3 research outputs found
Evolutionary Dynamic Multi-Objective Optimisation : A survey
Peer reviewedPostprin
Performance measures for dynamic multi-objective optimisation algorithms
When algorithms solve dynamic multi-objective optimisation problems (DMOOPs), performance
measures are required to quantify the performance of the algorithm and to compare
one algorithm’s performance against that of other algorithms. However, for dynamic multiobjective
optimisation (DMOO) there are no standard performance measures. This article
provides an overview of the performance measures that have been used so far. In addition,
issues with performance measures that are currently being used in the DMOO literature are
highlighted.http://www.elsevier.com/locate/insmv201
Benchmarks for dynamic multi-objective optimisation algorithms
Algorithms that solve Dynamic Multi-Objective Optimisation Problems (DMOOPs) should be tested on benchmark functions to determine whether the algorithm can overcome specific difficulties that can occur in real-world problems. However, for Dynamic Multi-Objective Optimisation (DMOO), no standard benchmark functions are used. A number of DMOOPs have been proposed in recent years. However, no comprehensive overview of DMOOPs exist in the literature. Therefore, choosing which benchmark functions to use is not a trivial task. This article seeks to address this gap in the DMOO literature by providing a comprehensive overview of proposed DMOOPs, and proposing characteristics that an ideal DMOO benchmark function suite should exhibit. In addition, DMOOPs are proposed for each characteristic. Shortcomings of current DMOOPs that do not address certain characteristics of an ideal benchmark suite are highlighted. These identified shortcomings are addressed by proposing new DMOO benchmark functions with complicated Pareto-Optimal Sets (POSs), and approaches to develop DMOOPs with either an isolated or deceptive Pareto-Optimal Front (POF). In addition, DMOO application areas and real-world DMOOPs are discussed.http://surveys.acm.orghj201