logo
CanyamAI summaries for academic research
language
MobileMobile
  • Phone FeatureWeChat Mini Program
EnglishEnglish
noticeNotice
BlogBlog
delete
arrow
Return

MOMEVO: a modular surrogate-assisted evolutionary framework for efficient multi-objective optimization

delete2026-04-13
delete0
PRE
AI
R
Rafael Batres *
A
Azul Rosales
DOI：10.1007/s12530-026-09814-0delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
This paper introduces MOMEVO (Multi-Objective Metamodel-based Evolutionary Optimizer), a surrogate-assisted multi-objective evolutionary algorithm developed to address the challenges of computationally expensive optimization tasks commonly encountered in engineering and other domains. One of the core contributions of MOMEVO is its distance-based candidate selection mechanism, promoting global exploration more effectively than traditional criteria. Additionally, it features an iterative search-space reduction framework that adaptively focuses on high-potential regions while maintaining global coverage. MOMEVO’s modular architecture allows dynamic switching between Gaussian Processes and ensemble models, enhancing robustness in challenging landscapes. To explore the solution space effectively, MOMEVO uses evolutionary operators such as crossover and Gaussian mutation. Comprehensive experimental evaluations were conducted on synthetic multi-objective benchmark problems. Results demonstrate that MOMEVO delivers competitive or superior performance relative to established surrogate-assisted algorithms in terms of inverted generational distance (IGD), maximum spread, and computational efficiency. Additionally, results show that MOMEVO is significantly more efficient, completing optimization nearly three times faster than MOBopt and over five times faster than ParEGO and TSEMO. These findings highlight MOMEVO’s robustness, efficiency, and potential for solving real-world multi-objective optimization problems under limited evaluation budgets.
Keywords:
Simulation-based optimization
Surrogate-based optimization
Multi-objective optimization
Evolutionary computation

Journal

Evolving Systems cover
Evolving Systems
IF:
2.7
Papers:
226
Citations:
1.2K

Organization

No organization information available