The Experts below are selected from a list of 32220 Experts worldwide ranked by ideXlab platform

Hakima Chaouchi - One of the best experts on this subject based on the ideXlab platform.

  • rfid Network Topology Design based on genetic algorithms
    International Conference on RFID, 2011
    Co-Authors: Oscar Botero, Hakima Chaouchi
    Abstract:

    Radio Frequency Identification (RFID) is a well known technology that has entered successfully in the realm of innumerable applications and is considered as one of the principal building blocks for the realization of the Internet of Things concept. However, the majority of RFID applications require the utilization of multiple RFID readers and therefore effective and efficient planning of their Networks is a major concern. Network planning is a complex process that involves different steps, one of which is its Topology Design. In this paper, we focus on the Topology Design process of a RFID Network following an optimization-based approach. More precisely, we propose a multi-objective cost function that is used to evaluate candidate solutions for the position and power levels of the set of RFID readers to be deployed. In order to obtain optimal solutions we applied Genetic Algorithms, a well known heuristic technique. Finally, a developed software tool to assist in the Topology Design is also presented and processing delay measurements are performed.

  • RFID-TA - RFID Network Topology Design based on Genetic Algorithms
    2011 IEEE International Conference on RFID-Technologies and Applications, 2011
    Co-Authors: Oscar Botero, Hakima Chaouchi
    Abstract:

    Radio Frequency Identification (RFID) is a well known technology that has entered successfully in the realm of innumerable applications and is considered as one of the principal building blocks for the realization of the Internet of Things concept. However, the majority of RFID applications require the utilization of multiple RFID readers and therefore effective and efficient planning of their Networks is a major concern. Network planning is a complex process that involves different steps, one of which is its Topology Design. In this paper, we focus on the Topology Design process of a RFID Network following an optimization-based approach. More precisely, we propose a multi-objective cost function that is used to evaluate candidate solutions for the position and power levels of the set of RFID readers to be deployed. In order to obtain optimal solutions we applied Genetic Algorithms, a well known heuristic technique. Finally, a developed software tool to assist in the Topology Design is also presented and processing delay measurements are performed.

Salman A Khan - One of the best experts on this subject based on the ideXlab platform.

  • A multi-objective evolutionary artificial bee colony algorithm for optimizing Network Topology Design
    Swarm and Evolutionary Computation, 2017
    Co-Authors: Amani Saad, Salman A Khan, Amjad Mahmood
    Abstract:

    The topological Design of a computer communication Network is a well-known NP-hard problem. The problem complexity is further magnified by the presence of multiple Design objectives and numerous Design constraints. This paper presents a goal programming-based multi-objective artificial bee colony optimization (MOABC) algorithm to solve the problem of topological Design of distributed local area Networks (DLANs). Five Design objectives are considered herein, namely, Network reliability, Network availability, average link utilization, monetary cost, and Network delay. Goal programming (GP) is incorporated to aggregate the multiple Design objectives into a single objective function. A modified version of MOABC, named as evolutionary multi-objective ABC (EMOABC) is also proposed which incorporates the characteristics of simulated evolution (SE) algorithm for improved local search. The effect of control parameters of MOABC is investigated. Comparison of EMOABC with MOABC and the standard ABC (SABC) shows better performance of EMOABC. Furthermore, a comparative analysis is also done with non-dominated sorting genetic algorithm II (NSGA-II), Pareto-dominance particle swarm optimization (PDPSO) algorithm and two recent variants of decomposition based multi-objective evolutionary algorithms, namely, MOEA/D-1 and MOEA/D-2. Results indicate that EMOABC demonstrated superior performance than all the other algorithms.

  • A fuzzy particle swarm optimization algorithm for computer communication Network Topology Design
    Applied Intelligence, 2012
    Co-Authors: Salman A Khan, Andries P Engelbrecht
    Abstract:

    Particle swarm optimization (PSO) is a powerful optimization technique that has been applied to solve a number of complex optimization problems. One such optimization problem is Topology Design of distributed local area Networks (DLANs). The problem is defined as a multi-objective optimization problem requiring simultaneous optimization of monetary cost, average Network delay, hop count between communicating nodes, and reliability under a set of constraints. This paper presents a multi-objective particle swarm optimization algorithm to efficiently solve the DLAN Topology Design problem. Fuzzy logic is incorporated in the PSO algorithm to handle the multi-objective nature of the problem. Specifically, a recently proposed fuzzy aggregation operator, namely the unified And-Or operator (Khan and Engelbrecht in Inf. Sci. 177: 2692–2711, 2007), is used to aggregate the objectives. The proposed fuzzy PSO (FPSO) algorithm is empirically evaluated through a preliminary sensitivity analysis of the PSO parameters. FPSO is also compared with fuzzy simulated annealing and fuzzy ant colony optimization algorithms. Results suggest that the fuzzy PSO is a suitable algorithm for solving the DLAN Topology Design problem.

  • An evolutionary algorithm for Network Topology Design
    IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222), 2001
    Co-Authors: Habib Youssef, Steven M. Sait, Salman A Khan
    Abstract:

    The Topology Design of campus Networks is a hard constrained combinatorial optimization problem, dictated by physical and technological constraints and must optimize several objectives. Furthermore, due to the non-deterministic nature of Network traffic and other Design parameters, the objective criteria are imprecise. Fuzzy logic provides a suitable mathematical framework in such a situation. We present an approach based on a simulated evolution algorithm for Design of a campus Network Topology. Three variations of the algorithm are presented and compared. Results show that the third variation, namely, simulated evolution with tabu search characteristics gives the best result.

  • EMO - Fuzzy Evolutionary Hybrid Metaheuristic for Network Topology Design
    Lecture Notes in Computer Science, 2001
    Co-Authors: Habib Youssef, Steven M. Sait, Salman A Khan
    Abstract:

    Topology Design of enterprise Networks is a hard combinatorial optimization problem. It has numerous constraints, several objectives, and a very noisy solution space. Besides the NP-hard nature of this problem, many of the performance metrics of the Network can only be estimated, given their dependence on many of the dynamic aspects of the Network, e.g., routing and number and type of traffic sources. Further, many of the desirable features of a Network Topology can best be expressed in linguistic terms, which is the basis of fuzzy logic. In this paper, we present a fuzzy evolutionary hybrid metaheuristic for Network Topology Design. This approach is dominance preserving and scales well with larger problem instances and a larger number of objective criteria. Experimental results are provided.

  • Application of swarm intelligence algorithms to multi-objective distributed local area Network Topology Design problem
    Swarm Intelligence - Volume 3: Applications, 1
    Co-Authors: Salman A Khan, Amjad Mahmood
    Abstract:

    Artificial bee colony (ABC) optimization, ant colony optimization (ACO), and particle swarm optimization (PSO) are well-known swarm intelligence algorithms. They have been widely used for solving many real-life optimization problems in various domains. This chapter presents how these algorithms can be used in optimizing the distributed local area Network Topology Design. The problem has been modelled as a constrained multi-objective optimization problem using goal programming. In addition to adapting the three algorithms for the problem, a hybrid ABC algorithm has also been proposed. Performance of the algorithms has been evaluated through a simulation study, and the results indicate that the hybrid ABC algorithm outperforms ACO, PSO and ABC algorithms.

Oscar Botero - One of the best experts on this subject based on the ideXlab platform.

  • rfid Network Topology Design based on genetic algorithms
    International Conference on RFID, 2011
    Co-Authors: Oscar Botero, Hakima Chaouchi
    Abstract:

    Radio Frequency Identification (RFID) is a well known technology that has entered successfully in the realm of innumerable applications and is considered as one of the principal building blocks for the realization of the Internet of Things concept. However, the majority of RFID applications require the utilization of multiple RFID readers and therefore effective and efficient planning of their Networks is a major concern. Network planning is a complex process that involves different steps, one of which is its Topology Design. In this paper, we focus on the Topology Design process of a RFID Network following an optimization-based approach. More precisely, we propose a multi-objective cost function that is used to evaluate candidate solutions for the position and power levels of the set of RFID readers to be deployed. In order to obtain optimal solutions we applied Genetic Algorithms, a well known heuristic technique. Finally, a developed software tool to assist in the Topology Design is also presented and processing delay measurements are performed.

  • RFID-TA - RFID Network Topology Design based on Genetic Algorithms
    2011 IEEE International Conference on RFID-Technologies and Applications, 2011
    Co-Authors: Oscar Botero, Hakima Chaouchi
    Abstract:

    Radio Frequency Identification (RFID) is a well known technology that has entered successfully in the realm of innumerable applications and is considered as one of the principal building blocks for the realization of the Internet of Things concept. However, the majority of RFID applications require the utilization of multiple RFID readers and therefore effective and efficient planning of their Networks is a major concern. Network planning is a complex process that involves different steps, one of which is its Topology Design. In this paper, we focus on the Topology Design process of a RFID Network following an optimization-based approach. More precisely, we propose a multi-objective cost function that is used to evaluate candidate solutions for the position and power levels of the set of RFID readers to be deployed. In order to obtain optimal solutions we applied Genetic Algorithms, a well known heuristic technique. Finally, a developed software tool to assist in the Topology Design is also presented and processing delay measurements are performed.

Amjad Mahmood - One of the best experts on this subject based on the ideXlab platform.

  • A multi-objective evolutionary artificial bee colony algorithm for optimizing Network Topology Design
    Swarm and Evolutionary Computation, 2017
    Co-Authors: Amani Saad, Salman A Khan, Amjad Mahmood
    Abstract:

    The topological Design of a computer communication Network is a well-known NP-hard problem. The problem complexity is further magnified by the presence of multiple Design objectives and numerous Design constraints. This paper presents a goal programming-based multi-objective artificial bee colony optimization (MOABC) algorithm to solve the problem of topological Design of distributed local area Networks (DLANs). Five Design objectives are considered herein, namely, Network reliability, Network availability, average link utilization, monetary cost, and Network delay. Goal programming (GP) is incorporated to aggregate the multiple Design objectives into a single objective function. A modified version of MOABC, named as evolutionary multi-objective ABC (EMOABC) is also proposed which incorporates the characteristics of simulated evolution (SE) algorithm for improved local search. The effect of control parameters of MOABC is investigated. Comparison of EMOABC with MOABC and the standard ABC (SABC) shows better performance of EMOABC. Furthermore, a comparative analysis is also done with non-dominated sorting genetic algorithm II (NSGA-II), Pareto-dominance particle swarm optimization (PDPSO) algorithm and two recent variants of decomposition based multi-objective evolutionary algorithms, namely, MOEA/D-1 and MOEA/D-2. Results indicate that EMOABC demonstrated superior performance than all the other algorithms.

  • Application of swarm intelligence algorithms to multi-objective distributed local area Network Topology Design problem
    Swarm Intelligence - Volume 3: Applications, 1
    Co-Authors: Salman A Khan, Amjad Mahmood
    Abstract:

    Artificial bee colony (ABC) optimization, ant colony optimization (ACO), and particle swarm optimization (PSO) are well-known swarm intelligence algorithms. They have been widely used for solving many real-life optimization problems in various domains. This chapter presents how these algorithms can be used in optimizing the distributed local area Network Topology Design. The problem has been modelled as a constrained multi-objective optimization problem using goal programming. In addition to adapting the three algorithms for the problem, a hybrid ABC algorithm has also been proposed. Performance of the algorithms has been evaluated through a simulation study, and the results indicate that the hybrid ABC algorithm outperforms ACO, PSO and ABC algorithms.

Andries P Engelbrecht - One of the best experts on this subject based on the ideXlab platform.

  • A fuzzy particle swarm optimization algorithm for computer communication Network Topology Design
    Applied Intelligence, 2012
    Co-Authors: Salman A Khan, Andries P Engelbrecht
    Abstract:

    Particle swarm optimization (PSO) is a powerful optimization technique that has been applied to solve a number of complex optimization problems. One such optimization problem is Topology Design of distributed local area Networks (DLANs). The problem is defined as a multi-objective optimization problem requiring simultaneous optimization of monetary cost, average Network delay, hop count between communicating nodes, and reliability under a set of constraints. This paper presents a multi-objective particle swarm optimization algorithm to efficiently solve the DLAN Topology Design problem. Fuzzy logic is incorporated in the PSO algorithm to handle the multi-objective nature of the problem. Specifically, a recently proposed fuzzy aggregation operator, namely the unified And-Or operator (Khan and Engelbrecht in Inf. Sci. 177: 2692–2711, 2007), is used to aggregate the objectives. The proposed fuzzy PSO (FPSO) algorithm is empirically evaluated through a preliminary sensitivity analysis of the PSO parameters. FPSO is also compared with fuzzy simulated annealing and fuzzy ant colony optimization algorithms. Results suggest that the fuzzy PSO is a suitable algorithm for solving the DLAN Topology Design problem.