# A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment
## 5 Experimental Results and Analysis
### The Experimental Results of BCSO  
The BCSO algorithm is applied to the different trials to test the performance. To eliminate the randomness, algorithms run 30 independent trials from different initial populations in each trial and get the mean, best and worst values. The Table 2 shown the comparison result between BCSO and improved binary particle swarm optimization (IBPSO), improved particle swarm optimization (IPSO), hybrid particle swarm optimization (HPSO), quantum-inspired particle swarm optimization (QPSO), genetic algorithm (GA), simulated annealing (SA), binary- real-code GA (brGA), discrete binary differential evolution (DBDE), binary differential evolution (BDE), binary glowworm swarm optimization (BPSO), binary particle swarm optimization (BPSO), best parallel particle swarm optimization (BLPSO), and new binary particle swarm optimization (NBPSO) algorithm with the unit is 10, from it can see that the optimum result of NBPSO and BCSO are 563,937.68 $/day, but BCSO has an advantage in the worst cost among all the algorithms, and the stability of BCSO is proven. The experimental results of different unit numbers used BCSO are shown in Table 3. It can be seen from the table, when the unit is 10, the optimum solution of the BCSO is 563,937.68 $/day, and the worst fuel cost is 563,937.68 $/day. From the mean, best and worst values, the fluctuation of the optimum solutions can be compared. It can also be seen from the result that all the corresponding values obtained by BCSO are comparatively small. The convergence curve of BCSO for the 10-unit case is shown in Figure 1, demonstrating that the convergence speed of BCSO is fast and can obtain the optimal solution within around 30 iterations. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & **Unit 1** & **Unit 2** & **Unit 3** & **Unit 4** & **Unit 5** & **Unit 6** & **Unit 7** & **Unit 8** & **Unit 9** & **Unit 10** \\ \hline Pmax (MW) & 455 & 455 & 130 & 130 & 162 & 80 & 85 & 55 & 55 & 55 \\ Pmin (MW) & 150 & 150 & 20 & 20 & 25 & 20 & 25 & 10 & 10 & 10 \\ a ($/MW h) & 1000 & 970 & 700 & 680 & 450 & 370 & 480 & 660 & 665 & 670 \\ b ($/MW h) & 16.19 & 17.26 & 16.6 & 16.5 & 19.7 & 22.26 & 27.47 & 25.92 & 27.27 & 27.79 \\ c ($/MW h) & 0.00048 & 0.00031 & 0.002 & 0.00211 & 0.00398 & 0.00712 & 0.000793 & 0.00413 & 0.002221 & 0.00173 \\ MU (h) & 8 & 8 & 5 & 5 & 6 & 3 & 3 & 1 & 1 \\ MD (h) & 8 & 8 & 5 & 5 & 6 & 3 & 3 & 1 & 1 & 1 \\ Hot Start Cost (\$) & 4500 & 5000 & 550 & 560 & 900 & 170 & 260 & 30 & 30 & 30 \\ Cold Start Cost (\$) & 9000 & 10,000 & 1100 & 1120 & 1800 & 340 & 520 & 60 & 60 & 60 \\ Initial Status (h) & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline \end{tabular}
\end{table} 
Table 1: Parameter setting for Binary Competitive Swarm Optimizer (BCSO). Figure 1: Optimal convergences using different algorithms for different unit numbers. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Methods** & **Trials** & **Population** & **Iteration** & **Best (S)** & **Mean (S)** & **Worst (S)** & **STDV** & **Time (s)** \\ \hline IBPSO & 10 & 20 & 2000 & 563,777 & 564,155 & 565,312 & 143 & 27 \\ IPSO & 50 & 40 & 1000 & 563,954 & 564,162 & 564,579 & - & - \\ HFPSO & 100 & 20 & 1000 & 563,942.3 & 564,772.3 & 565,785.3 & - & - \\ QBPSO & 50 & - & 1000 & 563,977 & 563,977 & 563,977 & 0.00 & 18 \\ GA & 20 & 50 & 500 & 565,852 & - & 570,032 & - & 221 \\ SA & - & - & 50 & 565,828 & 565,988 & 566,260 & - & 3.35 \\ brGA & 30 & - & 1000 & 563,938 & 564,253 & 564,088 & 18 & - \\ DBDE & 20 & 40 & 1000 & 563,977 & 564,028 & 564,241 & 103 & 3.6 \\ BDE & 50 & 20 & 1000 & 563,977 & 563,977 & 563,977 & 0.00 & - \\ BGSO & 50 & 50 & - & 563,938 & 563,952 & 564,226 & - & 3 \\ BPSO & 30 & 150 & 200 & 563,955,996 & 564,000.40 & 564,053.73 & 21.63 & 25.45 \\ BLPSO & 30 & 150 & 200 & 563,977.01 & 563,982.09 & 563,987.16 & - & 22.09 \\ NBPSO & 30 & 150 & 200 & 563,937.68 & 563,962.59 & 563,977.01 & - & 21.91 \\ BCSO & 30 & 150 & 200 & 563,937.68 & 563,937.68 & 563,937.68 & 0.00 & 11.58 \\ \hline \hline \end{tabular}
\end{table}