Special Issue on: "New Trends in Many-Objective Optimisation"
Dr. Gai-Ge Wang, Jiangsu Normal University, China
Dr. Yi Mei and Dr. Mengjie Zhang, Victoria University of Wellington, New Zealand
Dr. Witold Pedrycz, University of Alberta, Canada
The optimisation problems with only one, two/three, and more than three objectives are called single-objective, multi-objective, and many-objective optimisation (MOO), respectively. Because MOO is much closer to real life, it has attracted more and more attention. During recent decades, although many methods have been proposed and successfully applied to cope with single and multi-objective optimisation problems, MOO problems have not been fully addressed. Especially, the performance of most existing algorithms seriously degrades when dealing with MOO problems. However, MOO problems are widely seen in the real-world and therefore efficient solutions to them are of great practical relevance.
The MOO is indeed a topic of interest amongst researchers in various fields of science and engineering as well as commence. The challenge to the existing solutions to MOO problems is how to strike a good balance between convergence (accuracy) and coverage (distribution/diversity). In addition, existing performance indicators for multi-objective optimisation may become incapable of accurately assessing and comparing the quality of solution sets for MOO problems with four or more objectives. Finally, visualisation of the solutions of MOO problems also becomes a grand challenge.
Suitable topics include, but are not limited to, the following:
- Improvements of traditional metaheuristics to solve MOO problems (e.g. genetic algorithms, genetic programming, evolutionary strategies, evolutionary programming, ant colony optimisation and particle swarm optimisation, monarch butterfly optimisation, earthworm optimisation algorithm, elephant herding optimisation, NSGA-II, MOEA/D, and MOPSO)
- Recent development of novel MOO algorithms (e.g. NSGA-III, HypE, and SPEA2)
- Reproduction operators of MOO algorithms
- Performance indicators of MOO algorithms
- Visualization techniques for MOO
- Preference-based many-objective optimisation
- Objective reduction
- New fitness assignment schemes and pareto dominance relationship
- New diversity preservation methods
- Large scale many-objective optimisation
- Theoretical study on MOO algorithms using various techniques (e.g. rough set, Markov chain, dynamic system, complex system/networks, and martingale)
- Application of MOO algorithms (e.g. planning and scheduling and other combinatorial problems, data mining and machine learning, reliability, task assignment problem, IIR filter design, optimisation under dynamic and uncertain environments)
Notes for Prospective Authors
Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. (N.B. Conference papers may only be submitted if the paper has been completely re-written and if appropriate written permissions have been obtained from any copyright holders of the original paper).
All papers are refereed through a peer review process.
All papers must be submitted online. To submit a paper, please read our Submitting articles page.
If you have any queries concerning this special issue, please email the Guest Editor Gai-Ge Wang at firstname.lastname@example.org.
Manuscripts due by: 1 May, 2017
Notification to authors: 1 July, 2017
Final versions due by: 1 October, 2017