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.
Particle Swarm Optimization	PSO	single	x	A swarm intelligence algorithm where each particle is guided by its own and the swarm’s best-known positions to explore the search space.
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	x	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.
G3PCX	G3PCX	single		A real-coded genetic algorithm using the generalized generation gap (G3) model with the parent-centric recombination (PCX) operator.
Newton-Raphson-based Optimizer	NRBO	single	x	A population-based metaheuristic inspired by Newton-Raphson method with high optimization efficiency.
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.
PI-NSGA-II	PINSGA2	multi	x	An interactive version of NSGA-II that uses user preference to guide the optimization towards desired solutions.
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.
AGE-MOEA2	AGEMOEA2	many		An improved AGE-MOEA with a more accurate estimation of the Pareto-front geometry used to compute the survival score.
C-TAEA	CTAEA	many	x	An algorithm with a more sophisticated constraint-handling for many-objective optimization algoritms.
SMS-EMOA	SMSEMOA	many	x	An algorithm that uses hypervolume during the environmental survival.
SPEA2	SPEA2	multi	x	Strength Pareto evolutionary algorithm using a strength-based fitness with a k-th nearest neighbor density estimate and an archive truncation operator for diversity.
Omni-Optimizer	OmniOptimizer	multi	x	A generic NSGA-II based algorithm using a dynamic epsilon-dominance and crowding in objective and variable space to find multiple equivalent Pareto-optimal solutions.
RVEA	RVEA	many		A reference direction based algorithm used an angle-penalized metric.
CMOPSO	CMOPSO	multi	x	A competitive mechanism based multi-objective particle swarm optimizer with fast convergence using binary tournament selection on elites.
MOPSO-CD	MOPSO_CD	multi	x	Multi-objective particle swarm optimization with crowding distance mechanism for leader selection and archive management.
D-NSGA-II	DNSGA2	multi	x	A dynamic version of NSGA-II that detects and reacts to changes of the objectives over time.
KGB-DMOEA	KGB	multi	x	A knowledge-guided Bayesian dynamic multi-objective optimizer that reuses past search knowledge when the problem changes.
NSDE	NSDE	multi	x	NSGA-II combined with differential evolution variation operators instead of simulated binary crossover and polynomial mutation.
GDE3	GDE3	multi	x	Generalized Differential Evolution 3 - differential evolution variation with non-dominated sorting survival and a greedy parent-trial comparison.
NSDE-R	NSDER	many	x	A reference-direction based differential evolution algorithm extending NSGA-III’s survival to many-objective problems.