Swarm Intelligence for Deep Learning: Concepts, Challenges and Recent Trends

Abstract

AbstractMachine learning and deep learning have undoubtedly contributed to tremendous achievements in Artificial Intelligence (AI) in recent years, and more are likely to follow. They have demonstrated extraordinary superiority in various real-world applications like computer vision, medical diagnostic systems, agriculture, robotics, and many more. It enables automating the computer-aided system and drastically reducing the human workload where correct prediction with accurate precision is needed. On the other side, as technology advances, a vast amount of data is generated, raising the problem complexity and computational challenges of real-world applications. Furthermore, machine learning, deep learning, and the majority of real-world applications have complex optimization problems within themselves that must be adequately addressed for better and more accurate analysis. Nonetheless, we believe that swarm intelligence-based approaches to deep learning have traditionally been understudied and may ultimately deliver similar advances in AI capabilities - either building on those provided by deep learning or offering whole new ones. Swarm intelligence approaches are frequently employed to solve a wide range of optimization issues. Nowadays, swarm intelligence-based methods are attracting a lot of attention from the research communities of different domains because previous research in complex optimization has shown that behavioral patterns and phenomena observed in nature have the ability to facilitate the foundation for many optimization algorithms and solve problems efficiently. Swarm intelligence, machine learning, and deep learning, on the other hand, each has its own set of advantages and disadvantages. Recently, research communities have discovered an interest in integrating these concepts in order to overcome the limitations of each domain and give rise to a new paradigm known as evolutionary machine learning or evolutionary deep learning. In the case of machine learning and deep learning, the “curse of dimensionality,” non-convex optimization, automatic parameter optimization, and optimal architecture are just a few of the issues that can be efficiently addressed with swarm intelligence, whereas in the case of swarm intelligence, slow convergence, local optima stagnation, and extensive computation cost can be addressed with the machine learning and deep learning community. Therefore, a robust and self-efficient model can be developed by integrating these concepts to solve the complex problem associated with real-world applications. This hybrid approach benefits the majority of research domains. Thus, this chapter will primarily present the ideas, challenges, and recent trends of an integrative approach of swarm intelligence with deep learning, which is currently in high demand for addressing industrial problems.KeywordsSwarm intelligenceDeep learningNeuroevolutionHyperparameter optimization