A rank based particle swarm optimization algorithm with dynamic adaptation

TL;DR: A novel PSO method called LHNPSO, with low-discrepancy sequence initialized particles and high-order (1/π2) nonlinear time-varying inertia weight and constant acceleration coefficients, is proposed in this paper.

TL;DR: Several aspects of optimization heuristic designs and analysis are discussed in this paper and, as a result, detailed explanation, comparison, and discussion on AI are achieved.

TL;DR: The improvements in inertia weights, acceleration factors, and localization prevent the algorithm falling into a local minimum value and increase convergence speed of the algorithm, and the smoothing principle is applied in path planning.

TL;DR: The two-swarm cooperative particle swarm optimization (TCPSO) can not only catch the global optimum in a large search space such as 2×10 10, but also obtains a good balance between the swarm diversity and the convergence speed.

TL;DR: This paper first analyzes the impact that inertia weight and maximum velocity have on the performance of the particle swarm optimizer, and then provides guidelines for selecting these two parameters.

TL;DR: The comprehensive learning particle swarm optimizer (CLPSO) is presented, which uses a novel learning strategy whereby all other particles' historical best information is used to update a particle's velocity.

TL;DR: A novel parameter automation strategy for the particle swarm algorithm and two further extensions to improve its performance after a predefined number of generations to overcome the difficulties of selecting an appropriate mutation step size for different problems.

TL;DR: In this article, the authors proposed a theory of social impact specifying the effect of other persons on an in-dividual, where other people are the source of impact and the individual is the target, and impact should be a multiplicative function of the strength, immediacy and number of other people.