DeepEMO: A Multi-indicator Convolutional Neural Network-Based Evolutionary Multi-objective Algorithm

Quality Indicators (QIs) have been used in numerous Evolutionary Multi-objective Optimization Algorithms (EMOAs) as selection mechanisms within the evolutionary process. Because each QI prefers specific point-distribution properties, an Indicator-based EMOA (IB-EMOA) that uses a single QI has an intrinsically limited scope of problems it can solve accurately. To overcome the issues that IB-EMOAs have, we present the first results of a new general multi-indicator-based multi-objective evolutionary algorithm, denoted as DeepEMO. It uses a Convolutional Neural Network (CNN) as a hyper-heuristic to choose, depending on the Pareto-front geometry, the appropriate indicator-based selection mechanism at each generation of the evolutionary process. We employ state-of-the-art benchmark problems with different Pareto front geometries to test our approach. Our experimental results show that DeepEMO obtains competitive performance across multiple QIs. This is because the CNN is employed to classify the geometry of the point cloud that approximates the Pareto front. Hence, DeepEMO compensates for the weaknesses of a single QI with the strengths of others, showing that its performance is invariant to the Pareto front geometry.