Mohammad Reza Meybodi

Amirkabir University of Technology | TUS · Department of Computer Engineering and Information Technology

A mixed dominating set in a graph G=(V,E)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G=(V,E)$$\end{document} is a subset D of vertices and edges of G such that ever...

Learning Automata (LAs) are adaptive decision-making models designed to find an appropriate action in unknown environments. LAs can be classified into two classes: variable structure and fixed structure. To the best of our knowledge, there is no hybrid model based on both of these classes. In this paper, we propose a model that brings together the...

A set $D \subseteq V$ of a graph $G = (V,E)$ is called an outer-connected dominating set of $G$ if every vertex $v$ not in $D$ is adjacent to at least one vertex in $D$, and the induced subgraph of $G$ on $V \setminus D$ is connected. The Minimum Outer-connected Domination problem is to find an outer-connected dominating set of minimum cardinality...

Given a positive integer k and a graph G = (V, E), a function f from V to the power set of Ik is called a k-rainbow function if for each vertex v ∈ V, f(v)=∅ implies ∪u ∈ N(v)f(u)=Ik where N(v) is the set of all neighbors of vertex v and Ik = {1, …, k}. Finding a k-rainbow function of minimum weight of ∑v ∈ V|f(v)|, which is called the k-rainbow do...

Dynamic optimization problems have emerged as an important field of research during the last two decades, since many real-world optimization problems are changing over time. These problems need fast and accurate algorithms, not only to locate the optimum in a limited amount of time but also track its trajectories as close as possible. Although lots...

Peer-to-peer networks are usually implemented without a central controller with the aim of providing some features such as flexibility, scalability and reliability.The use of peer-to-peer systems is often limited to unstructured peer-to-peer networks. Finding an object in such networks has led to various search methods. In the random walk method, a...

Peer-to-peer systems are well-known patterns of distributed systems which created several revelations in designing ultra-scalable systems. Recently, these types of systems are evolved into cognitive peer-to-peer networks. Because of the distributed nature of Internet of Things (IoT), peer-to-peer systems such as blockchain can be used to design man...

A fusion of learning automata and Petri nets, referred to as APN-LA, has been recently introduced in the literature for achieving adaptive Petri nets. A number of extensions to this adaptive Petri net have also been introduced; together we name them the APN-LA family. Members of this family can be utilized for solving problems in the domain of grap...

A set D V for the graph G = (V,E) is called a dominating set if any vertex v V \D has at least one neighbor in D. Fomin et al. [Combinatorial bounds via measure and conquer: Bounding minimal dominating sets and applications, ACM Transactions on Algorithms (TALG) 5(1) (2008) 9] gave an algorithm for enumerating all minimal dominating sets with n ver...

Given a graph $G = (V, E)$, a set $S \subseteq V \cup E$ of vertices and edges is called a mixed dominating set if every vertex and edge that is not included in $S$ happens to be adjacent or incident to a member of $S$. The mixed domination number $\gamma_{md}(G)$ of the graph is the size of the smallest mixed dominating set of $G$. We present an e...

In wireless mesh networks, random changes in the environment can increase the complexity of the multi-channel assignment. In this work, a new channel assignment scheme based on learning automata is proposed, which adaptively improves the network's overall performance by predicting network dynamics. First, we use a practical utility function that re...

We study relations between evidence theory and S-approximation spaces. Both theories have their roots in the analysis of Dempster's multivalued mappings and lower and upper probabilities and have close relations to rough sets. We show that an S-approximation space, satisfying a monotonicity condition, can induce a natural belief structure which is...

Osteoarthritis (OA) is a common chronic disorder among elderly people that affects joints such as the knee and hip in particular. The objective of the current study was to examine the efficacy of an intervention based on a theory of planned behavior (TPB) in improving health-related quality of life in middle-age and older adults with this condition...

A set $D \subseteq V$ for the graph $G=(V, E)$ is called a dominating set if any vertex $v\in V\setminus D$ has at least one neighbor in $D$. Fomin et al.[9] gave an algorithm for enumerating all minimal dominating sets with $n$ vertices in $O(1.7159^n)$ time. It is known that the number of minimal dominating sets for interval graphs and trees on $...

Congestion in wireless sensor networks degrades the quality of the channel and network throughput. This leads to packet loss and energy dissipation. To cope with this problem, a two-stage cognitive network congestion control approach is presented in this paper. In the first stage of the proposed strategy, initially downstream nodes calculate their...

Mobile peer-to-peer (MP2P) networks refer to the peer-to-peer overlay networks superimposing above the mobile ad-hoc networks. Heterogeneity of capacity and mobility of the peers as well as inherent limitation of resources along with the wireless networks characteristics are challenges on MP2P networks. In some MP2P networks, in order to improve ne...

Bayesian network (BN) is a probabilistic graphical model which describes the joint probability distribution over a set of random variables. Finding an optimal network structure based on an available training dataset is one of the most important challenges in the field of BNs. Since the problem of searching the optimal BN structure belongs to the cl...

Peer-to-peer network is organized on top of another network as an overlay network. Super peer network is one of the peer-to-peer networks. A super peer, in a super peer based network, is a peer that has more responsibility than other peers have and is responsible for some of the tasks of network management. Since different peers vary in terms of ca...

A dominating set in a graph $G$ is a subset of vertices $D$ such that every vertex in $V\setminus D$ is a neighbor of some vertex of $D$. The domination number of $G$ is the minimum size of a dominating set of $G$ and it is denoted by $\gamma(G)$. Also, a subset $D$ of a graph $G$ is a $[1,2] $-set if, each vertex $v \in V \setminus D$ is adjacent...

Adding cognition to existing Wireless Sensor Networks (WSNs) with a cognitive networking approach, which deals with using cognition to the entire network protocol stack to achieve end-to-end goals, brings about many benefits. However cognitive networking may be confused with cognitive radio or cross-layer design, it is a different concept; cognitiv...

An adaptive Petri net, called APN-LA, that has been recently introduced, uses a set of learning automata for controlling possible conflicts among the transitions in a Petri net (PN). Each learning automaton (LA) in APN-LA acts independently from the others, but there could be situations, where the operation of a LA affects the operation of another...

Since online social networks usually have quite huge size and limited access, smaller subgraphs of them are often produced and analysed as the representative samples of original graphs. Sampling algorithms proposed so far are categorized into three main classes: node sampling, edge sampling, and topology-based sampling. Classic node sampling algori...

Sybil attack is a well-known attack against wireless sensor networks (WSNs) in which a malicious node attempts to propagate multiple identities. This attack is able to affect routing protocols negatively as well as many other operations such as voting, data aggregation, resource allocation, misbehavior detection, etc. In this paper, a light weight,...

Social networks are usually modeled and represented as deterministic graphs with a set of nodes as users and edges as connection between users of networks. Due to the uncertain and dynamic nature of user behavior and human activities in social networks, their structural and behavioral parameters are time varying parameters and for this reason using...

Block Matching Algorithms (BMAs) are widely employed for motion estimation. BMAs divide input frames into several blocks and minimize an error function for each block to calculate motion vectors. Afterward, each motion vector is applicable for all of the pixels within the block. Since computing the error functions is resource intensive, many fast-s...

Incorporating trust and distrust information into collaborative recommender systems alleviates data sparsity and cold start problems. Since trust and distrust are a gradual phenomenon, they can be stated more naturally by fuzzy logic. Finding the most appropriate fuzzy sets which cover the domains of trust and distrust is not an easy task. Existing...

Congestion in wireless sensor networks causes packet loss, throughput reduction and low energy efficiency. To address this challenge, a transmission rate control method is presented in this article. The strategy calculates buffer occupancy ratio and estimates the congestion degree of the downstream node. Then, it sends this information to the curre...

Vertex coloring problem is a combinatorial optimization problem in graph theory in which a color is assigned to each vertex of graph such that no two adjacent vertices have the same color. In this paper a new hybrid algorithm which is obtained from combination of cellular learning automata (CLA) and memetic algorithm (MA) is proposed for solving th...

Existing literature on multicast routing protocols in wireless mesh networks (WMNs) from the view point of the links involved in routing are divided into two categories: schemes are aimed at multicast construction with minimal interference which is known as NP hard problem. In contrast, other methods develop network-coding-based solutions with the...

Many of the real-world networks, such as complex social networks, are intrinsically weighted networks, and therefore, traditional network models, such as binary network models, will result in losing much of the information contained in the edge weights of the networks and is not very realistic. In this paper, we propose that when the network model...

Bayesian Network (BN) is a probabilistic graphical model which describes the joint probability distribution over a set of random variables. One of the most important challenges in the field of BNs is to find an optimal network structure based on an available training dataset. Since the problem of searching the optimal BN structure belongs to the cl...

This paper presents two modifications of the cuckoo search (CS) algorithm for numerical optimization problems. The first modified algorithm is CS with composite flight (CSCF) which is aimed at improving the performance of the CS by introducing a novel composite flight operator in the standard CS. The main idea of composite flight operator is to...

Cognitive networking deals with using cognition to the entire network protocol stack to achieve stack-wide, as well as network-wide performance goals; unlike cognitive radios that apply cognition only at the physical layer to overcome the problem of spectrum scarcity. Adding cognition to the existing Wireless Sensor Networks (WSNs) with a cognitive...

In this work, we propose a class of public-key cryptosystems called multiplicative coupled cryptosystem, or MCC for short, as well as discuss its security within three different models. Moreover, we discuss a chaotic instance of MCC based on the first and the second types of Chebyshev polynomials over real numbers for these three security models. T...

Estimation of distribution algorithms are considered to be a new class of evolutionary algorithms which are applied as an alternative to genetic algorithms. Such algorithms sample the new generation from a probabilistic model of promising solutions. The search space of the optimization problem is improved by such probabilistic models. In the Bayesi...

Many problems in the modern world have a decentralized and distributed nature. Irregular cellular learning automata (ICLA) is a powerful mathematical model for decentralized problems and applications. Convergence of ICLA to a compatible point is very important because this convergence can provide efficient solutions for the problems. The local rule...

Bare bones PSO is a simple swarm optimization approach that uses a probability distribution like Gaussian distribution in the position update rules. However, due to its nature, Bare bones PSO is highly prone to premature convergence and stagnation. The characteristics of the probability distribution functions used in the update rule have a tense im...

Community structure is an important feature in complex networks which has great significant for organization of networks. The community detection is the process of partitioning the network into some communities in such a way that there exist many connections in the communities and few connections between them. In this paper a michigan memetic algor...

A cognitive network is a network which can learn to improve its performance while operating under its unknown and dynamic environment. Cognitive engine as part of a cognitive network tries to adaptively find an appropriate configuration for the network. Up until now no peer-to-peer network management algorithm has been designated utilizing cognitiv...

Many real-world optimization problems are dynamic in nature. The applied algorithms in this environment can pose serious challenges, especially when the search space is multimodal with multiple, time-varying optima. To address these challenges, this paper proposed a speciation-based firefly algorithm to maintain the population diversity in differen...

Peer-to-peer networks are overlay networks that are constructed over underlay networks. These networks can be structured or unstructured. In these networks, peers choose their neighbors without considering underlay positions, and therefore, the resultant overlay network may have a large number of mismatched paths. In a mismatched path, a message ma...

Link prediction is a social network research area that tries to predict future links using network structure. The main approaches in this area are based on predicting future links using network structure at a specific period, without considering the links behavior through different periods. For example, a common traditional approach in link predict...

An unstructured peer-to-peer network is an overlay network where all nodes play equal roles, and the topology and data location do not follow restrictive rules. So, in a traditional file search mechanism, such as flooding, a peer broadcasts a query to its neighbors through an unstructured peer-to-peer (P2P) network until the time-to-live decreases...

Maximum independent set problem is an NP-Hard one with the aim of finding the set of independent vertices with maximum possible cardinality in a graph. In this paper, we investigate a learning automaton based algorithm that finds a maximum independent set in the graph. Initially, a learning automaton is assigned to each vertex of graph. In order to...

Self-localization is the process of estimating the robot position exploiting noisy measurements. Since localization is a key issue for soccer playing robots, some probabilistic approaches have been developed over last years to address it. Methods based on Monte Carlo Localization (MCL) show good ability in dealing with kidnap problem, however, most...

In Many real-world optimization problems, optimization goals, the problem instances or some restrictions may change over time. One of the famous problems in the dynamic environments optimization is moving peaks benchmark function or the moving maximum that behavior is similar to dynamic problems in real-world. In this paper, Memetics algorithm base...

Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for comm...

Wireless link scheduling is one of the major challenging issues in multi-hop wireless networks when they need to be designed in distributed fashion. This paper improves the general randomized scheduling method by using learning automata based framework that allows throughput optimal scheduling algorithms to be developed in a distributed fashion. A...

An S-approximation space is a novel approach to study systems with uncertainty that are not expressible in terms of inclusion relations. In this work, we further examined these spaces, mostly from a topological point of view by a combinatorial approach. This work also identifies a subclass of these approximation spaces, called $S_\mathcal{MC}$-appr...

Structure learning is a very important problem in the field of Bayesian networks (BNs). It is also an active research area for more than 2 decades; therefore, many approaches have been proposed in order to find an optimal structure based on training samples. In this paper, a Particle Swarm Optimization (PSO)-based algorithm is proposed to solve the...

A set S subset of V of the graph G = (V, E) is called a [1, 2]-set of G if any vertex which is not in S has at least one but no more than two neighbors in S.A set S' subset of V is called a [1, 2]-total set of G if any vertex of G, no matter in S' or not, is adjacent to at least one but not more than two vertices in S'. In this paper we introduce a...

Due to dynamic and uncertain nature of many optimisation problems in real-world, the applied algorithm in this environment must be able to continuously track the changing optima over the time. In this paper, we report a novel speciation-based firefly algorithm for dynamic optimisation, which improved its performance by employing prior landscape his...

Due to dynamic and uncertain nature of many optimisation problems in real-world, the applied algorithm in this environment must be able to continuously track the changing optima over time. In this paper, we report a novel speciation-based firefly algorithm for dynamic optimisation, which improved its performance by employing prior landscape histori...

Recently, studying social networks plays a significant role in many applications of social network analysis, from the studying the characterization of network to that of financial applications. Due to the large data and privacy issues of social network services, there is only a limited local access to the whole network data in a reasonable amount o...

One of the major challenges in wireless sensor networks (WSNs) research is to prevent traffic congestion without compromising with the energy of the sensor nodes. Network congestion leads to packet loss, throughput impairment, and energy waste. To address this issue in this paper, a distributed traffic-aware routing scheme with a capacity of adjust...

An S-approximation space is a novel approach to study systems with uncertainty that are not expressible in terms of inclusion relations. In this work, we further examined these spaces, mostly from a topological point of view by a combinatorial approach. This work also identifies a subclass of these approximation spaces, called $S_\mathcal{MC}$-appr...

Structural and behavioral parameters of many real networks such as social networks are unpredictable, uncertain, and have time-varying parameters, and for these reasons, deterministic graphs for modeling such networks are too restrictive to solve most of the real-network problems. It seems that stochastic graphs, in which weights associated to the...

In recent years, the data used for analysis of social networks become very huge and restrictive so that it can be used an appropriate and small sampled network of original network for analysis goals. Sampling social network is referred to collect a small subgraph of original network with high property similarities between them. Due to important imp...

A proper semantic representation of textual information underlies many natural language processing tasks. In this paper, a novel semantic annotator is presented to generate conceptual features for text documents. A comprehensive conceptual network is automatically constructed with the aid of Wikipedia that has been represented as a Markov chain. Fu...

Due to the large scales and limitations in accessing most online social networks, it is hard or infeasible to directly access them in a reasonable amount of time for studying and analysis. Hence, network sampling has emerged as a suitable technique to study and analyze real networks. The main goal of sampling online social networks is constructing...

In this paper, an adaptive Petri net (PN), capable of adaptation to environmental changes, is introduced by the fusion of learning automata and PN. In this new model, called learning automata-based adaptive PN (APN-LA), learning automata are used to resolve the conflicts among the transitions. In the proposed APN-LA model, transitions are portioned...

Grid computing brings heterogeneity and decentralization to the world of science and technology. It leverages every bit of idle computing resources and provides a straightforward middleware for integrating cross-domain scientific devices and legacy systems. In a super big Grid, job scheduling is challenging specifically when it needs to have access...

Given an initially uncovered field, and as more and more directional sensors (sensors with sector shape sensing area) are continuously added to the sensor network, the size of partial covered areas increases. At some point, the situation abruptly changes from small fragmented covered areas to a single large covered area. This abrupt change is calle...

Due to dynamic and uncertain nature of many optimization problems in real-world, an algorithm for applying to this environment must be able to track the changing optima over the time continuously. In this paper, we report a novel multi-population particle swarm optimization, which improved its performance by employing an external memory. This algor...

Many engineering optimization problems do not standard mathematical techniques, and cannot be solved using exact algorithms. Evolutionary algorithms have been successfully used for solving such optimization problems. Differential evolution is a simple and efficient population-based evolutionary algorithm for global optimization, which has been appl...

In this paper, we propose a cellular edge detection (CED) algorithm which utilizes cellular automata (CA) and cellular learning automata (CLA). The CED algorithm is an adaptive, intelligent and learnable algorithm for edge detection of binary and grayscale images. Here, we introduce a new CA local rule with adaptive neighborhood type to produce the...

To cover a set of targets with known locations within an area with limited or prohibited ground access using a wireless sensor network, one approach is to deploy the sensors remotely, from an aircraft. In this approach, the lack of precise sensor placement is compensated by redundant de-ployment of sensor nodes. This redundancy can also be used for...

Artificial Bee Colony (ABC) is a metaheuristic algorithm with proper ability in solving optimization problems. However, its performance can be improved by setting a better balance between exploitation and exploration. In this study, by changing the search pattern of neighborhood and incorporating the information of a set of qualified solutions into...

In recent years, online social networks (OSN) have emerged as a platform of sharing variety of information about people, and their interests, activities, events and news from real worlds. Due to the large scale and access limitations (e.g., privacy policies) of online social network services such as Facebook and Twitter, it is difficult to access t...

The emergence of knowledge repositories in a variety of domains provides a valuable opportunity for semantic interpretation of high dimensional datasets. Previous researches investigate the use of concept instead of word as a core semantic feature for incorporating semantic knowledge from an ontology into the representation model of documents. On t...

So far various methods for optimization presented and one of most popular of them are optimization algorithms based on swarm intelligence and also one of most successful of them is Particle Swarm Optimization (PSO). Prior some efforts by applying fuzzy logic for improving defects of PSO such as trapping in local optimums and early convergence has b...

Peer-to-peer networks are overlay networks that are built on top of communication networks that are called underlay networks. In these networks, peers are unaware of the underlying networks, so the peers choose their neighbors without considering the underlay positions, and therefore, the resultant overlay network may have mismatches with its under...

This paper proposes a novel hybrid approach based on particle swarm optimization and local search, named PSOLS, for dynamic optimization problems. In the proposed approach, a swarm of particles with fuzzy social-only model is frequently applied to estimate the location of the peaks in the problem landscape. Upon convergence of the swarm to previous...

Active database systems (ADSs) react automatically to the occurrence of predefined events by defining a set of active rules. One of the main modules of an ADS is the rule scheduler, which has a significant impact on the effectiveness and efficiency of ADSs. During the rule scheduling process, the rule scheduler is responsible for choosing one of th...

One of the effective techniques for improving the rate of convergence in the particle swarm optimisation (PSO) is modifying the inertia weight parameter. This parameter can specify the search area of the swarm in the environment and establish a good balance between the global and local search ability of the particles. Several strategies have been a...

In this paper, we propose an adaptive call admission algorithm based on learning automata. The proposed algorithm uses a learning automaton to specify the acceptance/rejection of incoming new calls. It is shown that the given adaptive algorithm converges to an equilibrium point which is also optimal for uniform fractional channel policy. To study t...

Because of unpredictable, uncertain and time-varying nature of real networks it seems that stochastic graphs, in which weights associated to the edges are random variables, may be a better candidate as a graph model for real world networks. Once the graph model is chosen to be a stochastic graph, every feature of the graph such as path, clique, spa...

Combing a genetic algorithm (GA) with a local search method produces a type of evolutionary algorithm known as a memetic algorithm (MA). Combining a GA with a learning automaton (LA) produces an MA named GALA, where the LA provides the local search function. GALA represents chromosomes as object migration automata (OMAs), whose states represent the...

Optimization is amongst the most significant problems in mathematics and sciences and many researchers are investigating different aspects of this problem. In this paper, a novel algorithm has been proposed for optimization in continuous static environments based on the individual and social behaviors of fish in their swarms. The proposed algorithm...

Gravitational Search Algorithm (GSA) is a population-based optimization algorithm based on Newton's law of gravity and the notion of mass interactions. GSA has the advantage of proper global search ability. However, it suffers from weak local search due to relatively big step-size of agents in the search process. In order to improve the balance bet...

This paper presents a novel pricing method for maximizing the profit of a cloud provider. Mostly, there are three different instances (on-demand, reserved, and spot instances) in big cloud providers. Each instance has its own characteristics. A user may choose one of the instances regarding his requirements and instance types. In this paper, differ...

Artificial Bee Colony (ABC) algorithm is a swarm-based optimization algorithm with advantages like simplicity and proper exploration ability. However, it suffers from improper exploitation in solving complicated problems. In order to overcome this disadvantage, modifications on all three bee types are proposed. By introducing a new procedure for th...

Recently cloud computing has gained enormous attention in the industry with an increasing number of cloud service providers. Their tendency to cloud computing is of benefit for cloud users, as the increasing number of cloud providers results in a competitive market for attracting and satisfying new and current cloud users. In this paper, price and...

A new variant of Differential Evolution (DE), called ADE-Grid, is presented in this paper which adapts the mutation strategy, crossover rate (CR) and scale factor (F) during the run. In ADE-Grid, learning automata (LA), which are powerful decision making machines, are used to determine the proper value of the parameters CR and F, and the suitable s...