Particle Swarm Optimization - A Survey <s> Introduction <s> The aggregate motion of a flock of birds, a herd of land animals, or a school of fish is a beautiful and familiar part of the natural world. But this type of complex motion is rarely seen in computer animation. This paper explores an approach based on simulation as an alternative to scripting the paths of each bird individually. The simulated flock is an elaboration of a particle systems, with the simulated birds being the particles. The aggregate motion of the simulated flock is created by a distributed behavioral model much like that at work in a natural flock; the birds choose their own course. Each simulated bird is implemented as an independent actor that navigates according to its local perception of the dynamic environment, the laws of simulated physics that rule its motion, and a set of behaviors programmed into it by the "animator." The aggregate motion of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual simulated birds. <s> BIB001 </s> Particle Swarm Optimization - A Survey <s> Introduction <s> A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described. <s> BIB002
This paper serves as a survey on a group of optimization algorithms known as Particle Swarm Optimization (PSO). PSO was originally introduced by Kennedy and Eberhart in 1995 BIB002 . After its introduction, the simplicity and the flexibility of the algorithm achieving a very efficient search of near-optimal solutions even for problems with quite a tricky search space landscape, has attracted many researchers who try to analyze and improve it, and also those who intend to apply it. The original research by Kennedy and Eberhart started out to acquire local agent rules for simulating the social behavior of a flock of birds or a school of fish. Inspired by the works such as BIB001 by Reynolds, who programmed a flocksimulating group of agents (boids) by sole description of agent interaction rules, they started to try various interaction rules among the agents. Soon, they found that some type of interaction in the flock can benefit the efficiency of finding an important location, such as food in the flock's domain of activity. They saw this nature effective as a novel algorithm for general nonlinear programming, so they improved and simplified the rule for updating the agent's position, making use of some local memory and information sharing. Thus the Particle Swarm Optimization was born. Since then, various modifications to the original PSO have been proposed. In parallel, they are being applied to various fields that require parameter optimization in a highdimensional space, favored due to the algorithm's simplicity Manuscript and its high efficiency in searching. This survey will try to follow the progress of PSO research. In the following, the original PSO will be reviewed and the trends of research in analyzing and improving the original algorithm in various aspects will be introduced in Sect. 2. In Sect. 3, works in several major research directions will be reviewed. Section 4 is devoted to current research trends towards using the PSO in large-scale optimization problems. Finally, in Sect. 5, the paper will be closed with concluding remarks.