Memetic algorithms: a short introduction
New ideas in optimization
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Genetic Algorithms in Noisy Environments
Machine Learning
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Averaging Efficiently in the Presence of Noise
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Evolution Strategies on Noisy Functions: How to Improve Convergence Properties
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Creating Robust Solutions by Means of Evolutionary Algorithms
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Designing evolutionary algorithms for dynamic optimization problems
Advances in evolutionary computing
Scheduling of genetic algorithms in a noisy environment
Evolutionary Computation
A Hybrid Algorithm of Immune Algorithm and Gradient Search for Multiple Solution Search
ICICIC '07 Proceedings of the Second International Conference on Innovative Computing, Informatio and Control
Use of biased neighborhood structures in multiobjective memetic algorithms
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Emerging Trends in Soft Computing - Memetic Algorithms; Guest Editors: Yew-Soon Ong, Meng-Hiot Lim, Ferrante Neri, Hisao Ishibuchi
Trade-off between performance and robustness: an evolutionary multiobjective approach
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
Multiobjective Particle Swarm Algorithm With Fuzzy Clustering for Electrical Power Dispatch
IEEE Transactions on Evolutionary Computation
A Multiobjective Memetic Algorithm Based on Particle Swarm Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust portfolio selection with uncertain exit time using worst-case VaR strategy
Operations Research Letters
A hybrid search strategy to enhance multiple objective optimization
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
Multi-Objective particle swarm optimization algorithm based on differential populations
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
A modified particle swarm optimizer for engineering design
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
Structural and Multidisciplinary Optimization
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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 biobjective 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.