Double Evaluation Of Particle Swarm Algorithm

Particle swarm optimization(PSO) is a kind of stchastic and global search algo-rithm based on the behavior and intelligence simulation of biology swarm. Because of its inherent parallelization, robustness, simple operation and less parameters, PSO was received extensive attention, and widely applied in the fields of science, engineering and so on. When applied to high dimensional and complex problems, this algorithm was easy to fall into local optimum and pre-convergence. Thus this paper proposes Double Evalution’s Particle Swarm Optimization and discusses its application.Contrary to Standard Particle Swarm Optimization doesn’t consider change of current particle’s location and fitness in iterative process, so some particles’indi-vidual cognitive abilities may not improve sometime. Firstly, the change of current particle’s location and fitness is compared with iterated particle’s in Double Evalu-tion’s Particle Swarm Optimization. Then. utilizing Genetic Algorithms’mutation thinking particle which is worsed than iterated particle’s location and fitness imu-tated, in order to improve particles’ individual cognitive abilities. Finally, utilizing eight, test functions tests algorithm. experimental results show that Double Evalu-tion’s Particle Swarm Optimization can accelerate speed of convergence, but improve solution accuracy.The knapsack problem is typical combinatorial optimization problem, it is ap-plied in capital budgeting, resource allocation practical problems and so on. This paper solves the0-1knapsack problem by Double Evalution’s Particle Swarm Op-timization. Solving the0-1knapsack problem’s instance results show that Double Evalution’s Particle Swarm Optimization can effectively solve combinatorial opti-mization problem.