Name
Abstract | ||
|---|---|---|
A conical area evolutionary algorithm (CAEA) is presented to further improve computational efficiencies of evolutionary algorithms for bi-objective optimization. CAEA partitions the objective space into a number of conical subregions and then solves a scalar subproblem in each subregion that uses a conical area indicator as its scalar objective. The local Pareto optimality of the solution with the minimal conical area in each subregion is proved. Experimental results on hi-objective problems have shown that CAEA offers a significantly higher computational efficiency than the multi-objective evolutionary algorithm based on decomposition (MOEA/D) while CAEA competes well with MOEA/D in terms of solution quality. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1587/transfun.E95.A.1420 | IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES |
Keywords | Field | DocType |
bi-objective optimization, evolutionary algorithm, computational complexity, conical area, local Pareto optimality | Conical surface,Evolutionary algorithm,Theoretical computer science,Mathematics,Computational complexity theory | Journal |
Volume | Issue | ISSN |
E95A | 8 | 0916-8508 |
Citations | PageRank | References |
6 | 0.53 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiqin Ying | 1 | 25 | 6.67 |
| Xing Xu | 2 | 20 | 4.05 |
| Yuxiang Feng | 3 | 6 | 0.87 |
| Yu Wu | 4 | 420 | 63.58 |