## 1 Introduction
### Literature Review of Categories of Metaheuristic Algorithms
In the initialization step, a population of individuals is generated. In the mutation step, a mutation strategy is applied to generate a mutant vector for each individual in the population. The crossover step generates a trial vector based on the mutant vector and the original individual. In the selection step, the trial vector will replace the original individual if it is better than the original individual. Game-based metaheuristic algorithms simulate the behavior of different entities such as players, coaches and other individuals in games and the behavior of different entities is governed by the underlying rules of the games. Football game-based optimization (FGBO) [26] and the volleyball premier league (VPL) algorithm [27] are two well-known examples of game-based metaheuristic algorithms. In FGBO, a population of clubs is generated initially, where each club consists of a number of players. The algorithm calculates the power of each club and determines the best club. It then iteratively follows a procedure with four phases: (a) league holding, (b) player transfer, (c) practice and (d) promotion and relegation to update the status of the clubs and improve the solution quality until the termination condition is satisfied. The VPL algorithm is inspired by the coaching process during a volleyball match. In a volleyball game, a team consists of players, substitutes and the coach. The formulation of a team includes several active players in the active part and standby players in the passive part. A coach decides whether to replace an active player with a standby player in the passive part. The algorithm determines the winning team and the losing team based on the power index and applies different strategies to update the best team. The algorithm removes the k-worst teams, adds new teams to the league, applies the transfer process and updates the best team iteratively until the termination condition is satisfied. Human-based metaheuristic algorithms are developed based on inspiration from human activities. Examples of human-based metaheuristic algorithms include teaching-learning-based optimization (TLBO) [28], poor and rich optimization (PRO) [29], human mental search (HMS) [30] and driving training-based optimization (DTBO) [17]. TLBO is a population-based algorithm and consists of two phases: Teacher Phase and Learner Phase. The algorithm learns from the teacher in the Teacher Phase whereas it learns through the interaction between learners in the Learner Phase to improve the quality of solutions. PRO is a multi-population-based algorithm. It is inspired by the mechanisms that are used by the poor and the rich to improve their economic situation. Initially, there are two populations: a rich population and a poor population. Each individual in the poor population tries to improve his/her status and reduce the gap between the rich population and poor population by learning from an individual in the rich population. Each individual in the rich population tries to increase the gap between the rich population and poor population by observing them and acquiring wealth. The rich population and poor population are updated after all the individuals have taken actions to try to improve their status. HMS is inspired by strategies to explore the bid space in online auctions. It simulates human behavior in online auctions and consists of three steps: (1) the mental search for exploring around each solution, (2) grouping bids to determine a promising region and (3) moving the bids toward the best strategy. DTBO is inspired by the learning process in a driving school. It is divided into three phases: (1) training by the driving instructor, (2) patterning of students from instructor skills and (3) practice. The best individuals selected from the population play the role of driving instructors and the rest play the role of learner drivers. In the training in the driving instructor phase, a learner driver must choose the driving instructor and learns from that instructor.