Particle swarm optimization (PSO) algorithm is a swarm intelligent searching algorithm based on population that simulates the social behavior of birds, bees, or fish groups. The discrete binary particle swarm optimization (BPSO) algorithm maps the continuous search space to a binary space through a new transfer function, and the update process is designed to switch the position of the particles between 0 and 1 in the binary search space. Aiming at the existed BPSO algorithms which are easy to fall into the local optimum, a new Z-shaped probability transfer function is proposed to map the continuous search space to a binary space. By adopting nine typical benchmark functions, the proposed Z-probability transfer function and the V-shaped and S-shaped transfer functions are used to carry out the performance simulation experiments. The results show that the proposed Z-shaped probability transfer function improves the convergence speed and optimization accuracy of the BPSO algorithm.

中文翻译：

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Z形传递函数用于二进制粒子群优化算法。

微粒群优化（PSO）算法是一种基于种群的种群智能搜索算法，可模拟鸟类，蜜蜂或鱼类群体的社会行为。离散二元粒子群优化（BPSO）算法通过新的传递函数将连续搜索空间映射到二元空间，并且设计了更新过程以在二元搜索空间中将粒子的位置切换为0和1之间。针对现有的BPSO算法容易陷入局部最优的问题，提出了一种新的Z形概率传递函数，将连续搜索空间映射到二进制空间。通过采用九种典型的基准函数，将提出的Z概率传递函数以及V形和S形传递函数用于进行性能模拟实验。