Core Algorithms for Multi-Objective Optimization

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  • License: MIT License
  • Author: Manuel López-Ibáñez
  • Tags math, Mathematics, Multi-criteria Decision-Making, operations research, Optimization
  • Requires: Python >=3.10
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Project description

moocore: Core Algorithms for Multi-Objective Optimization

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Contributors: Manuel López-Ibáñez, Fergus Rooney.

Introduction

The goal of moocore is to collect fast implementations of core mathematical functions and algorithms for multi-objective optimization. These functions include:

  • Identifying and filtering dominated vectors.
  • Quality metrics such as (weighted) hypervolume, epsilon, IGD, etc.
  • Computation of the Empirical Attainment Function. The empirical attainment function (EAF) describes the probabilistic distribution of the outcomes obtained by a stochastic algorithm in the objective space.

Keywords: empirical attainment function, summary attainment surfaces, EAF differences, multi-objective optimization, bi-objective optimization, performance measures, performance assessment

R package

There is also a moocore package for R: https://multi-objective.github.io/moocore/r

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  • License: MIT License
  • Author: Manuel López-Ibáñez
  • Tags math, Mathematics, Multi-criteria Decision-Making, operations research, Optimization
  • Requires: Python >=3.10
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