Name
Abstract | ||
|---|---|---|
Multi-objective optimization deals with problems having two or more conflicting objectives that have to be optimized simultaneously. When the objectives change somehow with time, the problems become dynamic, and if the decision maker indicates preferences at runtime, then the algorithms to solve them become interactive. In this paper, we propose the integration of SMPSO/RP, an interactive multi-objective particle swarm optimizer based on SMPSO, with InDM2, an algorithmic template for dynamic interactive optimization with metaheuristics. The result is SMPSO/RPD, an algorithm that provides the search capabilities of SMPSO, incorporates an interactive preference articulation mechanism based on defining one or more reference points, and is able to deal with dynamic problems. We conduct a qualitative study showing the working of SMPSO/RPD on three benchmark problems, remaining a qualitative analysis as an open line of future research. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s13748-019-00198-8 | Progress in Artificial Intelligence |
Keywords | Field | DocType |
Multi-objective optimization, Particle swarm optimization, Interactive decision making, Dynamic optimization problem, Comparative study | Particle swarm optimization,Mathematical optimization,Interactive optimization,Computer science,Multi-objective optimization,Artificial intelligence,Dynamic problem,Machine learning,Decision maker,Particle swarm optimizer,Metaheuristic | Journal |
Volume | Issue | ISSN |
9 | 1 | 2192-6352 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cristobal Barba Gonzalez | 1 | 14 | 3.66 |
| Antonio J. Nebro | 2 | 1118 | 54.62 |
| José García-Nieto | 3 | 348 | 25.75 |
| José F. Aldana-Montes | 4 | 72 | 12.82 |