中文说明：
各算法对应的问题如下：
PSO用基本粒子群算法求解无约束优化问题
YSPSO用带压缩因子的粒子群算法求解无约束优化问题
LinWPSO用线性递减权重粒子群优化算法求解无约束优化问题
SAPSO 用自适应权重粒子群优化算法求解无约束优化问题
RandWPSO用随机权重粒子群优化算法求解无约束优化问题
LnCPSO用学习因子同步变化的粒子群优化算法求解无约束优化问题
AsyLnCPSO用学习因子异步变化的粒子群优化算法求解无约束优化问题
SecPSO用二阶粒子群优化算法求解无约束优化问题
SecVibratPSO用二阶振荡粒子群优化算法求解无约束优化问题
CLSPSO用混沌粒子群优化算法求解无约束优化问题
SelPSO用基于选择的粒子群优化算法求解无约束优化问
BreedPSO用基于交叉遗传的粒子群优化算法求解无约束优化问
SimuAPSO用基于模拟退火的粒子群优化算法求解无约束优化问题
English Description：
The corresponding problems of each algorithm are as follows:PSO uses particle swarm optimization to solve unconstrained optimization problems
Yspso uses particle swarm optimization with compression factor to solve unconstrained optimization problems
Linwpso uses linear decreasing weight particle swarm optimization algorithm to solve unconstrained optimization problems
SAPSO uses adaptive weight particle swarm optimization algorithm to solve unconstrained optimization problems
Randwpso uses random weight particle swarm optimization algorithm to solve unconstrained optimization problems
Lncpso uses particle swarm optimization algorithm with synchronous change of learning factors to solve unconstrained optimization problems
Asylncpso uses particle swarm optimization algorithm with asynchronous learning factors to solve unconstrained optimization problems
Secpso uses second order particle swarm optimization algorithm to solve unconstrained optimization problems
Secvibratpso uses second order oscillatory particle swarm optimization algorithm to solve unconstrained optimization problems
Clspso uses chaos particle swarm optimization algorithm to solve unconstrained optimization problems
Selpso uses selection based particle swarm optimization algorithm to solve unconstrained optimization problems
Breedpso uses particle swarm optimization algorithm based on cross genetic algorithm to solve unconstrained optimization problems
Simuapso uses particle swarm optimization algorithm based on simulated annealing to solve unconstrained optimization problems