Name
Title | ||
|---|---|---|
MOEA/D with biased weight adjustment inspired by user preference and its application on multi-objective reservoir flood control problem |
Abstract | ||
|---|---|---|
Related to the safety of public lives and property in the lower area of reservoirs, flood control is a priority for most large reservoirs. Considering both dam safety and downstream flood control, reservoir flood control is a multi-objective problem (MOP). To meet the needs of irrigation and generating electricity after the flood, the decision maker usually has his/her preferred final scheduling water level. To deal with this kind of MOP with user-preference information, we incorporate user-preference information into the framework of MOEA/D (multi-objective evolutionary algorithm-based decomposition). The widely used preference information is mainly composed of reference points and preference directions. Compared with the Pareto dominance-based multi-objective evolutionary algorithms (MOEAs), MOEA/D can naturally include two kinds of preference information since MOEA/D is directly based on the reference point and the preference direction. The weight vector of a subproblem in MOEA/D is just its preference. Aiming to obtain uniformly distributed solutions on the objective space, one of innovation points in this paper is using modified Tchebycheff decomposition instead of Tchebycheff decomposition as the decomposition method. To focus the search on the interesting regions of decision maker, the other innovation point in this paper is to integrate biased subproblem (weight vector) adjustment into the framework of MOEA/D. The distribution of subproblems (weight vectors) are adjusted periodically so that the subproblems are re-distributed adaptively to search the interesting regions. Some subproblems, which are far away from the preference regions, are deleted. And then some new subproblems, which are expected to search the preference regions, are added into the current evolutionary population. The efficiency and the effectiveness of the proposed algorithm are assessed through multi-objective reservoir flood control problem and two- to ten-objective test problems. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s00500-015-1789-z | Soft Computing - A Fusion of Foundations, Methodologies and Applications |
Keywords | Field | DocType |
Multi-objective optimization, Evolutionary algorithm, Decomposition, Preference, Biased weight vector adjustment, Reservoir flood control | Population,Evolutionary algorithm,Scheduling (computing),Computer science,Multi-objective optimization,Decomposition method (constraint satisfaction),Theoretical computer science,Artificial intelligence,Mathematical optimization,Weight,Machine learning,Pareto principle,Flood myth | Journal |
Volume | Issue | ISSN |
20 | 12 | 1432-7643 |
Citations | PageRank | References |
15 | 0.58 | 28 |
Authors | ||
11 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoliang Ma | 1 | 182 | 18.51 |
| Fang Liu | 2 | 1188 | 125.46 |
| Yutao Qi | 3 | 81 | 7.35 |
| Ling-Ling Li | 4 | 150 | 11.32 |
| Licheng Jiao | 5 | 5698 | 475.84 |
| xiaozheng deng | 6 | 15 | 0.58 |
| xiaodong wang | 7 | 15 | 0.58 |
| bei dong | 8 | 15 | 0.58 |
| zhanting hou | 9 | 15 | 0.58 |
| yongxiao zhang | 10 | 15 | 0.58 |
| Jianshe Wu | 11 | 326 | 15.78 |