<![CDATA[Ttraveling thief problem is a combination of the traveling salesman problem and the knapsack problem. traveling thief problem itself is an NP-Hard problem, so most of the problems are solved using a heuristic algorithm and it continues to grow over time. The algorithm used in this study is simple random for the selection of low level heuristics (LLH) and tree physiology optimization (TPO) for the move acceptance step using the Hyper-Heuristics model.. In previous research, the TPO algorithm is able to produce competitive values with good computation time, while Hyper-Heuristics modeling can produce consistent values on various data. The research was started by modeling the TPO algorithm into Hyper-Heuristics and tested it with data from TSPLib. From the results of the trials conducted, it can be seen how the performance of the new algorithm on the data being tested. Based on the results obtained from this study, it can be concluded that the LLH TPO algorithm can process TTP data with sizes below 100 quite well, as evidenced by better results than the previous genetic programming based hyper-heuristic (GPHS) method, but the data above 100 LLH TPO performance decreased when compared to the GPHS method.]]>
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