Intelligent industrial applications, powered by big data, leads to the evolution of industry 4.0. The rapid development of key techniques such as artificial intelligence (AI), the Internet of Things (IoT), fog/edge computing, and 5G networks benefits each industry by enabling a new path towards their business. At present, industrial applications have already experienced a significant digital transformation. This is specifically true for enterprise management systems. The advent of digital technologies has created a new customer experience, enabled new sales and marketing services, and empowered e-commerce applications. Thanks to intelligent industrial automation for adding intelligence all the way along the value chain. Thus everything associated with a business firm is digitalized, giving rise to new infrastructures and portfolios, ultimately paving the way for a more sustainable and profitable business. In this context, it has become crucial for the business to unleash the waves of innovation across every dimension of their business (intelligent operations, intelligent products and systems, intelligent support, and services).
Evolutionary computation in intelligent industrial applications is interdisciplinary research that uses evolutionary computation techniques to extract and gather useful patterns to find intelligent business strategies, products, and services to meet emerging customer demands. In general, evolutionary computation is a form of AI inspired by biological evolution. It offers novel ideas and discovers solutions that are interesting and creative. The three major benefits of implementing evolutionary algorithms across industrial applications include increased flexibility (It easily meet target objectives by solving complex problems), improved optimization (It considers all the possible solution), and it offers unlimited solutions (It offers multiple potential solutions to a problem). Further, it expands its technical abilities beyond the capability of deep learning algorithms. This potential significantly improves numerous intelligent industrial applications such as healthcare, retail and E-commerce, food technology, finance & banking, travel, transportation & logistics, real estate, gaming & entertainment, and manufacturing. Due to its advantages, evolutionary computation becomes the key to the future of intelligent industrial applications.
The goal of this Special Issue is to provide a platform for researchers to share their novel and unpublished research works related to evolutionary computation and next-generation intelligent industrial applications. This Special Issue particularly focuses on powerful algorithms that solve various optimization problems of industrial applications. Research works suggesting appropriate parameters to reach optimal solutions across various intelligent business applications are also most welcomed. Extensive literature and case studies based on this background are also invited for submission.
The topics of interest for the Special Issue include, but are not limited to, the following:
- Data-driven evolutionary optimization algorithms for intelligent industrial applications
- Evolutionary programming for intelligent business strategies
- Effective ways of accelerating business outcomes with evolutionary algorithms
- Swarm intelligence and particle swam optimization for intelligent business optimization processes
- Quantum computing in next generation intelligent business applications
- Genetic algorithms and genetic programming
- Hybrid intelligent systems and evolutionary computation
- Novel-biologically inspired algorithms for intelligent business applications
- Evolutionary strategies for various intelligent applications such as healthcare, retail, manufacturing, etc.
- Evolutionary searches and metaheuristics
- Bioinspired hardware systems and software practices for intelligent industrial applications
- Evolutionary robotics and its real-world business applications
- Advanced evolutionary design paradigms for business applications
- Evolutionary multi-objective optimization
Tentative timeline:Manuscript Submission Deadline Date: 15 December 2021Authors Notification Date: 20 February 2022Revised Papers Due Date: 25 May 2022Final notification Date: 05 August 2022
Guest Editors:Dr Tu Nguyen, Purdue University Fort Wayne, Fort Wayne, USADr Warren Huang-Chen Lee, IBM Thomas J. Watson Research Center, Yorktown Heights, USADr Braulio Dumba, IBM Thomas J. Watson Research Center, Yorktown Heights, USA
Submission guidelines:Submissions should be made through Editorial Manager: https://www.editorialmanager.com/evos/default.aspxSubmission guidelines for authors are available: https://www.springer.com/journal/12530/submission-guidelines