This library is a decomposition strategy library under the advanced Cooperation Coevolution （CC） framework, implemented in Python to supplement the lack of Python implementations for many strategies.
We categorize some important problem decomposition strategies under the CC framework into three distinct groups：Static Decomposition, Strategies Based On Probabilistic And Statistical, and Strategies Based On Variable Interactions.
A review of the recent use of Differential Evolution for Large-Scale Global Optimization: An analysis of selected algorithms on the CEC 2013 LSGO benchmark suite (2019)
Large‑scale evolutionary optimization: a survey and experimental comparative study (2020)
A review of population-based metaheuristics for large-scale black-box global optimization (2022)
| Benchmark | Paper Link | Version |
|---|---|---|
| cec2013lsgo | Li X, Tang K, Omidvar M N, et al. "Benchmark functions for the CEC 2013 special session and competition on large-scale global optimization". (2013). | Original code,Python Efficient Parallel Version |
Static decomposition involves a one-time random decomposition, with each subgroup independently optimized and then combined to form the final optimization result.
| Strategy | Paper Link | Year | Description |
|---|---|---|---|
| CCGA | A Cooperative Coevolutionary Approach to Function Optimization | 1994 | The first decomposition strategy |
| CPSO | A Cooperative Approach to Particle Swarm Optimization | 2004 |
|
| CCDE | Cooperative Co-evolutionary Differential Evolution for Function Optimization | 2005 | Bipartite decomposition |
Strategies based on probabilistic and statistical aiming mitigate the issues inherent in static decomposition by capturing problem structure and variable interactions, with multiple rounds of decomposition before forming the final optimization result.
| Strategy | Paper Link | Year | Description |
|---|---|---|---|
| FEPCC | Scaling up fast evolutionary programming with cooperative coevolution | 2001 | |
| DECC-G | Large scale evolutionary optimization using cooperative coevolution | 2008 | Introduce the random decomposition scheme |
| MLCC | Cooperative Co-evolution for Large Scale Optimization Through More frequent Random Grouping | 2010 | Self-adaptation of the subcomponent sizes |
Theory: linkage identification by non-monotonicity detection (LIMD)
Paper: Linkage Identification by Non-monotonicity Detection for Overlapping Functions (1999)
Author Masaharu Munetomo，David E. Goldberg
| Strategy | Paper Link | Year | Description |
|---|---|---|---|
| CCVIL | Large-Scale Global Optimization Using Cooperative Coevolution with Variable Interaction Learning | 2010 | Propose the VIL based on LIMD |
| SVIL | A cooperative particle swarm optimizer with statistical variable interdependence learning | 2012 | |
| FVIL | Cooperative differential evolution with fast variable interdependence learning and cross-cluster mutation | 2015 | Propose a generalized version of the monotonicity check |
Theory: linkage identification by nonlinearity check (LINC)
Paper: Identifying Linkage by Nonlinearity Check （1998）
Author Masaharu Munetomo，David E. Goldberg
| Strategy | Paper Link | Year | Description |
|---|---|---|---|
| DG | Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization | 2014 | |
| XDG | Extended Differential Grouping for Large Scale Global Optimization with Direct and Indirect Variable Interactions | 2015 | |
| GDG | A Competitive Divide-and-Conquer Algorithm for Unconstrained Large-Scale Black-Box Optimization | 2016 | |
| gDG | Cooperative Co-evolution with Graph-based Differential Grouping for Large Scale Global Optimization | 2016 | |
| DG2 | DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box Optimization | 2017 | The parameter-free and efficiency-improving method of DG |
| DIAT | A Global Information Based Adaptive Threshold for Grouping Large Scale Optimization Problems | 2018 | |
| RDG | A Recursive Decomposition Method for Large Scale Continuous Optimization | 2018 | Propose the recursive decomposition method of DG |
| RDG2 | Adaptive Threshold Parameter Estimation with Recursive Differential Grouping for Problem Decomposition | 2018 | The parameter-free method of RDG |
| RDG3 | Decomposition for Large-scale Optimization Problems with Overlapping Components | 2019 | The clipping control for the overlapping components of RDG2 |
| DGSC | Differential Grouping with Spectral Clustering for Large Scale Global Optimization | 2019 | |
| ERDG | An Efficient Recursive Differential Grouping for Large-Scale Continuous Problems | 2021 |