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Bacterial chemotaxis

As explained in Optimization Algorithms: AI techniques for design, planning, and control problems

Bacterial chemotaxis is the movement of bacterial cells through chemical concentration gradients toward attractants or away from repellents.

Bacterial chemotaxis is the movement of bacterial cells through chemical concentration gradients, toward attractants or away from repellents. Bacteria use chemotactic signaling to cooperate, self-organize, and adapt to their environment. section 9.1

Chemotaxis matters in swarm intelligence because:

  • E. coli uses chemotaxis while foraging for resources. section 9.1
  • This collective behavior inspires optimization methods including bacterial foraging optimization and bacterial swarming algorithms. section 9.1
Table 9.1 Examples of swarm intelligence algorithms and their sources of inspiration (view table figure)

Organisms

Class

Source of inspiration

Algorithms

Unicellular organisms

Bacteria

Bacterial swarm foraging

Bacterial foraging optimization algorithm (BFO)

Bacterial swarming algorithm (BSA)

Multicellular organisms

Bird/fish

Bird flocking and fish schooling

Particle swarm optimization (PSO)

Ant

Ant foraging behaviors

Ant colony optimization (ACO)

Bees

Foraging behavior of honeybees

Artificial bee colony (ABC)

Bats

Echolocation behavior of bats

Bat algorithm (BA)

Fireflies

Flashing behavior of fireflies

Firefly algorithm (FA)

Butterflies

Foraging behavior of butterflies

Butterfly optimization algorithm (BOA)

Dragonflies

Static and dynamic swarming behaviors of dragonflies

Dragonfly algorithm (DA)

Spiders

Cooperative behavior of the social spiders

Social spider optimization (SSO)

Krill

Herding behavior of krill

Krill herd (KH)

Frogs

Frog cooperative search for food

Shuffled frog leaping algorithm (SFLA)

Fish

Gregarious behavior of fish

Fish school search (FSS)

Dolphins

Dolphins’ behavior in detecting, chasing after, and preying on swarms of sardines

Dolphin partner optimization (DPO)

Dolphin swarm optimization algorithm (DSOA)

Cats

Resting and tracing behaviors of cats

Cat swarm optimization (CSO)

Monkeys

Search for food

Monkey search algorithm (MSA)

Lions

Solitary and cooperative behaviors of lions

Lion optimization algorithm (LOA)

Cuckoos

Reproduction strategy of cuckoos

Cuckoo search (CS)

Cuckoo optimization algorithm (COA)

Wolves

Leadership hierarchy and hunting mechanism of gray wolves

Wolf search algorithm (WSA)

Grey wolf optimizer (GWO)

Compare bacterial chemotaxis with other biological sources of swarm-intelligence algorithms, including bird flocking, ant foraging, and honeybee behavior. section 9.1

How Optimization Algorithms: AI techniques for design, planning, and control problems covers it

How deep it goes
Defines chemotaxis and identifies its optimization influence, but stops short of algorithm mechanics, parameters, and implementation.
Examples
Uses E. coli foraging and cooperative bacterial colony organization as concrete biological examples.
What you can do after
Evaluate bacterial chemotaxis as a biological model for bacterial-inspired optimization algorithms.
How it connects
Connects bacterial chemotaxis with swarm intelligence, chemotactic signaling, bacterial foraging optimization, and bacterial swarming.
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