Multi-Objective Parameter-less Population Pyramid for Solving Industrial Process Planning Problems

TL;DR

This paper introduces a multi-objective version of the Parameter-less Population Pyramid (P3) to effectively solve complex, NP-hard industrial process planning problems, demonstrating superior performance over existing methods.

Contribution

The paper presents a novel multi-objective P3 algorithm tailored for industrial process planning, extending the original single-objective P3 with linkage learning capabilities.

Findings

Outperforms competing methods on practical industrial problems

Achieves better results on multi-objective benchmarks

Demonstrates effectiveness in solving NP-hard problems

Abstract

Evolutionary methods are effective tools for obtaining high-quality results when solving hard practical problems. Linkage learning may increase their effectiveness. One of the state-of-the-art methods that employ linkage learning is the Parameter-less Population Pyramid (P3). P3 is dedicated to solving single-objective problems in discrete domains. Recent research shows that P3 is highly competitive when addressing problems with so-called overlapping blocks, which are typical for practical problems. In this paper, we consider a multi-objective industrial process planning problem that arises from practice and is NP-hard. To handle it, we propose a multi-objective version of P3. The extensive research shows that our proposition outperforms the competing methods for the considered practical problem and typical multi-objective benchmarks.