Pruning-Based Pareto Front Generation for Mixed-Discrete Bi-Objective Optimization

TL;DR

This paper introduces a pruning-based method for efficiently generating Pareto fronts in mixed-discrete bi-objective optimization by decomposing problems and pruning non-contributory subproblems, demonstrated on benchmark examples.

Contribution

It presents a novel two-phase pruning approach that improves Pareto front generation efficiency in mixed-discrete bi-objective problems.

Findings

Effective pruning reduces computational effort.

Method successfully applied to benchmark problems.

Improves Pareto front accuracy and efficiency.

Abstract

This note proposes an effective pruning-based Pareto front generation method in mixed-discrete bi-objective optimization. The mixed-discrete problem is decomposed into multiple continuous subproblems; two-phase pruning steps identify and prune out non-contributory subproblems to the Pareto front construction. The efficacy of the proposed method is demonstrated on two benchmark examples.