Accelerating the convergence speed and avoiding the local optimal solution are two main goals of particle swarm optimization（PSO）. The very basic PSO model and some variants of PSO do not consider the enhancement of...Accelerating the convergence speed and avoiding the local optimal solution are two main goals of particle swarm optimization（PSO）. The very basic PSO model and some variants of PSO do not consider the enhancement of the explorative capability of each particle. Thus these methods have a slow convergence speed and may trap into a local optimal solution. To enhance the explorative capability of particles, a scheme called explorative capability enhancement in PSO（ECE-PSO） is proposed by introducing some virtual particles in random directions with random amplitude. The linearly decreasing method related to the maximum iteration and the nonlinearly decreasing method related to the fitness value of the globally best particle are employed to produce virtual particles. The above two methods are thoroughly compared with four representative advanced PSO variants on eight unimodal and multimodal benchmark problems. Experimental results indicate that the convergence speed and solution quality of ECE-PSO outperform the state-of-the-art PSO variants.展开更多
This paper proposes a bargaining game theoretic resource(including the subcarrier and the power) allocation scheme for wireless orthogonal frequency division multiple access(OFDMA) networks.We define a wireless user s...This paper proposes a bargaining game theoretic resource(including the subcarrier and the power) allocation scheme for wireless orthogonal frequency division multiple access(OFDMA) networks.We define a wireless user s payoff as a function of the achieved data-rate.The fairness resource allocation problem can then be modeled as a cooperative bargaining game.The objective of the game is to maximize the aggregate payoffs for the users.To search for the Nash bargaining solution(NBS) of the game,a suboptimal subcarrier allocation is performed by assuming an equal power allocation.Thereafter,an optimal power allocation is performed to maximize the sum payoff for the users.By comparing with the max-rate and the max-min algorithms,simulation results show that the proposed game could achieve a good tradeoff between the user fairness and the overall system performance.展开更多
基金supported by the Aeronautical Science Fund of Shaanxi Province of China(20145596025)
文摘Accelerating the convergence speed and avoiding the local optimal solution are two main goals of particle swarm optimization（PSO）. The very basic PSO model and some variants of PSO do not consider the enhancement of the explorative capability of each particle. Thus these methods have a slow convergence speed and may trap into a local optimal solution. To enhance the explorative capability of particles, a scheme called explorative capability enhancement in PSO（ECE-PSO） is proposed by introducing some virtual particles in random directions with random amplitude. The linearly decreasing method related to the maximum iteration and the nonlinearly decreasing method related to the fitness value of the globally best particle are employed to produce virtual particles. The above two methods are thoroughly compared with four representative advanced PSO variants on eight unimodal and multimodal benchmark problems. Experimental results indicate that the convergence speed and solution quality of ECE-PSO outperform the state-of-the-art PSO variants.
基金supported by National Natural Science Foundation of China (No.60972059)Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions+3 种基金Fundamental Research Funds for the Central Universities of China (No.2010QNA27)China Postdoctoral Science Foundation(No.20100481185)Postdoctoral Research Funds of Jiangsu Province(No.1101108C)Postdoctoral Fellowship Program of the China Scholarship Council
文摘This paper proposes a bargaining game theoretic resource(including the subcarrier and the power) allocation scheme for wireless orthogonal frequency division multiple access(OFDMA) networks.We define a wireless user s payoff as a function of the achieved data-rate.The fairness resource allocation problem can then be modeled as a cooperative bargaining game.The objective of the game is to maximize the aggregate payoffs for the users.To search for the Nash bargaining solution(NBS) of the game,a suboptimal subcarrier allocation is performed by assuming an equal power allocation.Thereafter,an optimal power allocation is performed to maximize the sum payoff for the users.By comparing with the max-rate and the max-min algorithms,simulation results show that the proposed game could achieve a good tradeoff between the user fairness and the overall system performance.
