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Degree distribution plays a great role in the performance of Luby transform codes. Typical degree distributions such as ideal soliton distribution and robust soliton distribution are easy to implement and widely used. Nevertheless, their adaptabilities are not always outstanding in various code lengths, especially in the case of short length. In this paper, our work is to optimize degree distributions for the short-length LT codes by using swarm intelligence algorithm, considering its conceptual simplicity, high efficiency, flexibility, and robustness. An optimization problem model based on sparse degree distributions is proposed in the first place. Then, a solution on the basis of an enhanced chicken swarm optimization algorithm, termed as ECSO, is designed for the problem. In ECSO, substitution of bottom individuals, revision of chicks' update equation, and introduction of differential evolution are designed to enhance the ability of optimization. Simulation comparisons show that the proposed solution achieves much better performance than two other swarm intelligence-based solutions.

Based the defects of global optimal model falling into local optimum easily and local model with slow convergence speed during traditional PSO algorithm solving a complex high-dimensional and multi-peak function, a two sub-swarms particle optimization algorithm is proposed. All particles are divided into two equivalent parts. One part particles adopts global evolution model, while the other part uses local evolution model. If the global optimal fitness of the whole population stagnates for some iteration, a golden rule is introduced into local evolution model. This strategy can substitute the partial perfect particles of local evolution for the equivalent worse particles of global evolution model. So, some particles with advantage are joined into the whole population to make the algorithm keep active all the time. Compared with classic PSO and PSO-GL(A dynamic global and local combined particle swarm optimization algorithm, PSO-GL), the results show that the proposed PSO in this paper can get more effective performance over the other two algorithm in the simulation experiment for four benchmark testing function.

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