this limit. Multi-hop packet transmission, in which the packet to be transmitted from a sensor is relayed by the nodes located at a shorter distance from the sink, is one of these ideas. Indeed, the multi-hop scheme is claimed to be more energy efficient compared to its equivalent single-hop network, since less transmission power is required for shorter distances. Despite many efforts toimprove the energy efficiency ofmulti-hop-based WSNs[1–3], there is still uncertainty about its efficiency when more realistic models are taken into account.

The Multiple Traveling Salesmen Problem (mTSP) is of the famous and classical problems of researc... more The Multiple Traveling Salesmen Problem (mTSP)
is of the famous and classical problems of research in
operations and is accounted as one of the most famous and
widely used problems of combinational optimization. Most of
the complex problems can be modeled as the mTSP and then
be solved. The mTSP is a NP-Complete one; therefore, it is not
possible to use the exact algorithms for solving it instead the
heuristics methods are often applied for solving such problems.
In this paper, a new hybrid algorithm, called GELS-GA, has
been presented for solving the mTSP. The utility of GELS-GA
is compared with some related works such as GA and ACO
and achieves optimality even in highly complex scenarios.
Although, the proposed algorithm is simple, it includes an
appropriate time of completion and the least traversed distance
among existing algorithms.

Vehicle routing is considered the basic issue in distribution management. In real-world problems,... more Vehicle routing is considered the basic issue in
distribution management. In real-world problems, customer
demand for some commodities increases on special situations.
On the one hand, one of the factors that are very important for
customers is the timely delivery of the demanded commodities.
In this research, customers had several different kinds of
demands. Therefore, a new routing model was introduced in
the form of integer linear programming by combining the
concepts of time windows and multiple demands and by
considering the two contradictory goals of minimizing travel
cost and maximizing demand coverage. Moreover, two
approaches were designed for the problem-solving model
based on the NSGA-II algorithm with diversification of the
mutation operator structure. The two criteria of spread and
coverage of non-dominated solutions were used to compare
algorithms. Study of some typical created problems indicated
the validity of the model and the computational efficiency of
the proposed algorithm. The proposed algorithm could
increase the criterion of solution spread by about 10%, and
increased the number of obtained solutions on the Pareto
border compared to other algorithms, which indicated its high
efficiency.

Scheduling problems are naturally dynamic. Increasing flexibility will help solve bottleneck issu... more Scheduling problems are naturally dynamic. Increasing flexibility will help solve bottleneck issues, increase production, and improve performance and competitive advantage
of Small Medium Enterprises (SMEs). Maximum make span, as well as the average workflow time and latency time of parts are considered the objectives of scheduling, which are
compatible with the philosophy of on-time production and supply chain management goals. In this study, these objectives were selected to optimize the resource utilization, minimize
inventory turnover, and improve commitment to customers; simultaneously controlling these objectives improved system performance. In the job-shop scheduling problem considered in
this paper, the three objectives were to find the best total weight of the objectives, maximize the number of reserved jobs and improve job-shop performance. To realize these targets, a multi-parametric objective function was introduced with dynamic and flexible parameters. The other key accomplishment is the development of a new method called TIME_GELS that uses the gravitational emulation local search algorithm (GELS) for solving the multiobjective flexible dynamic job-shop scheduling problem. The proposed algorithm used two of the four parameters, namely velocity and gravity. The searching agents in this algorithm are a set of masses that interact with each other based on Newton’s laws of gravity and
motion. The results of the proposed method are presented for slight, mediocre and complete flexibility stages. These provided average improvements of 6.61, 6.5 and 6.54%. The results
supported the efficiency of the proposed method for solving the job-shop scheduling problem particularly in improving SME’s productivity.

Multiple Travelling Salesman Problem (mTSP) is one of the most popular and widely used combinator... more Multiple Travelling Salesman Problem (mTSP) is one of the most popular and widely used combinatorial optimization
problems in the operational research. Many complex problems can be modeled and solved by the mTSP. To solve the mTSP,
deterministic algorithms cannot be used as the mTSP is an NP-hard optimization problem. Hence, heuristics approaches are usually applied. In this paper, the Gravitational Emulation Local Search (GELS) algorithm is modified to solve the symmetric mTSP. The GELS algorithm is based on the local search concept and uses two main parameters in physics, velocity and gravity. Performance of the modified GELS has been compared with well-known optimization algorithms such as the genetic algorithm (GA) and ant colony optimization (ACO). Simulation results show superiority of the modified GELS over the other common optimization algorithms.

Traveling Salesman Problem (TSP) is a famous and classic operation for combination of optimizatio... more Traveling Salesman Problem (TSP) is a famous and classic operation for combination of optimization problems which is very used. Many complex issues can be modeled as traveling salesman problems. Since TSP is a NP-complete problem, certain algorithms cannot be used for solving it. Hence heuristic methods are common to resolve these issues. This paper presents a new algorithm called TSP-GSA for solving the traveling salesman problem by means of Gravitational Search algorithm or GSA. This algorithm has used 2 parameters out of 4 main parameters of velocity and gravitational force in physics based on random search concepts. The proposed algorithm has been compared with the genetic algorithm [1] and experimental results showed that not only proposed algorithm has better performance but also it takes less time to be solved.

One of the most important problems of timing in engineering and industry is timing of open shop. ... more One of the most important problems of timing in engineering and industry is timing of open shop. The problem of timing of the open shop induces big and complicated solve space. So, this problem is a kind of NP-Complete. In timing of the open shop, there some works, that each work has several operation. Each operation should do in machine whit should do in the same machine the aim of timing of the open shop is to catch a suitable timing for doing all of the operation, how that enough time to minimize to make-span. In problem solve of timing of the open shop.

Hidden Markov Model is a finite series of states that is continues with a probability distributio... more Hidden Markov Model is a finite series of states that is continues with a probability distribution in a special state, an output can be obtained by continuous probability distribution. Since states are hidden from outside, this model is called Hidden Markov Model. In ordinary Markov Model, the position is directly visible to observer so probabilities transference state will be the only parameters. In Hidden Markov Model, the position is not visible directly but the affected variants by the position are visible. Each state taken for a possible output will have a probability distribution. Therefore, the sequence of taken states created by HMM would provide some information about the sequence state. Hidden Markov Models will be distinguished for their instruction in identifying the temporary patterns such as speech, handwriting, identifying hint and pointing, bioinformatics and so on. In this paper, a new method based on Modified Gravitational Search Algorithm (MGSA) has been used to improve the teaching of Hidden Markov Model (HMM). The teaching of HMM is based on Baum-Welch algorithm (BW). One of the problems of HMM teaching is the absence of any assurance about reaching of this algorithm to global optimum and the convergence of this method is often towards a local optimum. In this paper, the Modified Gravitational Search Algorithm has been used to exit Baum-Welch from local optimum and search for other optimal points. Furthermore, we have compared the proposed algorithm with two algorithms, PSO and Ant Colony, which have been used finally in Speech Recognition.

Clustering is a very powerful data mining technique for topic discovery from documents. In docume... more Clustering is a very powerful data mining technique for topic discovery from documents. In document clustering, it must be more similarity between intra-document and less similarity between intra-document of two clusters. The cosine function measures the similarity of two documents. when the clusters are not well separated, partitioning them just based on the pair wise is not good enough because some documents in different clusters may be similar to each other and the function is not efficient. To solve this problem, a measurement of the similarity in concept of neighbors and links is used. In this paper, an efficient method for measurement of the similarity with a more accurate weighting in bisecting k_means algorithms is proposed. Having evaluated by the data set of documents, the efficiency was compared with the cosine similarity criterion and traditional methods. Experimental results show an outstanding improvement in efficiency by applying the proposed criterion.

The Journal of Supercomputing, 2017

Computers and Electrical Engineering, 2017

Applied Mathematical Modelling

Neural Computing and Applications