Reference List of All Optimizers in the PyPop Library - Evolutionary-Intelligence/pypop Wiki


“It is both enjoyable and educational to hear the ideas directly from the creators”.
From Hennessy, J.L. and Patterson, D.A., 2019. 
    Computer architecture: A quantitative approach (Sixth Edition). Elsevier.

Here we thank all the authors/coders very much for making their source code available, which make this library much easier to develop and compare.

Evolution Strategy (ES)

MAES

Particle Swarm Optimizer (PSO)

The Particle Swarm Optimizer (PSO) class is the base class of all PSO variants, including Particle Swarm Optimizer with Global Topology (PSOGT), Particle Swarm Optimizer with Ring Topology (PSORT), Comprehensive Learning Particle Swarm Optimizer (CLPSO).

CLPSO

Simulated Annealing (SA)

The Simulated Annealing (SA) class is the base class of all SA variants, including the default SA version from [Corana et al., 1987] (SA) and Enhanced Simulated Annealing from [Siarry et al., 1997] (ESA).

SA

ESA

Random (Stochastic) Search (RS)

The Random Search (RS) class is the base class of all individual-based stochastic search optimizers, including Pure Random Search (PRS), Random Hill Climber (RHC).

“While pure random search (the Monte Carlo method), the simplest of all optimization techniques,
is universally applicable,
it is also much too inefficient to be taken seriously.”
From Preuss, M., 2015. Multimodal optimization by means of evolutionary algorithms.
Springer International Publishing.

PRS (Pure Random Search)

RHC