Abstract
Service composition aims to search a composition plan of candidate services that produces the optimal results with respect to multiple and possibly conflicting Quality-of-Service (QoS) attributes, e.g., latency, throughput and cost. This leads to a multi-objective optimization problem for which evolutionary algorithm is a promising solution. In this paper, we investigate different ways of injecting knowledge about the problem into the Multi-Objective Evolutionary Algorithm (MOEA) by seeding. Specifically, we propose four alternative seeding strategies to strengthen the quality of the initial population for the MOEA to start working with. By using the real-world WS-DREAM dataset, we conduced experimental evaluations based on 9 different workflows of service composition problems and several metrics. The results confirm the effectiveness and efficiency of those seeding strategies. We also observed that, unlike the discoveries for other problem domains, the implication of the number of seeds on the service composition problems is minimal, for which we investigated and discussed the possible reasons. © 2018 Copyright held by the owner/author(s).
| Original language | English |
|---|---|
| Title of host publication | GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 1419-1426 |
| Number of pages | 8 |
| ISBN (Print) | 9781450356183 |
| DOIs | |
| Publication status | Published - 2 Jul 2018 |
| Externally published | Yes |
Keywords
- Evolutionary algorithm
- Multiobjective optimization
- Search-based software engineering
- Seeding strategy
- Service composition