It is inspired by the foraging behavior of animals and birds. The algorithm aims to locate all the particles in a multi-dimensional space. Initially, the particles will be in a random position with random velocity and reach the appropriate place with the help of exploration and exploitation.

This algorithm is inspired by the foraging behavior of the honey bees. In a beehive, a scout bee searches for nectar. It communicates the distance, direction, and quality of the food to other bees through waggle dance. The fundamental objective is to determine and explore good sites within a problem's search space where each iteration searches additional good sites.

Ants algorithm is inspired by the pheromone communication of the blind ants to determine the best route. The principal objective of this algorithm is to make use of historic and heuristic information for constructing the candidate's solutions in a probabilistic manner.

Swarm Intelligence is used in numerous applications. Shortly, Swarm techniques will be used by NASA for controlling unmanned vehicles and planetary mapping. It is used for data mining and cluster analysis. It plays an indispensable role in the field of nanotechnology. Nanobots injected inside the body to kill the cancer tumors are controlled using Swarm Intelligence.

The use of swarm intelligence in telecommunication networks is explored based upon the ant-based routing technique. Mobile media and new technologies have the potential to improve the threshold for collective action because of swarm intelligence. In our day-to-day life, politicians and artists use swarm technology for crowd simulation.

A Swarm is a configuration of individuals who have chosen their own will to converge on a common goal. Swarm Intelligence can be used by any business when many individuals unite for a cause. When an organization is decentralized, optimized for trust and speed, Swarm Intelligence energizes individuals to shoot. It carries out different approaches in a parallel manner and furnishes a cost efficiency advantage.

Swarm Intelligence is explained clearly through the following illustration. There is a team of 5 members gathered to decide on a solution for a controversial problem. During the lunch break, they decided to eat any one of the cuisine's dishes. But, there were four different cuisines, such as Indian, Mexican, Chinese, and Italian.

To find out the overall satisfaction of the team, they conducted a poll. Two persons opted for Chinese, and the other three opted for Mexican, Indian, and Italian individually. Now, it is clear that Chinese food with the most votes must be the best choice. But, the most popular answer and the optimized answer are often different. What if the other three people hate Chinese food or are allergic or had very recently.

Imagine if this process is a swarm the process is the same as the above one. The swarm moves towards Chinese cuisine. But this is not a vote, and it is a real-time system with feedback loops where everyone can react and interact. One who opts for Mexican food might be willing to go to Indian cuisine, and another person may go anywhere as long as it's not Chinese switching multiple times. As these changes happen, our swarm intelligence algorithms watch the real-time behavior of every participant, can assess the strength of their conviction, determine how the swarm should move at every instant, and guess what the participants wrestle with the issue. The algorithms find a better path that optimizes the selective satisfaction of the group.

Imagine that instead of five participants, it was 10 or 20 or 50, and instead of choosing lunch, they will be deliberating which product features would perform best in the market or which marketing strategy would give the largest return. All kinds of organizations, both small and large used swarms to make better decisions by evaluating products and features to optimize ad campaigns from hiring better people to make more accurate sales forecasts.

To conclude, although there have been improvements in the optimization algorithms using swarm intelligence, there is not a unique algorithm that is successful in all types of optimization problems. At the same time, developing hybrid algorithms will also continue until the best combination of the algorithm is found. Indeed, of the above aspects, Swarm Intelligence plays a vital role in our day-to-day life!