A power control MAC protocol for ad hoc networks
Proceedings of the 8th annual international conference on Mobile computing and networking
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Optimal and distributed protocols for cross-layer design of physical and transport layers in MANETs
IEEE/ACM Transactions on Networking (TON)
Optimization of power allocation for interference cancellation with particle swarm optimization
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Opportunistic link scheduling for multihop wireless networks
IEEE Transactions on Wireless Communications
Resource-redistributive opportunistic scheduling for wireless systems
IEEE Transactions on Wireless Communications
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Communications
IEEE Transactions on Wireless Communications
Opportunistic power scheduling for dynamic multi-server wireless systems
IEEE Transactions on Wireless Communications
Asynchronous distributed power and rate control in ad hoc networks: a game-theoretic approach
IEEE Transactions on Wireless Communications
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Opportunistic transmission scheduling with resource-sharing constraints in wireless networks
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
Information Sciences: an International Journal
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In this paper, the joint opportunistic power and rate allocation (JOPRA) algorithm, which aims at maximizing the sum of source utilities while minimizing power allocation for all links in wireless ad hoc networks, is solved by means of an improved adaptive particle swarm optimization (IAPSO), which can overcome some limitations of the traditional dual and subgradient method. Compared with the original APSO, in our IAPSO, the maximum movement velocity of the particles changes dynamically, a modified replacement procedure with no introduced additional parameters is employed in constraint handling, and the state of the optimization run and the diversity in the population are taken into account in stopping criteria. It is shown that the proposed JOPRA algorithm can fast converge to the optimum and reach larger total data rate and utility while less total power is consumed. The efficiency of our approach is further illustrated via numerical comparison with the original APSO. This work is a beneficial attempt to integrate adaptive evolutionary algorithms with the resource allocation in wireless ad hoc networks.