Name
Title | ||
|---|---|---|
Multi-objective particle swarm optimization for robust optimization and its hybridization with gradient search |
Abstract | ||
|---|---|---|
This paper proposes an algorithm using multi-objective Particle Swarm Optimization (MOPSO) for finding robust solutions against small perturbations of design variables. If an optimal solution is sensitive to small perturbations of variables, it may be inappropriate or risky for practical use. Robust optimization finds solutions which are moderately good in terms of optimality and also good in terms of robustness against small perturbations of variables. The proposed algorithm formulates robust optimization as a bi-objective optimization problem, and finds robust solutions by searching for Pareto solutions of the bi-objective problem. This paper also proposes a hybridization of MOPSO and quasi-Newton method as an attempt to design effective memetic algorithm for robust optimization. Experimental results have shown that the proposed algorithms could find robust solutions effectively. The advantage and drawback of the hybridization were also clarified by the experiments, helping design an effective memetic algorithm for robust optimization. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CEC.2009.4983137 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
gradient search,design variable,robust solution,pareto solution,small perturbation,biobjective optimization problem,multi-objective particle swarm optimization,effective memetic algorithm,bi-objective problem,robust optimization,proposed algorithm,quasi newton method,particle swarm optimization,hypercubes,pareto optimization,robustness,design optimization,memetic algorithm,evolutionary computation,six sigma,hybridization,optimization,algorithm design and analysis,design methodology,optimization problem,data mining,product design | Mathematical optimization,Probabilistic-based design optimization,Derivative-free optimization,Robust optimization,Computer science,Meta-optimization,Multi-objective optimization,Multi-swarm optimization,Artificial intelligence,Optimization problem,Machine learning,Metaheuristic | Conference |
ISBN | Citations | PageRank |
978-1-4244-2959-2 | 11 | 0.62 |
References | Authors | |
17 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Satoshi Ono | 1 | 219 | 39.83 |
| Shigeru Nakayama | 2 | 75 | 16.14 |