INCUBATION OF METAHEURISTIC SEARCH ALGORITHMS INTO NOVEL APPLICATION FIELDS

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九州工業大学令和3年

An evolutionary algorithm approach to ecological optimal control problems

There are several challenges associated with applying conventional (hereafter classic) optimal control (OC) methods to ecological optimal control problems (OCPs). Conditions required by these methods, including differentiability and convexity, for example, are not always met, and ecological problems do not always adhere to solvable OCP formulations. Moreover, mathematically optimal solutions do not always translate to optimal ecological strategies in practice. Despite this, alternative OC approaches are relatively under-explored. Evolutionary algorithms (EAs) circumvent many of the complex aspects of classic OC methods and have been successfully applied to diverse OCPs. Nevertheless, EAs have sel dom been applied to ecological OCPs. The viability of an EA approach to ecological OCPs was therefore investigated in the current study, facilitated by four case studies of increasing complexity and a genetic algorithm (GA) as a representative EA approach. To ascertain the accuracy of a GA approach, comparisons between a GA and classic OC methods were conducted in the first three case studies. The GA generated near-optima in these comparisons, comparable to the corresponding classical solutions, whilst avoiding non-trivial mathematical theory. Supported by these results, an unconventional OCP, that arguably cannot be solved using classic OC methods, was formulated in the fourth case study, and solved using a GA approach. The resulting solution was feasible and further conformed with strategies found to be successful in practice. Additionally, the GA approach was rela tively simple to apply in all case studies. These collective outcomes of demonstrated the viability of a GA as an OC method in eco logical OCPs, thereby supporting the use of an EA approach as an alternative to classic OC methods in ecological OCPs. The feasibility of an EA approach to atypical OCPs was further demonstrated, which may act to increase realism in OC applications. Further investigation in this regard is thus warranted by this study.Thesis (MS) -- Faculty of Science, Mathematics and Applied Mathematics, 202

On the role of metaheuristic optimization in bioinformatics

Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics

Maximizing Power Loss Reduction in Radial Distribution Systems by Using Modified Gray Wolf Optimization

This paper presents an optimal Distribution Network Reconfiguration (DNR) framework and solution procedure that employ a novel modified Gray Wolf Optimization (mGWO) algorithm to maximize the power loss reduction in a Distribution System (DS). Distributed Generation (DG) is integrated optimally in the DS to maximize the power loss reduction. DNR is an optimization problem that involves a nonlinear and multimodal function optimized under practical constraints. The mGWO algorithm is employed for ascertaining the optimal switching position when reconfiguring the DS to facilitate the maximum power loss reduction. The position of the gray wolf is updated exponentially from a high value to zero in the search vicinity, providing the perfect balance between intensification and diversification to ascertain the fittest function and exhibiting rapid and steady convergence. The proposed method appears to be a promising optimization tool for electrical utility companies, thereby modifying their operating DS strategy under steady-state conditions. It provides a solution for integrating more DG optimally in the existing distribution network. In this study, IEEE 33-bus and 69-bus DSs are analyzed for maximizing the power loss reduction through reconfiguration, and the integration of DG is exercised in the 33-bus test system alone. The simulation results are examined and compared with those of several recent methods. The numerical results reveal that mGWO outperforms other contestant algorithms