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Algorithm for finding optimal paths that is based on the behavior of ants seeking a path between their colony and a source of food. Example scenario: solving the traveling salesman problem.

Variants of evolutionary algorithms that primarily use mutation and selection to evolve a set of candidate solutions. Example scenario: optimizing the acoustic properties of a concert hall.

A heuristic method for solving computationally complex optimization problems by iteratively examining neighboring states of a current solution. Example scenario: finding a better seating arrangement to minimize dissatisfaction in a group.

Inspired by social behavior of birds and fish, it optimizes a problem by iteratively improving a candidate solution with regard to a given measure of quality. Example scenario: optimizing the weights of a neural network.

An evolutionary algorithm that uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Example scenario: optimizing a complex nonlinear function with many local optima, such as the design of an aerodynamic shape.

A probabilistic technique for approximating the global optimum of a given function. It is often used for discrete optimization problems. An example scenario is optimizing the layout of components on a circuit board to minimize wiring length.

A local search-based algorithm using a tabu list to escape local optimality by forbidding or penalizing moves that involve reversing recent changes. An example scenario is finding the optimal routing for a fleet of delivery vehicles.

Inspired by the flashing behavior of fireflies, this algorithm uses attraction based on brightness to move towards optimal solutions in a search space. Example scenario: tuning hyper-parameters for a machine learning model.

A metaheuristic that mimics the obligate brood parasitism of some cuckoo species. It uses randomly placed eggs and Levy flights to search for solutions. Example scenario: optimizing the layout of solar panels for maximum efficiency.