Multi-objective optimization problems (MOPs) arise regularly in real-world where multiple objectives are required to be optimized at the same time. So far, Evolutionary multi-objective optimization (EMO) algorithms have been demonstrated as effective in addressing MOPs with two and three objectives. However, they tend to face difficulties on addressing MOPs with four or more objectives, the so called Many-objective Optimization Problems (MaOPs).
Challenges to evolutionary algorithms and other metaheuristics in solving MaOPs include the inability of dominance based MOEAs to converge to the Pareto frontier while maintaining good diversity, the prohibitively high computational complexity for EMO algorithms based on performance indicators, and the difficulty for human users or decision makers to clearly understand the relationship between objectives and articulate preferences. Finally, visualization of the solutions of MaOPs also becomes a grand challenge.
This special issue– Advanced Methods for Evolutionary Many Objective Optimization, aims to discuss the philosophical changes needed in tackling MaOPs using evolutionary algorithms and in evaluating the quality of the solution sets they achieved. It will present most recent advances in theory, algorithm development and applications of evolutionary algorithms for MaOPs.
List of Topics
We cordially invite you to submit high-quality original research to this SI at Information Sciences (https://www.journals.elsevier.com/information-sciences ) addressing various topics related to many-objective optimization, but are not limited to:
Algorithm design for many-objective optimization
Benchmarks and performance indicators for many-objective optimization;
Dimensionality reduction, visualization techniques of many-objective optimization;
Constraint handling methods for many-objective optimization;
Preference articulation and decision making methods for many-objective optimization;
Hybrid algorithms for many-objective optimization;
Many-objective optimization in combinatorial/discrete, large-scale problems;
Many-objective optimization in dynamic environments;
Many-objective real-world optimization problems
All submissions should strictly follow the Author Guideline of Information Sciences (https://www.elsevier.com/journals/information-sciences/0020-0255/guide-for-authors ) .
Manuscript Due: February 1st 2018
First Decision Date: May 1st, 2018
Final Decision: July 1st, 2018
Please submit you manuscript at https://ees.elsevier.com/ins/default.asp?pg=login.asp
Prof. Rui Wang ( firstname.lastname@example.org), National University of Defense Technology, China/ The University of Sheffield, UK; http://ruiwangnudt.gotoip3.com/
Prof. Guohua Wu (email@example.com), National University of Defense Technology, China;