Designing collection routes through bank branches
Computers and Operations Research
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Fuzzy Sets and Systems - Featured Issue: Selected papers from ACIDCA 2000
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Creating Robust Solutions by Means of Evolutionary Algorithms
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Design of combinational logic circuits through an evolutionary multiobjective optimization approach
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
A Study on Distribution Preservation Mechanism in Evolutionary Multi-Objective Optimization
Artificial Intelligence Review
A multi-objective genetic algorithm for robust design optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Fitness inheritance for noisy evolutionary multi-objective optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Application notes: MEBRA: multiobjective evolutionary-based risk assessment
IEEE Computational Intelligence Magazine
A systems approach to evolutionary multiobjective structural optimization and beyond
IEEE Computational Intelligence Magazine
Closed-loop evolutionary multiobjective optimization
IEEE Computational Intelligence Magazine
Trade-off between performance and robustness: an evolutionary multiobjective approach
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Reliability-based multi-objective optimization using evolutionary algorithms
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Genetic algorithms with a robust solution searching scheme
IEEE Transactions on Evolutionary Computation
Considerations in engineering parallel multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
The balance between proximity and diversity in multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Rank-density-based multiobjective genetic algorithm and benchmark test function study
IEEE Transactions on Evolutionary Computation
Dynamic multiobjective optimization problems: test cases, approximations, and applications
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Max-min surrogate-assisted evolutionary algorithm for robust design
IEEE Transactions on Evolutionary Computation
Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation
IEEE Transactions on Evolutionary Computation
A review of multiobjective test problems and a scalable test problem toolkit
IEEE Transactions on Evolutionary Computation
A general-purpose tunable landscape generator
IEEE Transactions on Evolutionary Computation
An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization
IEEE Transactions on Evolutionary Computation
The integer L-shaped method for stochastic integer programs with complete recourse
Operations Research Letters
Benchmarks for dynamic multi-objective optimisation algorithms
ACM Computing Surveys (CSUR)
Hi-index | 12.05 |
Multi-objective (MO) optimization is a challenging research topic because it involves the simultaneous optimization of several complex and conflicting objectives that requires researchers to address many issues which are unique to MO problems. However multi-objectivity is only one aspect of real-world applications and there is a growing interest in the optimization of solutions that are insensitive to parametric variations as well. In order to evaluate the capability of MO evolutionary algorithms (MOEAs) to find robust solutions, it is important to employ suitable test functions. In this paper, empirical studies are conducted to examine the suitability of existing robust test functions. Results suggest that these test functions have a bias towards the region where the robust solutions lie, rendering it difficult to assess the true capability of MOEAs. Motivated by such a finding, we present a framework for the construction of robust continuous MO test functions characterized by different noise-induced features. These noise-induced features can pose different difficulties to the optimization algorithms. A fitness-inheritance scheme is also presented and incorporated into two well-known MOEAs. Empirical analysis of the proposed robust MO test functions reveals that some noise-induced features present greater challenges to robust MOEAs as compared to existing robust test functions. In addition, the vehicle routing problem with stochastic demand (VRPSD) is presented as a practical example of robust combinatorial MO optimization problems. The work presented in this paper should encourage further studies and the development of more effective algorithms for robust MO optimization.