Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Multi-objective optimization using self-adaptive differential evolution algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A robust stochastic genetic algorithm (StGA) for global numerical optimization
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
SDE: a stochastic coding differential evolution for global optimization
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Enhance differential evolution with random walk
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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Many real-world applications can be modeled as multi-objective optimization problems (MOPs). Applying differential evolution (DE) to MOPs is a promising research topic and has drawn a lot of attention in recent years. To search high-quality solutions for MOPs, this paper presents a robust adaptive DE (termed AS-MODE) with following two features. First, a stochastic coding strategy is used to improve the solution quality. This coding strategy represents each individual by a stochastic region, which enables the algorithm to fine-tune solutions efficiently. Second, a probability-based adaptive control strategy is utilized to reduce the influence of parameter settings. The adaptive control strategy associates each parameter with a candidate value set. Better candidate values would have higher selection probabilities to generate new individuals. The performance of the proposed AS-MODE is compared with several highly regarded multi-objective evolutionary algorithms. Simulation results on ten benchmark test functions with different characteristics reveal that AS-MODE yields very promising performance.