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 Wireless Communications
Opportunistic power scheduling for dynamic multi-server wireless systems
IEEE Transactions on Wireless Communications
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
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
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Particle swarm optimization (PSO) is one of the most important biological swarm intelligence paradigms. However, the standard PSO algorithm can easily get trapped in the local optima when solving complex multimodal problems. In this paper, an improved adaptive particle swarm optimization (IAPSO) is presented. Based on IAPSO, a joint opportunistic power and rate allocation (JOPRA) algorithm is proposed to maximize the sum of source utilities while minimize power allocation for all links in wireless ad hoc networks. It is shown that the proposed JOPRA algorithm can converge fast to the optimum and reach larger total data rate and utility while less total power is consumed by comparison with the original APSO. This thanks to the dynamic change of the maximum movement velocity of the particles, the use of the modified replacement procedure in constraint handling, and the consideration of the state of the optimization run and the population diversity in stopping criteria. Numerical simulations further verify that our algorithm with the IAPSO outperforms that with the original APSO.