Abstract
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.
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