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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]

There are two problems in the development of swarm-based metaheuristic. First, there are not any metaheuristic is able to solve all problems superiorly. Second, the cross-dimension mapping between the entity and its reference during performing the directed search is rare to find. Based on these problems, this work introduces a new metaphorfree swarm-based metaheuristic called the triple-shake algorithm (TSA). As its name suggests, TSA consists of three directed searches. The reference in the first search is the balance mixture between the finest entity and the member of permutation set. The reference in the second search is the balance mixture between the finest entity and a randomly chosen entity. The reference in the third search is the finest entity only. But the cross-dimension mapping is performed in this third search with 50 percent probability. In the benchmark assessment, TSA is compared with zebra optimization algorithm (ZOA), walrus optimization algorithm (WaOA), migration algorithm (MA), total interaction algorithm (TIA), and one-toone based optimization (OOBO). The result indicates the dominance of TSA among its comparators. TSA is better than ZOA, WaOA, MA, TIA, and OOBO in 21, 19, 17, 19, and 22 functions consecutively out of 23 functions. [ABSTRACT FROM AUTHOR]

In recent years, there is massive development of new metaheuristics as stochastic methods. Meanwhile, there is not any metaheuristics is powerful to handle all problems as stated in the no-free-lunch (NFL) theory. Based on this circumstance, this paper introduces a new swarm-based metaheuristics with the main strategy moving toward the resultant of better swarm members and avoiding the resultant of worse swarm members called group better-worse algorithm (GBWA). It consists of five searches: moving toward the best swarm member, moving toward the resultant of better swarm members, moving away from the resultant of worse swarm members, searching locally, and jumping to the opposite area. GBWA is then evaluated in three ways. The first evaluation is a comparative evaluation where GBWA is compared to five recent metaheuristics: coati optimization algorithm (COA), average and subtraction-based optimization (ASBO), clouded leopard optimization (CLO), total interaction algorithm (TIA), and osprey optimization algorithm (OOA). The second evaluation is the individual search evaluation. The third evaluation is hyperparameter test. The collection of 23 classic functions is chosen as the use case in all evaluations. The result of the first evaluation shows that GBWA is better than COA, ASBO, CLO, TIA, and OOA in 20, 21, 20, 21, and 21 functions consecutively. Meanwhile, the result of the second evaluation shows the equal contribution between the motion toward the best swarm member and the motion toward the resultant of better swarm members. [ABSTRACT FROM AUTHOR]

A new swarm-based metaheuristic, namely the enriched coati osprey algorithm (ECOA), is proposed in this paper. As its name suggests, ECOA hybridizes two new metaheuristics, the coati optimization algorithm (COA) and the osprey optimization algorithm (OOA). ECOA is constructed by five searches performed sequentially by the swarm members. The first three are directed searches, while the last two are neighborhood searches. All three directed searches are adopted from COA and OOA. Meanwhile, the four-bordered neighborhood search is developed based on a new approach. During the assessment, ECOA was challenged to overcome the set of 23 functions and contended with five new metaheuristics: total interaction algorithm (TIA), golden search optimization (GSO), average and subtraction-based optimization (ASBO), COA, and OOA. The result shows that ECOA outperforms TIA, GSO, ASBO, COA, and OOA in 16, 23, 18, 21, and 21 functions. Meanwhile, the individual search test result shows that the directed searches perform better than the neighborhood searches. Moreover, the directed search toward the best member becomes the most dominant search. [ABSTRACT FROM AUTHOR]

This study designs a new stochastic optimization i.e., metaheuristic technique, namely run-catch optimizer (RCO). RCO provides a distinct mechanism regarding the diversification-intensification strategy. Each member runs two sequential activities in every iteration. The first activity is running and the second one is catching. Each activity generates a seed. In the first activity, a virtual best member moves away from the corresponding member to become the first seed. The second seed is generated along the way between the corresponding member and the first seed in the first activity. If both seeds fail to improve, the member conducts a random search to find a new member. Otherwise, the better seed replaces the corresponding member. Then, RCO is challenged to handle both theoretical and real-world optimization problems. The classic 23 functions represent theoretical problems, while the outsourcing optimization problem represents the practical problem. In these simulations, RCO is confronted with five other algorithms: grey wolf optimizer (GWO), particle swarm optimization (PSO), marine predator algorithm (MPA), Komodo mlipir algorithm (KMA), and pelican optimization algorithm (POA). The result shows that RCO is better than POA, KMA, MPA, GWO and PSO in optimizing 20, 21, 13, 12, and 21 functions consecutively. Meanwhile, RCO is better than PSO, GWO, and KMA, but worse than MPA and POA in optimizing the outsourcing problem. [ABSTRACT FROM AUTHOR]

This work presents a hybrid metaheuristic, namely a hybrid marine predator algorithm - hide object game optimization (MPA-HOGO). MPA-HOGO is built by combining two shortcoming metaheuristics: marine predator algorithm (MPA) and hide object game optimization (HOGO). The distinct strategy in MPA-HOGO is as follows. MPA-HOGO utilizes an exploration-to-exploitation strategy gradually as the iteration goes on. It uses the global best solution evaluated in every iteration to ensure that the final solution comes from the best solution found during the iteration. It also eliminates the normalized score of every solution to reduce the computational process. This metaheuristic is then tested by solving 23 classic functions. MPA-HOGO is benchmarked with particle swarm optimization (PSO), Komodo mlipir algorithm (KMA), MPA, and HOGO. The result indicates that MPA-HOGO is superior among these metaheuristics. MPA-HOGO is also competitive in solving practical optimization problems. Through simulation, MPA-HOGO produces 13%, 7%, and 2% lower total costs than PSO, MPA, and HOGO respectively in solving inventory management problems in the vendor-managed inventory system. [ABSTRACT FROM AUTHOR]

This paper demonstrates a novel simple metaheuristic algorithm, the cone search (CS). This name comes from the distinct strategy of CS in searching the suboptimal solution. In the early iteration, its local search space is wide to facilitate exploration. The local search space is reduced linearly during the iteration so that the exploration changes to exploitation gradually. As a swarm intelligence, CS contains several autonomous agents and a collective intelligence called memory. This memory consists of several best solutions. In this work, CS is challenged to find the global optimal of 23 benchmark functions. In the simulation, CS is compared with four metaheuristic algorithms: particle swarm optimization (PSO), marine-predators algorithm (MPA), Komodo mlipir algorithm (KMA), and pelican optimization algorithm (POA). The result shows that CS performs well in solving these 23 functions. Moreover, it can find the global optimal of six fixeddimension multimodal functions: Branin, six hump camel, Hartman 6, Shekel 5, Shekel 7, and Goldstein price. CS beats PSO, MPA, KMA, and POA in solving 23, 22, 21, 22 functions respectively. [ABSTRACT FROM AUTHOR]

This work proposes a new coordinated ambulance routing model suitable for implementation during the COVID-19 pandemic. This model is different from the existing model, where it was conducted uncoordinatedly, so that mismatch between supply and demand may occur. In general, high number of unserved requests and travel distance are unwanted. Therefore, this work proposes a model consisting of three steps: hospital-patient allocation, ambulance-patient dispatching, and ambulance pickup-delivery sequencing. The proposed model consists of two objectives: minimizing the number of unserved patients and minimizing total travel distance. It is developed by using cloud-theory-based simulated annealing. The simulation result shows that the proposed model outperforms the existing uncoordinated model in number of unserved patients, total travel distance, and average travel distance. It creates zero unserved patients if the total number of patients does not surpass the total number of slots in all hospitals. It produces 12 to 19 percent lower total travel distance and 27 to 29 percent lower average travel distance than the uncoordinated model. [ABSTRACT FROM AUTHOR]