The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform

A.s. Pomportsis - One of the best experts on this subject based on the ideXlab platform.

  • A neural approach to adaptive MAC protocols for wireless LANs
    2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577), 2004
    Co-Authors: P. Nicopolitidis, G.i. Papadimitrou, A.s. Pomportsis
    Abstract:

    An adaptive MAC protocol for distributed wireless LANs, capable of operating efficiently under bursty traffic conditions, is introduced. According to the proposed protocol, the mobile station that is granted permission to transmit is selected by means of a neural-based algorithm. The neural-based algorithm takes into account the network feedback information in order to update the Choice Probability of each mobile station. The proposed protocol is compared via simulation to TDMA and IEEE 802.11 and is shown to exhibit superior performance under bursty traffic conditions even when the network feedback is noisy.

  • On carrier-sense integration in learning automata-based MAC protocols for ad-hoc wireless LANs
    IEEE International Conference on Performance Computing and Communications 2004, 2004
    Co-Authors: P. Nicopolitidis, G.i. Papadimitriou, A.s. Pomportsis
    Abstract:

    A carrier-sense-assisted learning automata-based MAC protocol for wireless LANs, capable of operating efficiently under bursty traffic and unreliable channel feedback, is introduced. According to the proposed protocol, the mobile station that is granted permission to transmit is selected by means of learning automata. At each station, the learning automaton takes into account the network feedback information in order to update the Choice Probability of each mobile station. The proposed protocol utilizes carrier sensing in order to reduce the collisions that are caused by different decisions at the various mobile stations due to the unreliable channel feedback.

  • A new class of /spl epsi/-optimal learning automata
    IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2004
    Co-Authors: G.i. Papadimitriou, M. Sklira, A.s. Pomportsis
    Abstract:

    A new class of P-model absorbing learning automata is introduced. The proposed automata are based on the use of a stochastic estimator in order to achieve a rapid and accurate convergence when operating in stationary random environments. According to the proposed stochastic estimator scheme, the estimates of the reward probabilities of actions are not strictly dependent on the environmental responses. The dependence between the stochastic estimates and the deterministic ones is more relaxed for actions that have been selected only a few times. In this way, actions that have been selected only a few times, have the opportunity to be estimated as "optimal," to increase their Choice Probability and consequently, to be selected. In this way, the estimates become more reliable and consequently, the automaton rapidly and accurately converges to the optimal action. The asymptotic behavior of the proposed scheme is analyzed and it is proved to be /spl epsi/-optimal in every stationary random environment. Furthermore, extensive simulation results are presented that indicate that the proposed stochastic estimator scheme converges faster than the deterministic-estimator-based DP/sub RI/ and DGPA schemes when operating in stationary P-model random environments.

  • An adaptive MAC protocol for ad-hoc wireless LANs
    2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484), 2003
    Co-Authors: P. Nicopolitidis, G.i. Papadimitriou, A.s. Pomportsis
    Abstract:

    An ad-hoc learning automata-based protocol for wireless LANs, capable of operating efficiently under bursty traffic conditions, is introduced. According to the proposed protocol, the mobile station that is granted permission to transmit is selected by means of learning automata. The learning automaton takes into account the network feedback information in order to update the Choice Probability of each mobile station. The proposed protocol is compared via simulation to TDMA under bursty traffic conditions and is shown to exhibit superior performance even when the network feedback is noisy.

  • On the use of population-based incremental learning in the medium access control of broadcast communication systems
    10th IEEE International Conference on Electronics Circuits and Systems 2003. ICECS 2003. Proceedings of the 2003, 2003
    Co-Authors: G.i. Papadimitriou, A.s. Pomportsis
    Abstract:

    The Time Division Multiple Access protocol suffers from poor performance when the offered traffic is bursty. In this paper, an adaptive Time Division Multiple Access protocol, which is capable of operating efficiently under bursty traffic conditions, is introduced. According to the proposed protocol, the station which is granted permission to transmit at each time slot is selected by means of a variation of the population-based incremental learning (PBIL) algorithm. The Choice Probability of the selected station is updated by taking into account the network feedback information. In this way, the proposed protocol is always capable of being adapted to the sharp changes of the station's traffic.

Rogério M. Gomes - One of the best experts on this subject based on the ideXlab platform.

  • An adaptive genetic algorithm to solve the Single Machine Scheduling Problem with Earliness and Tardiness Penalties
    IEEE Congress on Evolutionary Computation, 2010
    Co-Authors: Fabio Fernandes Ribeiro, Sergio Ricardo De Souza, Marcone Jamilson Freitas Souza, Rogério M. Gomes
    Abstract:

    This paper deals with the Single Machine Scheduling Problem with Earliness and Tardiness Penalties, considering distinct due windows and sequence-dependent setup time. Due to its complexity, an adaptive genetic algorithm is proposed for solving it. Five search operators are used to explore the solution space and the Choice Probability for each operator depends on the success in a previous search. The initial population is generated by the combination between construct methods based on greedy, random and GRASP techniques. For each job sequence generated, a polynomial time algorithm are used for determining the processing initial optimal date to each job. During the evolutive process, a group with the best five individuals generated by each crossover operator is built. Then, periodically, a Path Relinking module is applied taking as base individual the best one so far generated by the algorithm and as guide individual each one of the five best individuals generated by each crossover operator. Three variations of this algorithm were submitted to computational experiments. The results shows the effectiveness of the proposal.

Fabio Fernandes Ribeiro - One of the best experts on this subject based on the ideXlab platform.

  • An adaptive genetic algorithm to solve the Single Machine Scheduling Problem with Earliness and Tardiness Penalties
    IEEE Congress on Evolutionary Computation, 2010
    Co-Authors: Fabio Fernandes Ribeiro, Sergio Ricardo De Souza, Marcone Jamilson Freitas Souza, Rogério M. Gomes
    Abstract:

    This paper deals with the Single Machine Scheduling Problem with Earliness and Tardiness Penalties, considering distinct due windows and sequence-dependent setup time. Due to its complexity, an adaptive genetic algorithm is proposed for solving it. Five search operators are used to explore the solution space and the Choice Probability for each operator depends on the success in a previous search. The initial population is generated by the combination between construct methods based on greedy, random and GRASP techniques. For each job sequence generated, a polynomial time algorithm are used for determining the processing initial optimal date to each job. During the evolutive process, a group with the best five individuals generated by each crossover operator is built. Then, periodically, a Path Relinking module is applied taking as base individual the best one so far generated by the algorithm and as guide individual each one of the five best individuals generated by each crossover operator. Three variations of this algorithm were submitted to computational experiments. The results shows the effectiveness of the proposal.

  • An adaptive genetic algorithm for solving the single machine scheduling problem with earliness and tardiness penalties
    2009 IEEE International Conference on Systems Man and Cybernetics, 2009
    Co-Authors: Fabio Fernandes Ribeiro, Sergio Ricardo De Souza, Marcone Jamilson Freitas Souza
    Abstract:

    This paper deals with the single machine scheduling problem with earliness and tardiness penalties, considering distinct time windows and sequence-dependent setup time. Due to the complexity of this problem, an adaptive genetic algorithm is proposed for solving it. Many search operators are used to explore the solution space where the Choice Probability for each operator depends on the success in a previous search. The initial population is generated by applying GRASP to five dispatch rules. For each individual generated, a polynomial time algorithm is used to determine the initial optimal processing date for each job. During the evaluation process, the best individuals produced by each crossover operator, in each generation undergo refinement in order to improve quality of individuals. Computational results show the effectiveness of the proposed algorithm.

G.i. Papadimitriou - One of the best experts on this subject based on the ideXlab platform.

  • On carrier-sense integration in learning automata-based MAC protocols for ad-hoc wireless LANs
    IEEE International Conference on Performance Computing and Communications 2004, 2004
    Co-Authors: P. Nicopolitidis, G.i. Papadimitriou, A.s. Pomportsis
    Abstract:

    A carrier-sense-assisted learning automata-based MAC protocol for wireless LANs, capable of operating efficiently under bursty traffic and unreliable channel feedback, is introduced. According to the proposed protocol, the mobile station that is granted permission to transmit is selected by means of learning automata. At each station, the learning automaton takes into account the network feedback information in order to update the Choice Probability of each mobile station. The proposed protocol utilizes carrier sensing in order to reduce the collisions that are caused by different decisions at the various mobile stations due to the unreliable channel feedback.

  • A new class of /spl epsi/-optimal learning automata
    IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2004
    Co-Authors: G.i. Papadimitriou, M. Sklira, A.s. Pomportsis
    Abstract:

    A new class of P-model absorbing learning automata is introduced. The proposed automata are based on the use of a stochastic estimator in order to achieve a rapid and accurate convergence when operating in stationary random environments. According to the proposed stochastic estimator scheme, the estimates of the reward probabilities of actions are not strictly dependent on the environmental responses. The dependence between the stochastic estimates and the deterministic ones is more relaxed for actions that have been selected only a few times. In this way, actions that have been selected only a few times, have the opportunity to be estimated as "optimal," to increase their Choice Probability and consequently, to be selected. In this way, the estimates become more reliable and consequently, the automaton rapidly and accurately converges to the optimal action. The asymptotic behavior of the proposed scheme is analyzed and it is proved to be /spl epsi/-optimal in every stationary random environment. Furthermore, extensive simulation results are presented that indicate that the proposed stochastic estimator scheme converges faster than the deterministic-estimator-based DP/sub RI/ and DGPA schemes when operating in stationary P-model random environments.

  • An adaptive MAC protocol for ad-hoc wireless LANs
    2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484), 2003
    Co-Authors: P. Nicopolitidis, G.i. Papadimitriou, A.s. Pomportsis
    Abstract:

    An ad-hoc learning automata-based protocol for wireless LANs, capable of operating efficiently under bursty traffic conditions, is introduced. According to the proposed protocol, the mobile station that is granted permission to transmit is selected by means of learning automata. The learning automaton takes into account the network feedback information in order to update the Choice Probability of each mobile station. The proposed protocol is compared via simulation to TDMA under bursty traffic conditions and is shown to exhibit superior performance even when the network feedback is noisy.

  • A neural-based MAC protocol for distributed wireless LANs
    SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems Man and Cybernetics. Conference Theme - System Security and Assurance (Ca, 2003
    Co-Authors: P. Nicopolitidis, G.i. Papadimitriou, A.s. Pomportis
    Abstract:

    A self-adaptive neural-based MAC protocol for distributed wireless LANs, capable of operating efficiently under bursty traffic conditions, is introduced. According to the proposed protocol, the mobile station that is granted permission to transmit is selected by means of a neural-based algorithm. The neural-based algorithm takes into account the network feedback information in order to update the Choice Probability of each mobile station. The proposed protocol is compared via simulation to TDMA and is shown to exhibit superior performance under bursty traffic conditions even when the network feedback is noisy.

  • On the use of population-based incremental learning in the medium access control of broadcast communication systems
    10th IEEE International Conference on Electronics Circuits and Systems 2003. ICECS 2003. Proceedings of the 2003, 2003
    Co-Authors: G.i. Papadimitriou, A.s. Pomportsis
    Abstract:

    The Time Division Multiple Access protocol suffers from poor performance when the offered traffic is bursty. In this paper, an adaptive Time Division Multiple Access protocol, which is capable of operating efficiently under bursty traffic conditions, is introduced. According to the proposed protocol, the station which is granted permission to transmit at each time slot is selected by means of a variation of the population-based incremental learning (PBIL) algorithm. The Choice Probability of the selected station is updated by taking into account the network feedback information. In this way, the proposed protocol is always capable of being adapted to the sharp changes of the station's traffic.

Marcone Jamilson Freitas Souza - One of the best experts on this subject based on the ideXlab platform.

  • An adaptive genetic algorithm to solve the Single Machine Scheduling Problem with Earliness and Tardiness Penalties
    IEEE Congress on Evolutionary Computation, 2010
    Co-Authors: Fabio Fernandes Ribeiro, Sergio Ricardo De Souza, Marcone Jamilson Freitas Souza, Rogério M. Gomes
    Abstract:

    This paper deals with the Single Machine Scheduling Problem with Earliness and Tardiness Penalties, considering distinct due windows and sequence-dependent setup time. Due to its complexity, an adaptive genetic algorithm is proposed for solving it. Five search operators are used to explore the solution space and the Choice Probability for each operator depends on the success in a previous search. The initial population is generated by the combination between construct methods based on greedy, random and GRASP techniques. For each job sequence generated, a polynomial time algorithm are used for determining the processing initial optimal date to each job. During the evolutive process, a group with the best five individuals generated by each crossover operator is built. Then, periodically, a Path Relinking module is applied taking as base individual the best one so far generated by the algorithm and as guide individual each one of the five best individuals generated by each crossover operator. Three variations of this algorithm were submitted to computational experiments. The results shows the effectiveness of the proposal.

  • An adaptive genetic algorithm for solving the single machine scheduling problem with earliness and tardiness penalties
    2009 IEEE International Conference on Systems Man and Cybernetics, 2009
    Co-Authors: Fabio Fernandes Ribeiro, Sergio Ricardo De Souza, Marcone Jamilson Freitas Souza
    Abstract:

    This paper deals with the single machine scheduling problem with earliness and tardiness penalties, considering distinct time windows and sequence-dependent setup time. Due to the complexity of this problem, an adaptive genetic algorithm is proposed for solving it. Many search operators are used to explore the solution space where the Choice Probability for each operator depends on the success in a previous search. The initial population is generated by applying GRASP to five dispatch rules. For each individual generated, a polynomial time algorithm is used to determine the initial optimal processing date for each job. During the evaluation process, the best individuals produced by each crossover operator, in each generation undergo refinement in order to improve quality of individuals. Computational results show the effectiveness of the proposed algorithm.