# A Heuristics-Guided Simplified Discrete Harmony Search Algorithm for Solving 0-1 Knapsack Problem
## 5 Comparative Experiments
### Experiment on the Second Set of Large-Scale 0-1 KP Instances
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Ins** & **Opt** & **Method** & **Best** & **Worst** & **Mean** & **Median** & **Std** & **Time (s)** \\ \hline KP06 & 35,069 & CSGHS & 34,850 & 34,795 & 34,824 & 34,825 & 14.00 & - \\  & & BMBO & 34,860 & 34,681 & 34,786 & 34,784 & 35.01 & - \\  & & CMBO & **35,069** & 35,064 & 35,067 & 35,067 & 1.45 & - \\  & & OMBO & **35,069** & 35,064 & 35,067 & 35,068 & 1.47 & - \\  & & NM & **35,069** & **35,069** & **35,069** & **35,069** & **0** & - \\  & & LBSA & **35,069** & 35,068 & 35,068.99 & **35,069** & 0.02 & - \\  & & SDHS & **35,069** & **35,069** & **35,069** & **35,069** & **0** & 0.38 \\ KP07 & 43,786 & CSGHS & 43,484 & 43,386 & 43,440 & 43,442 & 22.39 & - \\  & & BMBO & 43,491 & 43,359 & 43,412 & 43,413 & 31.36 & - \\  & & CMBO & **43,786** & 43,781 & 43,784 & 43,784 & 1.34 & - \\  & & OMBO & **43,786** & 43,782 & 43,785 & 43,785 & 1.03 & - \\  & & NM & **43,786** & 43,785 & 43,785.96 & **43,786** & 0.08 & - \\  & & LBSA & **43,786** & 43,785 & 43,785.97 & **43,786** & 0.06 & - \\  & & SDHS & **43,786** & **43,786** & **43,786** & **43,786** & **0** & 0.63 \\ KP08 & 53,553 & CSGHS & 52,711 & 52,354 & 52,556 & 52,565 & 76.89 & - \\  & & BMBO & 52,774 & 52,110 & 52,425 & 52,390 & 158.2 & - \\  & & CMBO & 53,552 & 53,552 & 53,552 & 53,552 & **0** & - \\  & & OMBO & **53,553** & 53,552 & 53,552 & 53,552 & 1.82 & - \\  & & NM & **53,553** & 53,552 & 53,552.02 & 53,552 & 0.04 & - \\  & & LBSA & **53,553** & 53,552 & 53,552.03 & 53,552 & 0.06 & - \\  & & SDHS & **53,553** & **53,553** & **53,553** & **53,553** & **0** & 0.69 \\ KP09 & 65,710 & CSGHS & 65,116 & 64,980 & 65,045 & 65,044 & 38.14 & - \\  & & BMBO & 65,123 & 64,916 & 65,022 & 65,012 & 56.38 & - \\  & & CMBO & **65,710** & 65,708 & 65,709 & 65,708 & 0.58 & - \\  & & OMBO & **65,710** & 65,708 & 65,709 & 65,709 & 0.52 & - \\  & & NM & 65,709 & 65,709 & 65,709 & 65,709 & **0** & - \\  & & LBSA & 65,709 & 65,709 & 65,709 & 65,709 & **0** & - \\  & & SDHS & **65,710** & **65,710** & **65,710** & **65,710** & **0** & 0.76 \\ KP10 & 108,200 & CSGHS & - & - & - & - & - & - \\  & & BMBO & - & - & - & - & - & - \\  & & CMBO & **108,200** & **108,200** & **108,200** & **0** & - \\  & & OMBO & **108,200** & **108,200** & **108,200** & **0** & - \\  & & NM & **108,200** & **108,200** & **108,200** & **0** & - \\  & & LBSA & **108,200** & **108,200** & **108,200** & **0** & - \\  & & SDHS & **108,200** & **108,200** & **108,200** & **0** & 1.14 & - \\ \hline \end{tabular} Note: Bold numbers indicate the algorithm found the relatively best solution.  
\end{table} 
Table 11: Comparison of the SDHS algorithm with six metaheuristics on large-scale weakly correlated 0-1 KP instances. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Ins** & **Opt** & **Method** & **Best** & **Worst** & **Mean** & **Median** & **Std** & **Time (s)** \\ \hline KP11 & 40,167 & CSGHS & 40,147 & 40,126 & 40,132 & 40,130 & 5.54 & - \\  & & BMBO & 40,127 & 40,107 & 40,116 & 40,117 & 4.52 & - \\  & & CMBO & **40,167** & 40,166 & **40,167** & **40,167** & 0.14 & - \\  & & OMBO & **40,167** & **40,167** & **40,167** & **40,167** & **0** & - \\  & & NM & **40,167** & **40,167** & **40,167** & **40,167** & **0** & - \\  & & LBSA & **40,167** & **40,167** & **40,167** & **40,167** & **0** & - \\  & & SDHS & **40,167** & **40,167** & **40,167** & **40,167** & **0** & 0.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison of the SDHS algorithm with six metaheuristics on large-scale strongly correlated 0-1 KP instances.