To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The se...To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The self-adaptive inertia weight factor was used to accelerate the converging speed, and chaotic sequences were used to tune the acceleration coefficients for the balance between exploration and exploitation. The performance of the proposed algorithm was tested on four classical multi-objective optimization functions by comparing with the non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results verified the effectiveness of the algorithm, which improved the premature convergence problem with faster convergence rate and strong ability to jump out of local optimum.展开更多
In order to improve some shortcomings of the standard particle swarm optimization algorithm, such as premature convergence and slow local search speed, a double population particle swarm optimization algorithm based o...In order to improve some shortcomings of the standard particle swarm optimization algorithm, such as premature convergence and slow local search speed, a double population particle swarm optimization algorithm based on Lorenz equation and dynamic self-adaptive strategy is proposed. Chaotic sequences produced by Lorenz equation are used to tune the acceleration coefficients for the balance between exploration and exploitation, the dynamic self-adaptive inertia weight factor is used to accelerate the converging speed, and the double population purposes to enhance convergence accuracy. The experiment was carried out with four multi-objective test functions compared with two classical multi-objective algorithms, non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results show that the proposed algorithm has excellent performance with faster convergence rate and strong ability to jump out of local optimum, could use to solve many optimization problems.展开更多
Forest harvesting adjustment is a decision-making,large and complex system. In this paper,we analysis the shortcomings of the traditional harvest adjustment problems,and establish the model of multi-target harvest adj...Forest harvesting adjustment is a decision-making,large and complex system. In this paper,we analysis the shortcomings of the traditional harvest adjustment problems,and establish the model of multi-target harvest adjustment. As intelligent optimization,chaotic genetic algorithm has the parallel mechanism and the inherent global optimization characteristics which are suitable for multi-objective planning the settlement of the issue,specially in complex occasions where there are many objective functions and optimize variables. In order to solve the problem of forest harvesting adjustment,this paper introduces a genetic algorithm to the Forest Farm of Qiujia Liancheng Longyan for forest harvesting adjustment firstly. And the experimental result shows that the method is feasible and effective,and it can provide satisfactory solution for policy makers.展开更多
文摘To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The self-adaptive inertia weight factor was used to accelerate the converging speed, and chaotic sequences were used to tune the acceleration coefficients for the balance between exploration and exploitation. The performance of the proposed algorithm was tested on four classical multi-objective optimization functions by comparing with the non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results verified the effectiveness of the algorithm, which improved the premature convergence problem with faster convergence rate and strong ability to jump out of local optimum.
文摘In order to improve some shortcomings of the standard particle swarm optimization algorithm, such as premature convergence and slow local search speed, a double population particle swarm optimization algorithm based on Lorenz equation and dynamic self-adaptive strategy is proposed. Chaotic sequences produced by Lorenz equation are used to tune the acceleration coefficients for the balance between exploration and exploitation, the dynamic self-adaptive inertia weight factor is used to accelerate the converging speed, and the double population purposes to enhance convergence accuracy. The experiment was carried out with four multi-objective test functions compared with two classical multi-objective algorithms, non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results show that the proposed algorithm has excellent performance with faster convergence rate and strong ability to jump out of local optimum, could use to solve many optimization problems.
文摘Forest harvesting adjustment is a decision-making,large and complex system. In this paper,we analysis the shortcomings of the traditional harvest adjustment problems,and establish the model of multi-target harvest adjustment. As intelligent optimization,chaotic genetic algorithm has the parallel mechanism and the inherent global optimization characteristics which are suitable for multi-objective planning the settlement of the issue,specially in complex occasions where there are many objective functions and optimize variables. In order to solve the problem of forest harvesting adjustment,this paper introduces a genetic algorithm to the Forest Farm of Qiujia Liancheng Longyan for forest harvesting adjustment firstly. And the experimental result shows that the method is feasible and effective,and it can provide satisfactory solution for policy makers.
