Name
Abstract | ||
|---|---|---|
Invasive Weed Optimization (IWO) was recently proposed as a simple but powerful metaheuristic algorithm for real parameter optimization. IWO draws inspiration from the ecological process of weeds colonization and distribution and is capable of solving general multi-dimensional, linear and nonlinear optimization problems with appreciable efficiency. This article extends the basic IWO for tackling multi-objective optimization problems that aim at achieving two or more objectives (very often conflicting) simultaneously. The concept of fuzzy dominance has been used to sort the promising candidate solutions at each iteration. The new algorithm has been shown to be statistically significantly better than some state of the art existing evolutionary multi-objective algorithms, namely NSGAIILS, DECMOSA-SQP, MOEP, Clustering MOEA, GDE3, and MOEADGM on a 12-function test-suite (including both unconstrained and constrained problems) from the IEEE CEC (Congress on Evolutionary Computation) 2009 competition and special session on multi-objective optimization algorithms. The following performance metrics were considered: IGD, Spacing, and Minimum Spacing. Our experimental results suggest that IWO holds immense promise to appear as an efficient metaheuristic for multi-objective optimization. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.ins.2010.09.026 | Inf. Sci. |
Keywords | Field | DocType |
multi-objective optimization problem,evolutionary multi-objective algorithm,multi-objective optimization,powerful metaheuristic algorithm,efficient metaheuristic,artificial weed colony,basic iwo,nonlinear optimization problem,new algorithm,real parameter optimization,multi-objective optimization algorithm,pareto front,nonlinear optimization,metaheuristics,multi objective optimization,swarm intelligence,evolutionary computing,functional testing | Derivative-free optimization,Mathematical optimization,Meta-optimization,Test functions for optimization,Evolutionary computation,Multi-swarm optimization,Multi-objective optimization,Artificial intelligence,Optimization problem,Machine learning,Mathematics,Metaheuristic | Journal |
Volume | Issue | ISSN |
181 | 12 | 0020-0255 |
Citations | PageRank | References |
48 | 1.41 | 23 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Debarati Kundu | 1 | 158 | 8.83 |
| Kaushik Suresh | 2 | 166 | 7.66 |
| Sayan Ghosh | 3 | 237 | 16.12 |
| Swagatam Das | 4 | 6026 | 276.66 |
| B. K. Panigrahi | 5 | 446 | 30.87 |
| Sanjoy Das | 6 | 226 | 39.18 |