# CDDO-HS: Child Drawing Development Optimization-Harmony Search Algorithm
## 1 Introduction  
The term \"optimization\" refers to the method used to pick the optimal solution from a pool of possibilities. Each process may be optimized, and many difficult issues in management, economics, science, and technology can be cast as optimization problems [1]. Due to the computational effort required by conventional numerical optimization methods, it is most likely not possible to execute a thorough search of the optimal solution space for challenging engineering optimization problems. In the past two decades, there has been growing interest in the development of alternatives to traditional, mathematically derived, and gradient-based optimization strategies. This interest stems from a desire to discover more efficient methods for attaining optimal results [2]. In this scenario, heuristic optimization algorithms are better approaches for resolving difficult engineering optimization problems [3]. Due to this, heuristic optimization approaches have been developed, which can be applied to problems that derivative methods are unable to solve. As a result, a multitude of heuristic and metaheuristic algorithmic techniques has been created [4]. Metaheuristics are effective optimization strategies that have gained popularity in a variety of disciplines, including engineering, finance, and applied science [5, 6]. The vast majority of cutting-edge metaheuristics were developed in the years leading up to the year 2000, when these algorithms were still considered to be \"classical\" metaheuristicalgorithms [7]. The most common classical metaheuristics include genetic algorithms (GAs), introduced by [PERSON] in 1988, which are a population-based approach that mimics natural selection and genetics to search for optimal solutions [8]. [PERSON] and [PERSON] proposed particle swarm optimization (PSO) in 1995, which uses a swarm of moving particles in the search space to find the best solution [9]. [PERSON] et al. introduced simulated annealing (SA) in 1983 as a probabilistic method that employs the physical process of annealing to seek out the optimal solution [10]. Tabu search (TS), introduced by [PERSON] in 1986, is a local search-based metaheuristic that uses a taboo list to avoid returning to recently visited solutions [11]. Ant colony optimization (ACO), proposed by [PERSON] and [PERSON] in 1992, uses a population of artificial ants that deposit pheromones to communicate and find the optimal solution [12]. Notwithstanding the successes of classical metaheuristic algorithms, novel and innovative evolutionary techniques have been developed lately. Throughout this age, metaheuristic algorithm research has resulted in the introduction of a substantial number of new metaheuristics inspired by evolutionary or behavioral processes. This new wave of metaheuristic techniques frequently delivers the best solutions for some benchmark problem sets that have yet to be addressed [13]. These metaheuristics have been applied to various optimization problems and have demonstrated their effectiveness and efficiency in finding near-optimal solutions in a reasonable amount of time [14]. Based on the criteria, there are many classifications of inspired metaheuristic algorithms [15; 16; 17; 18]. Therefore, according to [18], five groups can be used to broadly categorize inspired metaheuristic algorithms:  
1. Evolution-based methods, such as the genetic algorithm (GA) [8], memetic algorithm (MA) [19], genetic programming (GP) [8], biogeography-based optimizer (BBO) [20], and virulence optimization algorithm (VAO) [21]. 2. Swarm-based methods, such as krill herd (KH) [22], fitness-dependent optimizer (FDO) [23], ant colony optimization (ACO) [12], bacterial foraging behavior (BFO) [24], particle swarm optimization (PSO) [9], cuckoo optimization algorithm (COA) [25], dolphin optimization algorithm (DOA) [26], dragonfly algorithm (DA) [27], bat algorithm (BA) [28], group search optimizer (GSO) [29], ant nesting algorithm (ANA) [30], and donkey and smuggler optimization (DSO) [31]. 3.