Particle Swarm Optimization - A Survey <s> Generalized Velocity Update Rules <s> The canonical particle swarm algorithm is a new approach to optimization, drawing inspiration from group behavior and the establishment of social norms. It is gaining popularity, especially because of the speed of convergence and the fact that it is easy to use. However, we feel that each individual is not simply influenced by the best performer among his neighbors. We, thus, decided to make the individuals "fully informed." The results are very promising, as informed individuals seem to find better solutions in all the benchmark functions. <s> BIB001 </s> Particle Swarm Optimization - A Survey <s> Generalized Velocity Update Rules <s> This paper presents a variant of particle swarm optimizers (PSOs) that we call the comprehensive learning particle swarm optimizer (CLPSO), which uses a novel learning strategy whereby all other particles' historical best information is used to update a particle's velocity. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. Experiments were conducted (using codes available from http://www.ntu.edu.sg/home/epnsugan) on multimodal test functions such as Rosenbrock, Griewank, Rastrigin, Ackley, and Schwefel and composition functions both with and without coordinate rotation. The results demonstrate good performance of the CLPSO in solving multimodal problems when compared with eight other recent variants of the PSO. <s> BIB002
In the original PSO, particle velocity update is only dependent to the particle's pbest (p i ) and gbest ( g i ) as in Eq. (2). Mendes et al. BIB001 extended this selective dependence to all the particles in the neighborhood; namely to make each particle fully informed of the pbest of the particles in S i . The modified update rule with constriction χ is, where q i is a weighted sum of p j for all particles in S i . The weights are set according to various aspects of the particles. Such aspects include the evaluation of each particle's pbest ( f ( p j )) and the distance to particle c i (||x i − x j ||). Liang et al. BIB002 use a similar update rule as Eq. (20), with each element of q i being chosen from the elements of p j (c j ∈ S i ) via a tournament selection.