HEMO improves multi-objective optimization by evolving search strategies
HEMO: Harness Self-Evolution for Multi-Objective Combinatorial Optimization
Neural and Evolutionary ComputingArtificial Intelligence
Summary
Finding the best solutions to complex problems often involves trying many options while balancing trade-offs. The paper introduces HEMO, a system that not only evolves the parts used to make new solutions but also improves the way the search for solutions is guided over time. By updating its approach based on past successes and failures, HEMO better adapts to changing problem landscapes. This leads to higher quality solutions and faster search times across different optimization tasks.
What this means in practice
- •For optimization engineers: Use HEMO to enhance solution quality and speed in engineering design problems balancing multiple objectives.
- •For logistics planners: Apply HEMO's evolving search process to improve route and resource allocation solutions with competing goals.
Authors
Jianghan Zhu, Cong Zhang, Ni Zhang, Yifan Yang, Yew Soon Ong, Zhiguang Cao
Abstract
Automatic operator design (AOD) has increasingly adopted large language models (LLMs) to generate problem-specific search and variation operators for multi-objective combinatorial optimization (MOCO). Yet most methods evolve the operators while leaving the process that discovers them fixed. This limits adaptation as new Pareto trade-offs and operator interactions change where and how the search should proceed. We introduce HEMO, which makes the AOD harness itself a self-evolving optimization target. HEMO maintains a persistent harness state and updates one module at a time, revising the logic that determines which experience to retrieve, which operators to improve, which parents to select, and how to formulate the orchestration of search steps. Downstream solution quality and runtime guide these updates, while a discounted UCB scheduler directs adaptation toward promising modules as their utility changes. A persistent memory filesystem preserves candidate outcomes and diagnostic feedback across harness states, enabling successive states to build on accumulated optimization experience. Experiments across eight MOCO tasks and two structurally distinct evolutionary backbones demonstrate strong performance against conventional evolutionary, neural, and LLM-based AOD baselines, together with effective transfer to related tasks. HEMO extends automatic operator design beyond evolving individual operators to improving the search process that discovers them.