Double Flight-Modes Particle Swarm Optimization

Abstract

Getting inspiration from the real birds in flight, we propose a new particle swarm optimization algorithm that we call the double flight modes particle swarm optimization (DMPSO) in this paper. In the DMPSO, each bird (particle) can use both rotational flight mode and nonrotational flight mode to fly, while it is searching for food in its search space. There is a King in the swarm of birds, and the King controls each bird’s flight behavior in accordance with certain rules all the time. Experiments were conducted on benchmark functions such as Schwefel, Rastrigin, Ackley, Step, Griewank, and Sphere. The experimental results show that the DMPSO not only has marked advantage of global convergence property but also can effectively avoid the premature convergence problem and has good performance in solving the complex and high-dimensional optimization problems.

An Algorithm for Solving Stochastic Chance-Constrained Programming Problem

“…As a new intelligent algorithm, particle swarm optimization(PSO) algorithm was pioneered by Eberhart and Kennedy [1] .it employs global search strategy based on population,which is similar to Genetic Algorithm(GA).However,it only uses simple V-S model to finish searching optimum. A few parameters only need adjust,it owns many merits such as simplicity,more rapid convergence, better precision and so on, its dominance has been exhibited in solving of all kinds of problems [2][3][4].…”