Personalized Indicator Based Evolutionary Algorithm for Uncertain Constrained Many-Objective Optimization Problem With Interval Functions

Abstract

In practical engineering problems, uncertainties due to prediction errors and fluctuations in equipment efficiency often lead to constrained many-objective optimization problem with interval parameters (ICMaOPs). These problems pose significant challenges for evolutionary algorithms, particularly in balancing solution convergence, diversity, feasibility, and uncertainty. To address these challenges, a personalized indicator-based evolutionary algorithm (PI-ICMaOEA) specifically designed for ICMaOPs is proposed. The PI-ICMaOEA integrates a comprehensive quality indicator that encapsulates convergence, diversity, uncertainty, and feasibility factors, converting multiple objectives in high-dimensional search spaces into a single evaluative metric. Each factor's weight is personalized assigned based on individual performance, objective dimension, and the evolving conditions of the population. By prioritizing individuals with excellent indicator values for mating and environmental selection, PI-ICMaOEA effectively enhances selection pressure in high-dimensional spaces. Comparative simulations demonstrate that PI-ICMaOEA is highly competitive, offering a robust solution for balancing convergence, diversity, uncertainty, and feasibility in ICMaOPs.