There is recently a great deal of interest and excitement in understanding the role of inertia and acceleration in the motion equation of discrete particle swarm optimization (DPSO) algorithms. It still remains unknown whether the inertia section should be abandoned and how to select the appropriate acceleration in order for DPSO to show the best convergence performance. Adopting channel assignment as a case study, this paper systematically conducts experimental filtering research on this issue. Compared with other channel assignment schemes, the proposed scheme and the selection of inertia and acceleration are verified to have the advantage to channel assignment in three respects of convergence rate, convergence speed, and the independency of the quality of initial solution. Furthermore, the experimental result implies that DSPO might have the best convergence performance when its motion equation includes an inertia section in a less medium weight, a bigger acceleration coefficient for global-search optimum, and a smaller acceleration coefficient for individual-search optimum. 1. Introduction The ever-increasing popularity of mobile communication services gives rise to the need for efficient use of the limited frequency spectrum [1, 2]. The channel assignment problem (CAP) is to obtain a conflict-free channel assignment scheme, which satisfies both the electromagnetic compatibility (EMC) constrains and regional demand for channel. Generally, there are three types of electromagnetic compatibility constraints. The cochannel constraint (CCC) restricts the assignment of the same channel to certain pairs of cells simultaneously. The adjacent channel constraint (ACC) restricts the assignment of channels adjacent in number to adjacent cells simultaneously. The cosite constraint (CSC) specifies that any pair of channels assigned to the same cell must be separated by a certain number. Currently, much effort has been made to solve the CAP, such as neural network (NN), simulated annealing (SA), and genetic algorithm (GA). However, NN based algorithms typically yield only suboptimal solutions. The SA approach, although it may be more flexible, is easily trapped in a local minimum, which cannot escape without spending a lot of computation time [3]. GA can effectively locate the neighborhood of the global optimum, but it has the problem of converging to the optimum itself. In other words, the algorithms mentioned above are not particularly efficient in local search [4, 5]. Particle swarm optimization (PSO) is an intelligent algorithm introduced by [6]. It does not
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