An introduction to differential evolution
New ideas in optimization
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
Instance generators and test suites for the multiobjective quadratic assignment problem
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
A scalable multi-objective test problem toolkit
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Solving rotated multi-objective optimization problems using differential evolution
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
SNDL-MOEA: stored non-domination level MOEA
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Improving the Performance and Scalability of Differential Evolution
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Steady-state selection and efficient covariance matrix update in the multi-objective CMA-ES
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Niching without niching parameters: particle swarm optimization using a ring topology
IEEE Transactions on Evolutionary Computation
A modified particle swarm optimization for correlated phenomena
Applied Soft Computing
Rotationally invariant crossover operators in evolutionary multi-objective optimization
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
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This paper presents four rotatable multi-objective test problems that are designed for testing EMO (Evolutionary Multi-objective Optimization) algorithms on their ability in dealing with parameter interactions. Such problems can be solved efficiently only through simultaneous improvements to each decision variable. Evaluation of EMO algorithms with respect to this class of problem has relevance to real-world problems, which are seldom separable. However, many EMO test problems do not have this characteristic. The proposed set of test problems in this paper is intended to address this important requirement. The design principles of these test problems and a description of each new test problem are presented. Experimental results on these problems using a Differential Evolution Multi-objective Optimization algorithm are presented and contrasted with the Non-dominated Sorting Genetic Algorithm II (NSGA-II).