Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Do additional objectives make a problem harder?
Proceedings of the 9th annual conference on Genetic and evolutionary computation
The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics)
The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics)
A new MOEA for multi-objective TSP and Its convergence property analysis
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Deterministic helper-objective sequences applied to job-shop scheduling
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Probabilistic based evolutionary optimizers in bi-objective travelling salesman problem
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Helper-objective optimization strategies for the Job-Shop Scheduling Problem
Applied Soft Computing
Multimodal optimization using a bi-objective evolutionary algorithm
Evolutionary Computation
Multiobjectivizing the HP model for protein structure prediction
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
Locality-based multiobjectivization for the HP model of protein structure prediction
Proceedings of the 14th annual conference on Genetic and evolutionary computation
An improved multiobjectivization strategy for HP model-based protein structure prediction
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
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This paper studies the multi-objectivization of single-objective optimization problems (SOOP) using evolutionary multi-objective algorithms (EMOAs). In contrast to the single-objective case, diversity can be introduced by the multi-objective view of the algorithm and the dynamic use of objectives. Using the travelling salesman problem as an example we illustrate that two basic approaches, a) the addition of new objectives to the existing problem and b) the decomposition of the primary objective into sub-objectives, can improve performance compared to a single-objective genetic algorithm when objectives are used dynamically. Based on decomposition we propose the concept "Multi-Objectivization via Segmentation" (MOS), at which the original problem is reassembled. Experiments reveal that this new strategy clearly outperforms both the traditional genetic algorithm (GA) and the algorithms based on existing multiobjective approaches even without changing objectives.