README.md

In kerschke/mogsa: A Multi-Objective Optimization Algorithm Based on Multi-Objective Gradients

mogsa: Visualizing and Optimizing Multi-Objective Optimization Problems

Description

Although many (real-world) optimization problems actually contain multiple objectives and hence actually are multi-objective optimization problems, people often prefer to handle them as single-objective problems (as those are easier to grasp).

In an attempt to make multi-objective problems more conceivable, mogsa provides the means to visualize the landscapes of bi-objective problems -- more precisely: of its gradients. As exemplarily shown by the heatmaps below, the visualizations reveal the interactions between the local optima of the problem's different objectives in the search and decision space.

Based on the insights gained from these visualizations, we developed a multi-objective gradient-based local search algorithm: the multi-objective gradient sliding algorithm (MOGSA), which is also implemented within this package.

Installation

Currently, mogsa is only available within this development version, however, we of course also plan to submit it to CRAN in the near future.

In the mean time, feel free to use the development version of this package:

install.packages("devtools")
devtools::install_github("kerschke/mogsa")

Quickstart

To be continued ...

Citation

If you like our proposed gradient-field heatmaps, cite our EMO 2017 paper in your publications. Similarly, please refer to our EMO 2019 paper when referencing MOGSA.

@inproceedings{KerschkeGrimme2017Expedition,
  author    = {Pascal Kerschke and Christian Grimme},
  title     = {{An Expedition to Multimodal Multi-Objective Optimization Landscapes}},
  booktitle = {{Proceedings of the 9$^{th}$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)}},
  pages     = {329~--~343},
  series    = {{Lecture Notes in Computer Science (LNCS)}},
  volume    = {11411},
  editor    = {Heike Trautmann and G{\"u}nter Rudolph and Kathrin Klamroth and Oliver Sch{\"u}tze and Margaret Wiecek and Yaochu Jin and Christian Grimme},
  year      = {2017},
  publisher = {Springer},
  address   = {M{\"u}nster, Germany},
  isbn      = {978-3-319-54157-0},
  doi       = {10.1007/978-3-319-54157-0_23},
  url       = {http://link.springer.com/chapter/10.1007/978-3-319-54157-0_23}
}

@inproceedings{GrimmeKerschkeTrautmann2019Multimodality,
  author    = {Christian Grimme and Pascal Kerschke and Heike Trautmann},
  title     = {{Multimodality in Multi-Objective Optimization --- More Boon than Bane?}},
  booktitle = {{Proceedings of the 10$^{th}$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)}},
  pages     = {126~--~138},
  series    = {{Lecture Notes in Computer Science (LNCS)}},
  volume    = {11411},
  editor    = {Kalyanmoy Deb and Erik Goodman and Carlos A. {Coello Coello} and Kathrin Klamroth and Kaisa Miettinen and Sanaz Mostaghim and Patrick Reed},
  year      = {2019},
  publisher = {Springer},
  location  = {{East Lansing}, {MI}, {USA}},
  doi       = {10.1007/978-3-030-12598-1_11},
  url       = {https://link.springer.com/chapter/10.1007/978-3-030-12598-1_11}
}

Contact

If you have any suggestions or ideas, or run into problems while running the code, please use the issue tracker or send me an e-mail ([e-mail]).

kerschke/mogsa documentation built on July 11, 2019, 11:52 p.m.