AI-Driven Engineering Optimization
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 510
Special Issue Editors
Interests: engineering modelling and optimization; evolutionary computation; multimodal optimization; mechanical engineering; species optimization
2. Technical Instructor Training Institute, Middle Technical University, Baghdad 10074, Iraq
Interests: modelling, simulation and optimization of membrane and thermal water desalination and wastewater treatment; hybrid systems based renewable energy
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid evolution of artificial intelligence (AI) is transforming the landscape of engineering design, analysis, and optimization. However, this evolution is accompanied by increasing complex challenges. Modern engineering systems are characterized by intricate interdependencies, vast data streams, and a constant need to adapt to dynamic environments. Traditional engineering optimizations struggle to address those challenges with need for real-time optimal decision-making, the inherent uncertainties of complex systems, and large scale of data.
From generative design algorithms to physics-informed neural networks, AI-driven methods are unlocking unprecedented opportunities to solve complex, multi-disciplinary engineering challenge. The intersection and combination of artificial intelligence and optimization will present new ways for developing innovative engineering solutions. AI has a strong capacity for pattern recognition, and decision analysis, while the strengths of engineering optimization are optimization, simulation and decision-making of vast engineering problems. Their combination will help us to develop next generation optimization methods that are not only efficient and effective, but also robust, adaptable and capable of exploring the complexities of the interconnected world.
This Special Issue invites researchers, practitioners and engineers to share work at the intersection of AI, machine learning and engineering optimization to explore cutting-edge advancements, practical applications and future directions in AI-driven optimization across engineering domains, topics of interest include but not limited to:
- AI/ML techniques for engineering optimization
- AI techniques: Neural networks, machine learning.
- Evolutionary computation: including genetic algorithms, particle swarm optimization, ant colony optimization and differential evolution etc.
- Hybrid models combining AI with traditional numerical methods.
- Generative /Multimodal optimization: explore new solutions.
- Design automation.
- Data-driven optimization.
- Applications across Engineering Disciplines
- Aerospace, automotives, civil, mechanical, chemical and energy systems etc.
- Real-time optimization for robotics and smart engineering design.
- Large scale engineering optimization.
- AI applications in engineering optimization.
Dr. Jian-Ping Li
Dr. Mudhar Al-Obaidi
Dr. Qingsheng Li
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- AI-driven engineering optimization
- artificial intelligence
- machine learning
- neural networks
- evolutionary computation
- genetic algorithm
- particle swarm optimization
- differential evolution
- swarm intelligence
- ant colon optimization
- cognitive computation
- modelling and optimization
- mechanical engineering
- chemical engineering
- water treatment
- electrical engineering
- aerospace engineering
- natural language processing
- data-driven optimization
- design automation
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