## 3 Egyptian Stray Dogs Algorithm
### Natural Inspiration  
The ESDO algorithm also heads a list of optimizations inspired by animals. For example, PSO is inspired by the movement of flocks of birds or shoals of fish, in which particles (solutions) modify their positions according to their individual and collective memory [20; 21; 22; 23; 24]. GOA is inspired by the dynamics of grasshopper swarming [37; 38], HHO resembles cooperative hunting strategies from Harris hawks [30; 31], and HO emulates the territorial behavior of hippos while moving from water to land and vice versa [39]. The rest of the algorithms, namely Prairie Dog Optimization (PDO) and German Shepherd Dog Optimization (GSDO) model canine behaviors. PDO utilizes a frequency wave strategy like that of prairie dogs to perform efficient explorations [53], while GSDO emulates the trained behavior of search dogs [54]. Thus, making use of scent detection for targets.  
ESDO draws inspiration from the natural behaviors of stray dogs and utilizes this inspiration to solve optimization problems. Drawing inspiration from the way a stray dog defines its territory, scavenges opportunistically, adapts to environmental changes, and uses a complex social hierarchy, ESDO employs a different approach to navigating large search spaces [55]. These can be further categorized into scavenging, resting, social hierarchies, defensive actions, territoriality, and entertainment as shown in Figure 1. Similarly, these will guide the algorithm toward effective explorations and exploitations of search spaces for optimal solutions to the given problem.  
One such major behavior is territoriality, whereby stray dogs mark and protect areas to ensure availability and access to food, raising their survival chances. In ESDO, this is reflected in territorial behavior by focusing the search effort on the most promising areas while defending these areas from other less efficient algorithms.  
The entertainment behavior, through play, allows the stray dogs to range and gauge their environment for social bonding. In optimization, this improves exploration and non-convergence to allow a new set of approaches toward the solution. Similarly, the defending behavior against intruders allows ESDO to maintain concentrated searches around high potential areas and avoid override by less optimum solutions.  
Social hierarchy is an important aspect in the groups of stray dogs to ensure resource allocation goes well and that there is a semblance of order. ESDO mimics this by structuring solutions in the hierarchy, giving precedence to those with higher potential, which ensures that the allocation of resources is carried out in an efficient manner during optimization. Resting behavior, allowing dogs to recuperate, is also manifested in the algorithm through the avoidance of constant search to evade entrapment in local optima, making exploration more effective.  
Scavenging behavior is, finally, the rule of opportunistic searching for food by stray dogs in different environments; such a feature is crucially important in survival. In ESDO, this trait has been used to explore various sections of the search space with the aim of  
Figure 1: Stray dogs’ common behaviors.  
finding creative solutions that might otherwise remain inaccessibly hidden by traditional methods of exploration.  
In other words, the survivalist behaviors of stray dogs are rich in inspiring the ESDO algorithm. It makes use of territorial, playful, defensive, and scavenging behaviors to inspire both the phases of exploration and exploitation. Because of this, ESDO enjoys the merit of a flexible and adaptive search process that can uncover novel solutions that would remain undiscovered by traditional optimization techniques.