List Of Algorithms¶

Algorithms available in pymoo¶
Algorithm	Class	Objective(s)	Constraints	Description
Genetic Algorithm	GA	single	x	A modular implementation of a genetic algorithm. It can be easily customized with different evolutionary operators and applies to a broad category of problems.
Differential Evolution	DE	single	x	Different variants of differential evolution which is a well-known concept for in continuous optimization especially for global optimization.
Biased Random Key Genetic Algorithm	BRKGA	single	x	Mostly used for combinatorial optimization where instead of custom evolutionary operators the complexity is put into an advanced variable encoding.
Nelder Mead	NelderMead	single	x	A point-by-point based algorithm which keeps track of a simplex with is either extended reflected or shrunk.
Pattern Search	PatternSearch	single	x	Iterative approach where the search direction is estimated by forming a specific exploration pattern around the current best solution.
CMAES	CMAES	single		Well-known model-based algorithm sampling from a dynamically updated normal distribution in each iteration.
Evolutionary Strategy	ES	single		The evolutionary strategy algorithm proposed for real-valued optimization problems.
Stochastic Ranking Evolutionary Strategy	SRES	single	x	An evolutionary strategy with constrained handling using stochastic ranking.
Improved Stochastic Ranking Evolutionary Strategy	ISRES	single	x	An improved version of SRES being able to deal dependent variables efficiently.
NSGA-II	NSGA2	multi	x	Well-known multi-objective optimization algorithm based on non-dominated sorting and crowding.
R-NSGA-II	RNSGA2	multi	x	An extension of NSGA-II where reference/aspiration points can be provided by the user.
NSGA-III	NSGA3	many	x	An improvement of NSGA-II developed for multi-objective optimization problems with more than two objectives.
U-NSGA-III	UNSGA3	many	x	A generalization of NSGA-III to be more efficient for single and bi-objective optimization problems.
R-NSGA-III	RNSGA3	many	x	Allows defining aspiration points for NSGA-III to incorporate the user’s preference.
MOEAD	MOEAD	many		Another well-known multi-objective optimization algorithm based on decomposition.
AGE-MOEA	AGEMOEA	many		Similar to NSGA-II but estimates the shape of the Pareto-front to compute a score replacing the crowding distance.
C-TAEA	CTAEA	many	x	An algorithm with a more sophisticated constraint-handling for many-objective optimization algoritms.
SMS-EMOA	CTAEA	many	x	An algorithm that uses hypervolume during the environmental survival.
RVEA	RVEA	many		A reference direction based algorithm used an angle-penalized metric.