An Objective Reduction Evolutionary Multiobjective Algorithm using Adaptive Density-Based Clustering for Many-objective Optimization Problem

• DOI: 10.1145/3590003.3590103
• Publication Date: 2023-05-29T18:22:56Z
• Authors: Mingjing Wang; Long Chen; Huiling Chen

Many-objective optimization problems (MaOPs), are the most difficult to solve when it comes multiobjective issues (MOPs). MaOPs provide formidable challenges current evolutionary methods such as selection operators, computational cost, visualization of high-dimensional trade-off front. Removal reductant objectives from original objective set, known reduction, is one significant approaches for MaOPs, which can tackle with more than 15 made feasible by its ability greatly overcome existing multi-objective computing techniques. In this study, an reduction algorithm using adaptive density-based clustering presented MaOPs. The parameters in be adaptively determined depending on data samples constructed. Based result, employs strategy aggregation that preserves structure Pareto front much feasible. Finally, performance proposed algorithms benchmarks thoroughly investigated. numerical findings and comparisons demonstrate efficacy superiority suggested may treated a potential tool