The Experts below are selected from a list of 43818 Experts worldwide ranked by ideXlab platform

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.

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.

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.

L O Barbosa - One of the best experts on this subject based on the ideXlab platform.

  • an efficient parallel optimization algorithm for the token bucket control mechanism
    Computer Communications, 2006
    Co-Authors: N U Ahmed, X Lu, L O Barbosa
    Abstract:

    The Token Bucket algorithm, one of the most widely used control mechanism in Computer Communication Network, has been extensively used to ensure the QoS needs for various applications. Recently, [N.U. Ahmed, Bo Li, Luis Orozco-Barbosa, Modelling and optimization of Computer Network traffic controllers, Mathematical Problems in Engineering, 2005, 6(2005), 617-640] an optimization technique, based on dynamic programming and genetic algorithm, has been developed which improves Network utilization or throughput by reducing data losses and service time, etc. This, however, requires long execution time and excessive memory space thereby imposing limitation on its applicability to high dimensional problems. In this study we have conserved both space and time complexity. This is achieved by introducing multiple processors and the Reduced Memory Algorithm thereby opening up the prospects of solving large-scale problems. Our parallel processing algorithm is tested with MPEG-4 traces. Our numerical results show that the algorithm can effectively solve the multiple Token Bucket problems. The results also provide us with the guidelines to configure the parallel processing platform.

  • an efficient parallel optimization algorithm for the token bucket control mechanism
    Computer Communications, 2006
    Co-Authors: N U Ahmed, X Lu, L O Barbosa
    Abstract:

    The Token Bucket algorithm, one of the most widely used control mechanism in Computer Communication Network, has been extensively used to ensure the QoS needs for various applications. Recently, [N.U. Ahmed, Bo Li, Luis Orozco-Barbosa, Modelling and optimization of Computer Network traffic controllers, Mathematical Problems in Engineering, 2005, 6(2005), 617-640] an optimization technique, based on dynamic programming and genetic algorithm, has been developed which improves Network utilization or throughput by reducing data losses and service time, etc. This, however, requires long execution time and excessive memory space thereby imposing limitation on its applicability to high dimensional problems. In this study we have conserved both space and time complexity. This is achieved by introducing multiple processors and the Reduced Memory Algorithm thereby opening up the prospects of solving large-scale problems. Our parallel processing algorithm is tested with MPEG-4 traces. Our numerical results show that the algorithm can effectively solve the multiple Token Bucket problems. The results also provide us with the guidelines to configure the parallel processing platform.

N U Ahmed - One of the best experts on this subject based on the ideXlab platform.

  • an efficient parallel optimization algorithm for the token bucket control mechanism
    Computer Communications, 2006
    Co-Authors: N U Ahmed, X Lu, L O Barbosa
    Abstract:

    The Token Bucket algorithm, one of the most widely used control mechanism in Computer Communication Network, has been extensively used to ensure the QoS needs for various applications. Recently, [N.U. Ahmed, Bo Li, Luis Orozco-Barbosa, Modelling and optimization of Computer Network traffic controllers, Mathematical Problems in Engineering, 2005, 6(2005), 617-640] an optimization technique, based on dynamic programming and genetic algorithm, has been developed which improves Network utilization or throughput by reducing data losses and service time, etc. This, however, requires long execution time and excessive memory space thereby imposing limitation on its applicability to high dimensional problems. In this study we have conserved both space and time complexity. This is achieved by introducing multiple processors and the Reduced Memory Algorithm thereby opening up the prospects of solving large-scale problems. Our parallel processing algorithm is tested with MPEG-4 traces. Our numerical results show that the algorithm can effectively solve the multiple Token Bucket problems. The results also provide us with the guidelines to configure the parallel processing platform.

  • an efficient parallel optimization algorithm for the token bucket control mechanism
    Computer Communications, 2006
    Co-Authors: N U Ahmed, X Lu, L O Barbosa
    Abstract:

    The Token Bucket algorithm, one of the most widely used control mechanism in Computer Communication Network, has been extensively used to ensure the QoS needs for various applications. Recently, [N.U. Ahmed, Bo Li, Luis Orozco-Barbosa, Modelling and optimization of Computer Network traffic controllers, Mathematical Problems in Engineering, 2005, 6(2005), 617-640] an optimization technique, based on dynamic programming and genetic algorithm, has been developed which improves Network utilization or throughput by reducing data losses and service time, etc. This, however, requires long execution time and excessive memory space thereby imposing limitation on its applicability to high dimensional problems. In this study we have conserved both space and time complexity. This is achieved by introducing multiple processors and the Reduced Memory Algorithm thereby opening up the prospects of solving large-scale problems. Our parallel processing algorithm is tested with MPEG-4 traces. Our numerical results show that the algorithm can effectively solve the multiple Token Bucket problems. The results also provide us with the guidelines to configure the parallel processing platform.

  • real time feedback control of Computer Networks based on predicted state estimation
    Mathematical Problems in Engineering, 2005
    Co-Authors: N U Ahmed, Hui Song
    Abstract:

    We present a real-time feedback control strategy to optimize the dynamic performance of a Computer Communication Network. In previous studies closely related to this topic, feedback delay, arising from Communication delay, was shown to degrade system performance. Considering this negative impact of delay, we propose a new control law which predicts the traffic in advance and exercises control based on the predicted traffic. We demonstrate through simulation experiments that the predictive feedback control law substantially improves the system performance.