Heuristic and Evolutionary Algorithms for Engineering Optimization
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 31 October 2024 | Viewed by 50
Special Issue Editors
Interests: heuristic optimization methods; manufacturing system scheduling and optimization; complex system modeling
Interests: combinatorial optimization; heuristic algorithms
Special Issue Information
Dear Colleagues,
In the ever-evolving landscape of engineering, the pursuit of optimization lies at the heart of innovation and progress. Applied Sciences is pleased to announce a Special Issue dedicated to exploring the intricate realm of “Heuristic and Evolutionary Algorithms for Engineering Optimization”. As industries strive for efficiency, sustainability, and excellence, the utilization of heuristic and evolutionary algorithms has emerged as a pivotal tool in tackling the complexities of optimization across a myriad of engineering domains. This Special Issue aims to delve deep into the innovative methodologies, applications, and advancements in the field, shedding light on the transformative potential of heuristic and evolutionary algorithms in shaping the future of engineering. Delving into the development and analysis of heuristic and evolutionary algorithms, this Special Issue welcomes contributions that offer novel insights and advancements in algorithmic design. Hybrid and parallel heuristic optimization techniques, which combine the strengths of different algorithms or utilize parallel computing architectures, are of particular interest; multi-objective optimization, a critical aspect of engineering design, is another focal point, with a focus on methodologies that leverage heuristic and evolutionary algorithms to navigate the complexities of optimizing conflicting objectives.
Dr. Junwen Ding
Prof. Dr. Yang Wang
Prof. Dr. Liji Shen
Guest Editors
Manuscript Submission Information
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Keywords
- engineering optimization problems
- complex system modelling and optimization
- control and manufacturing applications
- multi/many-objective optimization
- evolutionary computation
- swarm intelligence
- meta-heuristics
- genetic algorithm
- particle swarm optimization
- ant colony optimization
- knowledge processing
