# Studying Evolutionary Solution Adaption by Using a Flexibility Benchmark Based on a Metal Cutting Process

## TL;DR

This paper introduces a benchmark for studying how evolutionary algorithms adapt to changing manufacturing optimization tasks, showing that adapted methods reduce computational effort.

## Contribution

A new flexibility benchmark for evolutionary optimization in metal cutting, with two NSGA-II variants that improve adaptation efficiency.

## Key findings

- Adaptation with standard NSGA-II reduces evaluations needed for optimization.
- The proposed variants further halve the computational effort compared to non-adapted baselines.
- More research is needed to apply these methods effectively in real-world scenarios.

## Abstract

We consider optimization for different production requirements from the viewpoint of a bio-inspired framework for system flexibility that allows us to study the ability of an algorithm to transfer solutions from previous optimization tasks, which also relates to dynamic evolutionary optimization. Optimizing manufacturing process parameters is typically a multi-objective problem with often contradictory objectives, such as production quality and production time. If production requirements change, process parameters have to be optimized again. Since optimization usually requires costly simulations based on, for example, the Finite Element method, it is of great interest to have a means to reduce the number of evaluations needed for optimization. Based on the extended Oxley model for orthogonal metal cutting, we introduce a multi-objective optimization benchmark where different materials define related optimization tasks. We use the benchmark to study the flexibility of NSGA-II, which we extend by developing two variants: (1) varying goals, which optimizes solutions for two tasks simultaneously to obtain in-between source solutions expected to be more adaptable, and (2) active–inactive genotype, which accommodates different possibilities that can be activated or deactivated. Results show that adaption with standard NSGA-II greatly reduces the number of evaluations required for optimization for a target goal. The proposed variants further improve the adaption costs, where on average, the computational effort is more than halved in comparison to the non-adapted baseline. We note that further work is needed for making the methods advantageous for real applications.