| Abstract |
One of the preliminary challenges faced by globalization is to satisfy the rapidly growing demand for consumer goods to ensure sustainable evolution of the human race in economic dimensions. The adoption of sustainable manufacturing of sustainable supply chain management, sustainable industries, and sustainable smart cities. In order to meet this challenge, Industry 4.0 has evolved. Nature-inspired optimization algorithms are a collection of algorithms which are inspired by the behavior of animals, chemical reactions, and biological processes to solve complex engineering and medical problems. Nature-inspired optimization algorithms exhibit potential characteristics in terms of computation, learning rate, and drawing inferences to arrive at promising solutions. Hence, in this chapter, recent nature-inspired optimization algorithms like the Adam optimizer, stochastic gradient descent (SGD), water wave optimization, seed-based plant propagation algorithm, and bumblebee mating optimization (BBMO) are discussed, along with their advantages and disadvantages. © 2025 selection and editorial matter, Ajay Kumar, Parveen Kumar, Yang Liu, and Rakesh Kumar. |
