## 1 Introduction
### Literature Review of Categories of Metaheuristic Algorithms  
Simulated annealing and the gravitational search algorithm (GSA) are two examples of metaheuristic algorithms based on physics. Simulated annealing is based on the physical process of annealing in which materials are heated and cooled slowly to decrease defects and minimize the system energy [18]. The GSA [19] is inspired by [PERSON]'s law of gravity and motion. In the GSA, agents are modeled as objects. The performance of an object is measured by its mass. Objects attract each other due to gravity force. Gravity force makes all objects move toward heavier objects. An object with heavier mass corresponds to a better solution. Swarm-based metaheuristic algorithms are developed based on the concept of swarm intelligence, which refers to the collective learning and decision-making behavior of self-organized entities in natural or artificial systems. Well-known examples of swarm-based metaheuristic algorithms include particle swarm optimization (PSO) [20], the firefly algorithm (FA) [21], artificial bee colony (ABC) [22], ant colony optimization (ACO) [23], etc. ACO is a metaheuristic inspired by the foraging behavior of ant colonies. A variety of strategies are used by ants to forage for food. One strategy is to leave pheromones to mark their path to food. Ants mark some favorable paths by depositing pheromones on the ground. These favorable paths are followed by other ants of the colony to facilitate selection of the shortest route with the help of the pheromone. PSO is an approach inspired by the social behavior and movement of organisms in a bird flock. The standard PSO algorithm is initiated by creating a population of solutions called particles. Each particle adjusts its position and velocity according to its individual personal best solution and the global best solution to improve the solution. If a particle finds an improved solution that outperforms the current personal best solution, the improved solution will replace the personal best solution. Similarly, if the improved solution found by the particle outperforms the current global best solution, the improved solution will replace the global best solution. The PSO algorithm repeats the above process until the termination condition is satisfied. The flashing characteristics of fireflies inspired the creation of the FA: one firefly attracts another one according to attractiveness proportional to the brightness which decreases as their distance increases, where the brightness of a firefly depends on the objective function. A firefly will move randomly in case it cannot find a brighter one. Evolutionary-based metaheuristic algorithms mimic the phenomena of biological evolution which is similar to the natural selection theory introduced by [PERSON]. They borrow the reproduction, mutation, recombination and selection mechanisms from biological evolution to evolve the potential solutions. The genetic algorithm (GA) [24] and differential evolution (DE) [25] are two widely used evolutionary algorithms. The GA starts with a population of candidate solutions (called individuals). Evolution is an iterative process and each iteration is called a generation. For every individual, its fitness is evaluated based on the fitness function of the optimization problem. The population in a generation evolves through applying a combination of genetic operations, including crossover and mutation to form a new generation, to attempt to create better solutions. The GA selects the most fit individuals stochastically from the current population to play the role of parents. An individual in the next generation may be created by applying crossover operations to combine two parents or applying mutation rules to individual parents. The GA terminates either after a maximum number of generations has been executed or the solution quality has reached a satisfactory fitness level. The DE algorithm includes the operations of initialization, mutation, crossover and selection to iteratively improve the solution quality.