Improving OPT-GAN by Smooth Scale Mapping and Adaptive Exploration

In Black Box Optimization, traditional Estimation of Distribution Algorithms (EDAs) are limited by strong prior assumptions. To address this, deep generative model-based EDAs, particularly OPT-GAN using Generative Adversarial Networks, show promise due to their adaptability across diverse problems. However, OPT-GAN’s search efficiency and balance between exploration and exploitation remain challenging. This study introduces an improved version of OPT-GAN. By integrating smoothing scale mapping and an adaptive exploration mechanism, it mitigates existing limitations, effectively balancing the E-E trade-off and improving search efficiency. Our experimental results, validated on the COCO benchmark, demonstrate that this improved OPT-GAN outperforms existing neural network-based optimization algorithms, highlighting its potential for wider application in complex optimization scenarios.