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Metaheuristic has been utilized extensively to optimize power system. Meanwhile, the no-free-lunch (NFL) theory becomes the major consideration in the massive development of metaheuristic as there is not any ideal metaheuristic can solve all problems superiorly. Based on this problem, this work is aimed at introducing a novel metaphorfree swarm-based metaheuristic called iteration-controlled mixture optimizer (ICMO). ICMO contains three directed searches where the reference in each search is constructed by two entities. The first reference is the mixture between the finest entity and the mean of the finer entities. The second reference is the mixture between the finest entity and a randomly chosen entity. The third reference is the mixture between the finest entity and any generated entity within space. The portion between the first and second entities in each reference is controlled by the iteration. Then, ICMO is compared with five new swarm-based metaheuristics: attack leave optimization (ALO), total interaction algorithm (TIA), fully informed search algorithm (FISA), walrus optimization algorithm (WaOA), and ono-to-one based optimization (OOBO). The assessment result shows that ICMO is better than ALO, TIA, FISA, WaOA, and OOBO in 15, 13, 20, 12, and 20 functions out of 23 functions respectively. Then, ICMO is also challenged to solve the economic load dispatch (ELD) problem in the Java-Bali electricity system in Indonesia. The result shows that ICMO is competitive compared to these five metaheuristics in solving this practical problem. The result shows that the range between the best and worst metaheuristics in this problem is narrow as it represents the integer-based problem. [ABSTRACT FROM AUTHOR]

One critical problem in Indonesia's national joint courses program, initiated by the ministry of education and culture of Indonesia, is the lecturer-course assignment problem. Although the lecturer-course assignment problem has been studied widely as part of the education timetabling problem, no existing lecturer-course assignment model suits this circumstance. The new cases in this program are as follows. First, this program is conducted online. Second, the participants are students and lecturers from different universities. Based on this problem, this work proposes a novel lecturer-course assignment model that suits this program. The lecturers' preferred courses and timeslots become hard constraints. The model has three objectives: (1) maximizing the educational quality, (2) maximizing the lecturers' time preference, and (3) minimizing the number of unserved classes. This model is developed by using integer linear programming and optimized by using cloud theory-based simulated annealing. This proposed model is then compared with the four previous lecturers-course assignment models. The first model concerns about minimizing the number of unserved classes, while the second model focuses on maximizing the education quality. The maximum number of classes per course for every lecturer is considered in the third model while balancing the lecturer’s load (teach, research, community service) is the feature of the fourth model. The research concludes that the proposed model is appropriate for lecture-course assignment in Indonesia’s national joint courses program compared to the previous models. Based on the simulation result, the proposed model performs moderately in education quality and several unserved classes. Meanwhile, the proposed model is the best in the timeslot preference aspect by creating a 25% to 28% higher total timeslot score than other previous models. [ABSTRACT FROM AUTHOR]