**5. Swarm Search with Communication**

While decentralized swarms have been the main focus of swarm-based search algorithms due to their scalability and simplicity, there are also several promising works that utilize synchronization or communication among the agents. These parallel swarms often employ communication to enhance the efficiency of the search process, such as parallel ant colony optimization, parallel particle swarm optimization, and other parallel metaheuristic approaches.

Parallel ant colony optimization (PACO) [131] is an example of a parallel swarm algorithm that utilizes communication among agents. PACO algorithms allow multiple agents to cooperate by sharing pheromone information, which helps in quickly identifying the optimal solution. For instance, PACO has been used in multi-robot coverage problems, where a group of robots are required to explore an unknown environment while avoiding collisions with each other. By sharing pheromone information, the robots can quickly converge to a solution, even in complex and large environments [22,132].

Parallel particle swarm optimization (PPSO) [133] is another example of a parallel swarm algorithm that uses communication among agents. PPSO is a variant of particle swarm optimization (PSO) that allows multiple agents to communicate with each other to improve the search process. For instance, PPSO has been used to optimize complex systems such as power grids, where the agents need to communicate to efficiently manage the distributed resources [134].

In cases where decentralized swarms may not be sufficient, parallel swarms can be beneficial. For example, in situations where the problem space is complex, the search space is vast, and the search process is time-critical, parallel swarm algorithms can offer a significant advantage over decentralized swarms. In such scenarios, communication among agents can help to identify the optimal solution more quickly and efficiently.

However, one of the main drawbacks of parallel swarm algorithms is the increased complexity of the communication mechanisms, which may require significant computational resources [135]. Additionally, communication can also lead to increased synchronization overhead, which may impact the scalability of the algorithm. Thus, in cases where the problem space is relatively simple, decentralized swarm algorithms may still be a better choice.

While decentralized swarms remain the main focus of swarm-based search algorithms, parallel swarm algorithms that utilize communication among agents have shown significant promise in enhancing the efficiency of the search process. These algorithms have been used in various applications, such as multi-robot coverage problems and power grid optimization. However, the increased complexity of communication mechanisms and synchronization overhead should also be considered when deciding on the appropriate approach for a given problem.

#### **6. Opportunities and Future Research**

SI and swarm systems have received considerable attention in recent years due to their potential for solving complex problems in various fields, such as robotics, optimization, and network design. As a result, there are numerous opportunities for future research in this area.

One promising avenue for future research is the development of more sophisticated algorithms and models for SI, while current approaches have shown promise, there is still much to be done in terms of improving the efficiency and adaptability of swarm systems [136]. Researchers may explore new ways to optimize the communication and coordination of swarm agents, or develop new approaches for dealing with the inherent uncertainty and complexity of real-world environments [137].

Another important area for future research is the application of SI to real-world problems, while there have been many successful demonstrations of swarm systems in laboratory settings, there is a need for more research on how to apply these systems to real-world problems. This may involve working with industry partners to develop practical solutions that can be deployed in the field, or collaborating with government agencies to address societal challenges such as disaster response or urban planning [138,139].

In addition to these technical challenges, there are also important ethical and social considerations to be addressed. As swarm systems become more advanced and pervasive, there may be concerns around issues such as privacy, security, and control. Researchers may need to explore new ways to address these concerns, such as developing transparent and accountable algorithms, or working with policymakers to establish appropriate regulations and standards [140].

Overall, there are numerous opportunities for future research in SI and swarm systems. By continuing to explore these systems and their potential applications, researchers can help to unlock new solutions to complex problems and contribute to the advancement of science and technology.

#### **7. Conclusions**

The study of SI has revealed that even seemingly simple organisms, such as ants, can exhibit complex and sophisticated collective behaviours when allowed to work together in a synergistic manner. This insight has led researchers to investigate the potential for applying this approach to artificial intelligence and robotics, with promising results.

In this Special Issue, a number of research studies have been presented that demonstrate the power of SI in producing complex and adaptive behaviours. By studying the ways in which ants and other social insects cooperate and communicate with one another, researchers have been able to develop algorithms and models that can be applied to a wide range of problems.

One of the key insights from these studies is that individual agents within a swarm do not necessarily need to be highly intelligent or even aware of the larger goals of the group. Rather, by following simple rules and responding to local cues, they can collectively produce intelligent and adaptive behaviours that emerge at the swarm level.

This approach has numerous potential applications, from optimizing traffic flow to coordinating the movements of swarms of robots in search and rescue operations. By harnessing the power of SI, researchers are exploring new ways to tackle complex problems that would be difficult or impossible for any individual agent to solve alone.

Overall, the research presented in this Special Issue provides compelling evidence that even the simplest organisms can exhibit remarkable intelligence and adaptability when working together in a synergistic manner. By taking inspiration from nature, researchers are opening up exciting new avenues for developing advanced technologies that can benefit society in countless ways.

In summary, let us cite a statement made by a scientist after watching an ant making his laborious way across a wind-and-wave-moulded beach [141]:

"An ant, viewed as a behaving system, is quite simple. The apparent complexity of its behavior over time is largely a reflection of the environment in which it finds itself."

Such a point of view, as well as the results of the research presented in this Special Issue, lead us to believe that even simple, ant-like beings, when allowed to synergically collaborate, can yield a complicated, adaptive and quite efficient macroscopic behaviour, in the intelligent swarm-level scope.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
