# CDDO-HS: Child Drawing Development Optimization-Harmony Search Algorithm
## 1 Introduction
Physics-based methods, such as black hole (BH) [32], ray optimization (RO) [33], charged system search (CSS) [34], simulated annealing (SA) [10], big-bang-big-crunch (BBCB) [35], gravitational local search (GLSA) [36], central force optimization (CFO) [37], thermal exchange optimization (TEO) [38], and the water strider algorithm (WSA) [39]. 4. Human-based methods, such as the harmony search (HS) [40], group learning algorithm (GLA) [41], firework algorithm (FA) [42], league championship algorithm (LCA) [43], learner performance-based (LPB) [44], interior search algorithm (ISA) [45], mine blast algorithm (MBA) [46], soccer league competition (SLC) [47], exchange market algorithm (EMA) [48], and the social-based algorithm (SBA) [49]. 5. Natural disaster algorithms, such as the earthquake algorithm (EA) [50]. Child drawing development optimization (CDDO), as a human-based metaheuristic algorithm, is one of the latest metaheuristic algorithms. CDDO depends on the behavior of children learning to draw at an early age [51]. This algorithm demonstrated superior performance in locating the optimal global solution for optimization problems tested using classical benchmark functions. Its results were compared to multiple cutting-edge algorithms, such as PSO (particle swarm optimization) [9], DE (differential evolution) [52], the WOA (whale optimization algorithm) [53], GSA (gravitational search algorithm) [36], and FEP (fast evolutionary programing) [54]. The effectiveness of the CDDO was evaluated. This demonstrated that the CDDO was exceptionally resilient when acquiring a new solution. It also employs a random search mechanism to change the position to find the best solution [13]. According to [51], CDDO performs well in terms of convergence time and balancing exploration and exploitation. Although the method performs well compared to other algorithms, several restrictions affect how well it performs in particular circumstances. When an algorithm, for instance,becomes trapped in local optima, it finds a solution that is optimal within a specific region of the search space, but not necessarily the optimal solution overall. As the encircling mechanism is used in the search space, CDDO's performance in escaping from local solutions is poor, and convergence and speed are inefficient in both. This means that CDDO has issues relating to the balance between exploration and exploitation. In addition, not improving the best solution more effectively is a problem associated with the encircling mechanism, and the CDDO exploitation phase must be enhanced to obtain better solutions [14; 51]. The following are the primary motivations for hybridizing CDDO with HS in this paper:  
1. Due to CDDO employing an encircling search mechanism, thus, suffering from the inability to avoid local optima. 2. CDDO performs poorly during the exploitation phase. 3. Improving the CDDO solution after each iteration is insufficient. The authors came up with the hybridized algorithm as a solution to the CDDO concerns that were described previously, as well as the standard harmonic search algorithm's great exploitability in the execution of multimodal benchmark functions. As a consequence of this, the authors decided to go with a hybrid approach that combines CDDO and global HS to provide higher performance in the exploitation phase with global HS, particularly when tested using unimodal benchmark functions. Thus, the purpose of this paper is to propose a hybridized approach to overcoming CDDO problems by employing two effective mechanisms: The first mechanism for improving CDDO performance is updating the pattern memory iteratively by using the standard HS mechanism during the exploitation phase and comparing new experiences (pattern memory) with the current child's drawings. The second is to save the best solution for each iteration and then compare each new solution to the best solution in the exploration phase. If the outcome is better than the best solution, the child's drawings are changed; otherwise, they remain in their current drawings.