Submission Deadline: 30 April 2027 View: 19 Submit to Special Issue
Dr. Marwa M. Eid
Email: marwa.3eeed@gmail.com
Affiliation: Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, Egypt
Research Interests: optimization, artificial intelligence, multimedia processing, biomedical applications
Dr. Nima Khodadadi
Email: nima.khodadadi@miami.edu
Affiliation: Department of Civil and Architectural Engineering, University of Miami, Coral Gables, United States
Research Interests: AI, machine learning, fiber-reinforced polymers (FRP), concrete materials, metaheuristic optimization algorithms
Swarm intelligence (SI) is based on the coordinated behavior of a decentralized system that is self-organized, and the potential of SI in the AI field is very considerable. "This is how SI principles impact AI research. they are used to solve complex engineering problems using a distributed approach." Amongst various human being applications, SI optimization is one of the most effective. This field can be dubbed mathematical programming or simply optimization. Optimization techniques are always used to discover the best solution out of a whole set of possibilities, regardless of the industry sector, from engineering to finance, logistics, or telecommunications.
Swarm intelligence and metaheuristic optimization are two highly effective methods of AI optimization techniques. Different metaheuristic algorithms, namely genetic algorithms, simulated annealing and particle swarm optimization, serve as potent and versatile optimization techniques that possess a natural ability to travel through complex search areas with ease to obtain near-optimal solutions. These algorithms take inspiration from natural phenomena or problem-solving concepts, enabling them to tackle difficult problems that are not solvable using common methods.
The present special issue enters the area where smart computing finds its practical applications, covering the topics of swarm optimization and metaheuristic optimization for AI in engineering applications. Also, it can act as a stage for putting novel research conceptions and discoveries made about those approaches in the limelight. Both theoretical and practical contributions are encouraged anywhere from the foundation studies of swarm intelligence through system implementations to smart applications and other critical research areas intended to develop the fields of swarm intelligence and metaheuristics to the point that they can solve real problems. This collaborative effort serves to bring interdisciplinary dialogues to brush up on the new school of thought and find ways to find solutions in the field of AI and optimization.
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