Population-based stochastic global optimization algorithm based on the behavior of ant colonies, particularly their ability to collectively determine shortest paths through the cumulative affect of ph [..]

A process for evaluating derivatives of a function that depends only on an algorithmic specification of the function, such as a computer program.

Restriction that a design variables must satisfy, typically denoted in a mathematical program standard form as an inequality, g(x) <= 0, or equality, h(x)=0.

Population-based stochastic global optimization algorithm inspired by the mechanisms of genetics, evolution, and survival of the fittest.

A feasible solution that maximizes (or minimizes) the value of the objective function within a local neighborhood of that solution.

A constraint that specifies a minimum feasible value of an individual design variable.

The (real-valued) function to be optimized, typically denoted in a mathematical program standard form as f.

Population-based stochastic global optimization algorithm based on the optimal swarm behavior of animals, like bird flocking and bees.