Metaheuristic Algorithms in Optimal Design of Engineering Problems (2nd Edition)
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 471
Special Issue Editors
Interests: heuristic optimization algorithms; constrained optimization; permanent magnet machines; hybrid optimization algorithms
Special Issues, Collections and Topics in MDPI journals
Interests: renewable energy sources; artificial intelligence and optimization algorithms; hydrogen energy-fuel cells
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Metaheuristic algorithms are a class of optimization algorithms that can solve complex engineering design problems by finding near-optimal solutions efficiently. These algorithms are based on iterative searches of the permissible space, using various heuristics and strategies to explore the design space and refine solutions over time. Metaheuristics are used extensively in many engineering disciplines, including mechanical, civil, electrical, and aerospace engineering, to optimize the performance of systems, components, and processes.
Some of the most popular metaheuristic algorithms used in engineering design include genetic algorithms, particle swarm optimization, simulated annealing, and ant colony optimization. These algorithms can efficiently solve complex optimization problems with many design variables and constraints, allowing engineers to quickly and accurately identify optimal solutions.
The use of metaheuristic algorithms in engineering design has several advantages, including the ability to handle nonlinear and nonconvex optimization problems, the ability to find near-optimal solutions in a reasonable amount of time, and the ability to handle large-scale optimization problems. However, the effectiveness of these algorithms depends on several factors, such as the quality of the initial design, the choice of optimization algorithm, and the selection of appropriate optimization parameters.
Overall, metaheuristic algorithms are an important tool for engineers to optimize the design of complex engineering systems and processes. By combining advanced algorithms with domain-specific knowledge and expertise, engineers can design systems that meet performance, cost, and other constraints while achieving optimal outcomes.
Dr. Łukasz Knypiński
Dr. Ramesh Devarapalli
Guest Editors
Manuscript Submission Information
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Keywords
- metaheuristic optimization algorithms
- genetic algorithms
- particle swarm optimization
- simulated annealing
- engineering design
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