Swarm Intelligence: The Nature-Inspired Artificial Intelligence Which Uses both

Posted by Ajinkya on February 10th, 2021

The idea of Swarm Intelligence is not very far from the traditional idea of crowds. Crowds are basically groups of people or animals who work together to achieve a common goal. In the case of swarms, humans take the form of the swarms while the animals become the resources or the brains of the swarm. As in the case of the human swarm, the collective intelligence is used to accomplish the collective goal.

Swarm intelligence is all about applying the collective intelligence of a group of computers to solve a problem in a self-organized manner. Basically, it is an integrated range of computer science method, which uses both supervised and unsupervised learning. As with the traditional concept of supervised learning, the logic of the application is fed into the algorithm through a supervised memory. The logic is fed into the algorithm is typically either a supervised backtesting or an unsupervised backtesting. Therefore, swarm intelligence refers to both supervised and unsupervised learning.

Traditional logic networks such as Backpropagation and the neural network models are the backbone of swarm intelligence. In the context of the latter, the logic is fed into the simulation rather than directly into the real software. Such an approach means that the agent will be able to make use of past inputs to make new decisions as well as learn new rules from the simulation. Thus, the agents would be able to learn the parameters of the software in a realistic way. The agents can also be given pre-programmed rules for every task in the enterprise, which can be evaluated along the network to get the best results.

The algorithms that are part of the swarm intelligence are generally more complex compared to traditional optimization algorithms. The complexity comes from the requirement for support services along with the need for robustness. Typically, the agent will make use of the computing resources of its neighbors in order to accelerate the decision making process. Therefore, it may not always be possible to guarantee the best results. Furthermore, the algorithms for optimization that uses the supervisory processing power of a human operator are complex and therefore costly.

Swarm algorithms are used in a variety of areas such as online advertising, web search engines, consumer protection, and pharmaceuticals to name a few. The reason why these algorithms have proven so useful is that they allow agents to take on many tasks simultaneously. The flexibility provided by these algorithms allows these agents to adapt quickly to changing conditions. Additionally, the ant colony optimization algorithm provides for flexibility as the number of agents increases without the decrease of performance capabilities.

The cuckoo search algorithm is also used to support swarm intelligence. The cuckoo search algorithm was developed by Douglas Crookes and Frank Weinberg as part of the research team that developed the well-known java programming language java. Although this algorithm has not been utilized to the extent that the swarming intelligence algorithm can be utilized, it is still very useful. In fact, the cuckoo search algorithm is so good that even though java code is written in another programming language, many programmers still find it to be easy to work with. The availability of a swarming agent coupled with the ease of working with java code has made the cuckoo search algorithm one of the most popular methods of achieving swarm intelligence.

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