Computational Intelligence Algorithms for Dynamic Multiobjective Optimization Problems
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 3173
Special Issue Editors
Interests: dynamic multiobjective optimization; many-objective optimization; constraint handling; decision making; visualization; computational intelligence algorithms and metaheuristics
Interests: nonlinear optimization; many- and multiobjective optimization; metamodeling; constraint handling; engineering design; evolutionary algorithms and metaheuristics; innovization; neural networks; data mining and machine learning
Special Issue Information
Dear Colleagues,
Most optimization problems have more than one objective, with at least two objectives in conflict with one another. Due to the conflicting objectives of the optimization problem, a single solution does not exist. Instead, a set of optimal trade-off solutions exist, referred to as the Pareto-optimal front (POF) or Pareto frontier. These optimization problems are referred to as multiobjective optimization problems (MOPs).
In many real-world situations, the optimization problem does not remain static but is dynamic and changes over time. However, in recent years, most research has focused on either static MOPs or dynamic single-objective optimization problems (DSOPs). When solving dynamic multiobjective optimization (DMOO) problems (DMOPs), an algorithm must track the changing POF over time by finding solutions as close as possible to the POF and maintaining a diverse set of solutions.
This Special Issue aims to highlight the latest developments in DMOO, and to bring together researchers from both academia and industry to address challenges in the field.
Topics of particular interest are:
- Theoretical analysis of computational intelligence DMOO algorithms (DMOAs);
- New approaches to compare and analyse the performance of DMOAs (performance measures, benchmarks, visualization);
- Fitness landscape analysis of DMOPs;
- Dealing with uncertainty when solving DMOPs;
- Decision making and incorporating decision maker preferences when solving DMOPs;
- Applying DMOAs to real-world DMOPs;
- Comparing the performance of DMOAs to non-CI approaches on real-world DMOPs.
Dr. Marde Helbig
Prof. Dr. Kalyanmoy Deb
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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
- Dynamic multiobjective optimization
- Computational intelligence algorithms
- Fitness landscape analysis
- Decision making
- Uncertainty
- Real-world problems
