Abstract
In the real world, it is not uncommon to face an optimization problem with more than three objectives. Such problems, called many-objective optimization problems (MaOPs), pose great challenges to the area of evolutionary computation. The failure of conventional Pareto-based multi-objective evolutionary algorithms in dealing with MaOPs motivates various new approaches. However, in contrast to the rapid development of algorithm design, performance investigation and comparison of algorithms have received little attention. Several test problem suites which were designed for multi-objective optimization have still been dominantly used in many-objective optimization. In this paper, we carefully select (or modify) 15 test problems with diverse properties to construct a benchmark test suite, aiming to promote the research of evolutionary many-objective optimization (EMaO) via suggesting a set of test problems with a good representation of various real-world scenarios. Also, an open-source software platform with a user-friendly GUI is provided to facilitate the experimental execution and data observation.
Highlights
The field of evolutionary multi-objective optimization has developed rapidly over the last two decades, but the design of effective algorithms for addressing problems with more than three objectives remains a great challenge
The infeasibility of solutions’ direct observation can lead to serious difficulties in algorithms’ performance investigation and comparison. All of these suggest the pressing need of new methodologies designed for dealing with many-objective optimization problems (MaOPs), new performance metrics
Inverted generational distance (IGD) Let P∗ be a set of uniformly distributed points on the Pareto front
Summary
The field of evolutionary multi-objective optimization has developed rapidly over the last two decades, but the design of effective algorithms for addressing problems with more than three objectives (called many-objective optimization problems, MaOPs) remains a great challenge. The aggravation of the conflict between convergence and diversity, along with increasing time or space requirement as well as parameter sensitivity, has become key barriers to the design of effective many-objective optimization algorithms. The infeasibility of solutions’ direct observation can lead to serious difficulties in algorithms’ performance investigation and comparison. All of these suggest the pressing need of new methodologies designed for dealing with MaOPs, new performance metrics. Syst. (2017) 3:67–81 and benchmark functions tailored for experimental and comparative studies of evolutionary many-objective optimization (EMaO) algorithms
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
