@phdthesis{oai:kyutech.repo.nii.ac.jp:00007709,
 author = {Syafruddin,  Willa Ariela},
 month = {2022-06-14},
 note = {1 Introduction||2 Traditional Metaheuristic Search Optimization||3 Parametric Study of Metaheuristic Search Algorithms||4 Embedded Metaheuristic Search Algorithms for Blockchain Proof-of-Work||5 Idle-Metaheuristic for Flower Pollination Simulation||6 Conclusion and Future Works, Several optimization algorithms have been developed to handle various optimization issues in many fields, capturing the attention of many researchers. Algorithm optimizations are commonly inspired by nature or involve the modification of existing algorithms. So far, the new algorithms are set up and focusing on achieving the desired optimization goal. While this can be useful and efficient in the short term, in the long run, this is not enough as it needs to repeat for any new problem that occurs and maybe in specific difficulties, therefore one algorithm cannot be used for all real-world problems. This dissertation provides three approaches for implementing metaheuristic search （MHS） algorithms in fields that do not directly solve optimization issues. The first approach is to study parametric studies on MHS algorithms that attempt to understand how parameters work in MHS algorithms. In this first direction, we choose the Jaya algorithm, a relatively recent MHS algorithm defined as a method that does not require algorithm-specific control parameters. In this work, we incorporate weights as an extra parameter to test if Jaya’s approach is actually "parameter-free." This algorithm’s performance is evaluated by implementing 12 unconstrained benchmark functions. The results will demonstrate the direct impact of parameter adjustments on algorithm performance. The second approach is to embed the MHS algorithm on the Blockchain Proof of Work （PoW） to deal with the issue of excessive energy consumption, particularly in using bitcoin. This study uses an iterative optimization algorithm to solve the Traveling Salesperson Problem （TSP） as a model problem, which has the same concept as PoW and requires extending the Blockchain with additional blocks. The basic idea behind this research is to increase the tour cost for the best tour found for n blocks, extended by adding one more city as a requirement to include a new block in the Blockchain. The results reveal that the proposed concept can improve the way the current system solves complicated cryptographic problems Furthermore, MHS are implemented in the third direction approach to solving agricultural problems, especially the cocoa flowers pollination. We chose the problem in pollination in cacao flowers since they are distinctive and different from other flowers due to their small size and lack of odor, allowing just a few pollinators to successfully pollinate them, most notably a tiny midge called Forcipomyia Inornatipennis （FP）. This concept was then adapted and implemented into an Idle-Metaheuristic for simulating the pollination of cocoa flowers. We analyze how MHS algorithms derived from three well-known methods perform when used to flower pollination problems. Swarm Intelligence Algorithms, Individual Random Search, and Multi-Agent Systems Search are the three methodologies studied here. The results shows that the Multi-Agent System search performs better than other methods. The findings of the three approaches reveal that adopting an MHS algorithms can solve the problem in this study by indirectly solving the optimization problem using the same problem model concept. Furthermore, the researchers concluded that parameter settings in the MHS algorithms are not so difficult to use, and each parameter can be adjusted to solve the real-world issue. This study is expected to encourage other researchers to improve and develop the performance of MHS algorithms used to deal with multiple real-world problems., 九州工業大学博士学位論文 学位記番号: 情工博甲第367号 学位授与年月日: 令和4年3月25日, 令和3年度},
 school = {九州工業大学},
 title = {Incubation of Metaheuristic Search Algorithms Into Novel Application Fields},
 year = {}
}