Bacterial colony optimization

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The bacterial colony optimization algorithm is an optimization algorithm which is based on a lifecycle model that simulates some typical behaviors of E. coli bacteria during their whole lifecycle, including chemotaxis, communication, elimination, reproduction, and migration. [1] The bacterial foraging algorithm (BFA) is a biologically inspired swarm intelligence optimization approach that mimics bacteria's foraging activity to gather the most energy available throughout the search phase. Since its introduction in 2002, it has garnered widespread interest from scholars. [2]

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<span class="mw-page-title-main">Swarm behaviour</span> Collective behaviour of a large number of (usually) self-propelled entities of similar size

Swarm behaviour, or swarming, is a collective behaviour exhibited by entities, particularly animals, of similar size which aggregate together, perhaps milling about the same spot or perhaps moving en masse or migrating in some direction. It is a highly interdisciplinary topic.

Virulence is a pathogen's or microorganism's ability to cause damage to a host.

<span class="mw-page-title-main">Ant colony optimization algorithms</span> Optimization algorithm

In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Artificial ants stand for multi-agent methods inspired by the behavior of real ants. The pheromone-based communication of biological ants is often the predominant paradigm used. Combinations of artificial ants and local search algorithms have become a method of choice for numerous optimization tasks involving some sort of graph, e.g., vehicle routing and internet routing.

<span class="mw-page-title-main">Swarm intelligence</span> Collective behavior of decentralized, self-organized systems

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In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, tune, or select a heuristic that may provide a sufficiently good solution to an optimization problem or a machine learning problem, especially with incomplete or imperfect information or limited computation capacity. Metaheuristics sample a subset of solutions which is otherwise too large to be completely enumerated or otherwise explored. Metaheuristics may make relatively few assumptions about the optimization problem being solved and so may be usable for a variety of problems.

<span class="mw-page-title-main">Small intestinal bacterial overgrowth</span> Medical condition

Small intestinal bacterial overgrowth (SIBO), also termed bacterial overgrowth, or small bowel bacterial overgrowth syndrome (SBBOS), is a disorder of excessive bacterial growth in the small intestine. Unlike the colon, which is rich with bacteria, the small bowel usually has fewer than 100,000 organisms per millilitre. Patients with bacterial overgrowth typically develop symptoms which may include nausea, bloating, vomiting, diarrhea, malnutrition, weight loss, and malabsorption by various mechanisms.

<i>Proteus mirabilis</i> Species of bacterium

Proteus mirabilis is a Gram-negative, facultatively anaerobic, rod-shaped bacterium. It shows swarming motility and urease activity. P. mirabilis causes 90% of all Proteus infections in humans. It is widely distributed in soil and water. Proteus mirabilis can migrate across the surface of solid media or devices using a type of cooperative group motility called swarming. Proteus mirabilis is most frequently associated with infections of the urinary tract, especially in complicated or catheter-associated urinary tract infections.

<span class="mw-page-title-main">Waggle dance</span> Honey bees particular figure-eight dance

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<i>Paenibacillus</i> Genus of bacteria

Paenibacillus is a genus of facultative anaerobic, endospore-forming bacteria, originally included within the genus Bacillus and then reclassified as a separate genus in 1993. Bacteria belonging to this genus have been detected in a variety of environments, such as: soil, water, rhizosphere, vegetable matter, forage and insect larvae, as well as clinical samples. The name reflects: Latin paene means almost, so the paenibacilli are literally "almost bacilli". The genus includes P. larvae, which causes American foulbrood in honeybees, P. polymyxa, which is capable of fixing nitrogen, so is used in agriculture and horticulture, the Paenibacillus sp. JDR-2 which is a rich source of chemical agents for biotechnology applications, and pattern-forming strains such as P. vortex and P. dendritiformis discovered in the early 90s, which develop complex colonies with intricate architectures as shown in the pictures:

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Microbial intelligence is the intelligence shown by microorganisms. The concept encompasses complex adaptive behavior shown by single cells, and altruistic or cooperative behavior in populations of like or unlike cells mediated by chemical signalling that induces physiological or behavioral changes in cells and influences colony structures.

In microbiology, colony-forming unit is a unit which estimates the number of microbial cells in a sample that are viable, able to multiply via binary fission under the controlled conditions. Counting with colony-forming units requires culturing the microbes and counts only viable cells, in contrast with microscopic examination which counts all cells, living or dead. The visual appearance of a colony in a cell culture requires significant growth, and when counting colonies, it is uncertain if the colony arose from one cell or a group of cells. Expressing results as colony-forming units reflects this uncertainty.

<span class="mw-page-title-main">Bacteria</span> Domain of microorganisms

Bacteria are ubiquitous, mostly free-living organisms often consisting of one biological cell. They constitute a large domain of prokaryotic microorganisms. Typically a few micrometres in length, bacteria were among the first life forms to appear on Earth, and are present in most of its habitats. Bacteria inhabit soil, water, acidic hot springs, radioactive waste, and the deep biosphere of Earth's crust. Bacteria play a vital role in many stages of the nutrient cycle by recycling nutrients and the fixation of nitrogen from the atmosphere. The nutrient cycle includes the decomposition of dead bodies; bacteria are responsible for the putrefaction stage in this process. In the biological communities surrounding hydrothermal vents and cold seeps, extremophile bacteria provide the nutrients needed to sustain life by converting dissolved compounds, such as hydrogen sulphide and methane, to energy. Bacteria also live in mutualistic, commensal and parasitic relationships with plants and animals. Most bacteria have not been characterised and there are many species that cannot be grown in the laboratory. The study of bacteria is known as bacteriology, a branch of microbiology.

In computer science and operations research, the bees algorithm is a population-based search algorithm which was developed by Pham, Ghanbarzadeh et al. in 2005. It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both combinatorial optimization and continuous optimization. The only condition for the application of the bees algorithm is that some measure of distance between the solutions is defined. The effectiveness and specific abilities of the bees algorithm have been proven in a number of studies.

<span class="mw-page-title-main">Prokaryote</span> Unicellular organism lacking a membrane-bound nucleus

A prokaryote is a single-cell organism whose cell lacks a nucleus and other membrane-bound organelles. The word prokaryote comes from the Ancient Greek πρό 'before' and κάρυον 'nut, kernel'. In the two-empire system arising from the work of Édouard Chatton, prokaryotes were classified within the empire Prokaryota. But in the three-domain system, based upon molecular analysis, prokaryotes are divided into two domains: Bacteria and Archaea. Organisms with nuclei are placed in a third domain, Eukaryota.

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This is a chronological table of metaheuristic algorithms that only contains fundamental computational intelligence algorithms. Hybrid algorithms and multi-objective algorithms are not listed in the table below.

References

  1. Niu, Ben; Wang, Hong (2012). "Bacterial Colony Optimization". Discrete Dynamics in Nature and Society. 2012: 1–28. doi: 10.1155/2012/698057 . S2CID   121768687.
  2. Pang, Shinsiong; Chen, Mu-Chen (June 1, 2023). "Optimize railway crew scheduling by using modified bacterial foraging algorithm" . Computers & Industrial Engineering. 180: 109218. doi:10.1016/j.cie.2023.109218. ISSN   0360-8352. S2CID   257990456.