Observing the intricate ways that nature works can give us plenty of relevant ideas to develop solutions to combat our own problems. The reach of AI is far, and so too is its influencers, with nature helping to drive developments in the technology.
Already, many algorithms mimic natural phenomena such as how animals organize their lives, how they use instincts to survive, how generations evolve, how the human brain works, and how we as humans learn.
Computer scientists have even designed many AI algorithms by imitating human intelligence with machines.
In this article, we will take a deep dive into several different AI algorithms that are inspired by nature.
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The term Swarm intelligence, first introduced by G. Beni and J. Wang in 1989, describes a self-organization system formed by a group of individuals that interact with each other and their environment.
Each individual in the group serves as a simple agent that follows a small set of rules, and, without centralized control, the agents work together to make intelligent decisions.
The developers of the swarm intelligence algorithm based it off the behaviors of animals such as ants, bees, and birds.
These different animals all have low levels of intelligence when alone, but, when working together, they can all build complex organizational systems and survive.
Engineers have applied this algorithm in robotics, creating robots that execute simple tasks on their own and collaborate with other robots to resolve complex problems.
Several swarm intelligence algorithms have been inspired by specific types of animals such as bees, as seen in the following:
Ant Colony Optimization algorithm (ACO)
Ants use a form of swarm intelligence to survive. While each ant has a very small brain, they work together successfully to support their colonies.
In particular, some species of ant deposit pheromones on the ground to mark the optimal path for other ants to follow.
Inspired by these ants’ intelligence, researchers developed the effective general purpose Ant Colony Optimization algorithm (ACO).
Artificial Bee Colony algorithm
Introduced by Dervis Karaboga in his paper ¨An idea based on honey bee swarm for numerical optimization,” the artificial bee colony algorithm is an optimization algorithm based on the behavior of honey bees.
In bee colonies, different individuals have different roles such as employed bees, which gather food from a particular source, and unemployed bees, which look for new sources of food (scouts) or wait in the hive (onlookers).
Bacterial Foraging Optimization algorithm (BFO)
Another nature-inspired swarm intelligence optimization algorithm, BFO mimics the foraging behaviors of bacteria that obtain the maximum amount of energy during their search.
In 2010, Xin-She Yang introduced the Bat Algorithm based on microbats’ echolocation and bio-sonar abilities. The microbats use sounds to detect prey, avoid obstacles, and perceive their surroundings.
Firefly Algorithm (FA)
The Firefly algorithm is a metaheuristic algorithm inspired by the social behavior of fireflies and their communication through bioluminescent flashes.
Collective Animal Behavior (CAB)
The CAB algorithm takes inspiration from animals that form groups to benefit from the advantages of a community.
Animals who work in groups often increase their efficiency when finding food, optimize large distance migration patterns, and avoid predators better.
The CAB algorithm emulates a group of animals that follow rules based on collective behaviors.
Particle Swarm Algorithm
Many researchers have also developed algorithms inspired by human behavior. For instance, the particle swarm algorithm simulates how human societies process knowledge.
As an optimization algorithm, it tries to improve a candidate solution by assessing what individuals know and what their neighbors know.
Nature-inspired optimization algorithms
In addition to swarm behavior-inspired optimization algorithms, researchers have also developed other optimization algorithms based on natural biological, physical, and chemical systems.
Collectively, these algorithms are called Nature-inspired optimization algorithms (NIOAs). Let's look at several popular examples.
Gravitational search algorithm (GSA)
The paper ´GSA: A Gravitational Search Algorithm´ proposes a heuristic optimization algorithm based on the Newtonian law of gravity:
“Every particle in the universe attracts every other particle with a force that is directly proportional to the product of their masses and inversely proportional to the square of the distance between them”.
Gravitational search algorithm
Cuckoo search algorithm
Introduced in 2009 by Yang and Deb to solve global optimization problems, the cuckoo search algorithm takes inspiration from the brood parasitism of birds in the cuckoo species.
By leaving their eggs in the nests of other birds, certain cuckoo birds developed the ability to mimic the colors and patterns of other species’ eggs so the hosts cannot recognize the foreign eggs.
The evolutionary algorithm (EA) is based on Darwin’s theory of evolution and imitates the natural selection processes of living organisms.
Starting with a random population, the algorithm evaluates the group’s fitness (how well the solution is) to solve a certain problem and selects the best individuals for reproduction.
This cycle continues with the population until the algorithm reaches an optimal solution.
A category of EAs, genetic algorithms solve optimization and search problems with methods inspired by genetic mutations, crossovers, and natural selection.
First, a mutation is introduced into a population to create diversity. Next, crossovers combine two individuals from the population to create a new offspring.
Then, after each iteration, a fitness function evaluates the new generation and selects the best candidates.
Neural networks, or artificial neural networks (ANNs), are machine learning algorithms that mimic the way human neurons communicate with one another.
Neural networks possess several different layers, including an input layer, one or more hidden layers, and an output layer.
Each node in a neural network represents an artificial neuron that connects to other nodes with a specific weight. When activated, the artificial neuron sends data to the next layer of the network.
Animals and humans learn by trial and error with penalties and rewards. For instance, when a child learns to walk, they fail if they fall (penalty), and succeed if they don´t fall (reward).
If they fall, they stand up and try again until they eventually walk without error. This is an example of reinforcement learning.
In the field of machine learning, reinforcement learning algorithms execute a sequence of tasks in a certain environment with the objective of maximizing the rewards and minimizing penalties.
With each iteration, the algorithm learns what works best given the conditions in the environment. Reinforcement learning differs from supervised learning since the training data does not provide the answers associated with the inputs.
Instead, programmers set the rules for the rewards and penalties, and the reinforcement agent figures out how to perform a given task and obtain the most rewards.
Nature performs many complex functions that scientists are still working to understand. By mimicking certain animal behaviors, we can find methods to resolve problems in our lives and also gain a better understanding of nature.
For instance, understanding how the brain works helps improve artificial neural networks, and, by leveraging artificial neural networks in neuroscience research, we can discover patterns that help us understand the brain better.
Learning from nature, we can develop highly efficient solutions to solve a variety of complex problems.
What are your thoughts on how nature is influencing AI algorithms? Let us know in the comments!