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

Abstract

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