1.Self-Organization is based on


2.Stigmergy- Indirect interaction through communication with the environment.

The purpose of this engagement is to provide a comprehensive review of the current state of the art in Swarm Intelligence, with a particular emphasis on the role of stigmergy in distributed problem-solving. The scope of this engagement is broad and includes a variety of topics. However, to proceed, it is necessary to provide working definitions and the essential properties of swarm-capable systems, such as the fact that problem-solving is an emergent property of a system of primary agents. The stigmergy concept states that simple agents can interact with one another over a common channel without a centralized control system. As a result of applying this concept, they are querying individual agents reveals little or nothing about the system's emergent characteristics [12].

Consequently, simulation is frequently used to understand better the emergent dynamics of stigmergic systems and their interactions. Individual acts in stigmergic systems are frequently selected from a restricted behavioral repertoire in a probabilistic manner. It is the activities of the various agents that cause changes in the environment, for example, the deposit of a volatile chemical known as a pheromone. Other agents are alerted to the presence of this chemical signal, which results in a shift in the probabilistic selection of future actions.

The advantages of a system like this are self-evident. Generally speaking, the activity of a single agent is less important in a system where the actions of several agents are required for a solution to emerge. Stigmaria systems are resilient to the failure of individual agents while also responding exceptionally well to dynamically changing contexts, as demonstrated in the following example. When developing algorithms, they are making the most efficient use of available resources is usually a significant consideration. One other type of stigmaria system, the raid army ant model, uses pheromone-based signaling to forage for food and survive efficiently. Agents in an army ant system establish a forage front covering a large area, resulting in extraordinarily successful food discovery. This model has military value because it could be used to develop a system for searching for land mines, which is a problem that is all too common in some parts of the world and that this model could help solve. This model of military interest is the third stigmaria model of military interest, characterized by flocking or aggregation. Many simple agents can be programmed to travel across an environment filled with obstacles (and potentially dangerous threats) without the need for centralized control or supervision. The agents' positions and velocities serve as cues to the environment they are operating.

## **2.2 Swarm intelligence advantages and disadvantages**

There are several advantages of swarm Intelligence. In the following, some of them have been mentioned (**Table 1**) [13].

