An Interactive Knowledge-based Multi-objective Evolutionary Algorithm Framework for Practical Optimization Problems

TL;DR

This paper introduces an interactive framework that extracts, validates, and applies variable relationships from high-performing solutions to enhance multi-objective optimization, especially for large-scale real-world problems.

Contribution

It proposes a scalable, graph-based method for automatic knowledge extraction and user interaction to improve evolutionary multi-objective optimization in practical applications.

Findings

Effective extraction of variable relationships from solutions.

Improved optimization performance on large-scale problems.

User feedback integration enhances solution quality.

Abstract

Experienced users often have useful knowledge and intuition in solving real-world optimization problems. User knowledge can be formulated as inter-variable relationships to assist an optimization algorithm in finding good solutions faster. Such inter-variable interactions can also be automatically learned from high-performing solutions discovered at intermediate iterations in an optimization run - a process called innovization. These relations, if vetted by the users, can be enforced among newly generated solutions to steer the optimization algorithm towards practically promising regions in the search space. Challenges arise for large-scale problems where the number of such variable relationships may be high. This paper proposes an interactive knowledge-based evolutionary multi-objective optimization (IK-EMO) framework that extracts hidden variable-wise relationships as knowledge from evolving high-performing solutions, shares them with users to receive feedback, and applies them back to the optimization process to improve its effectiveness. The knowledge extraction process uses a systematic and elegant graph analysis method which scales well with number of variables. The working of the proposed IK-EMO is demonstrated on three large-scale real-world engineering design problems. The simplicity and elegance of the proposed knowledge extraction process and achievement of high-performing solutions quickly indicate the power of the proposed framework. The results presented should motivate further such interaction-based optimization studies for their routine use in practice.