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Surrogate-Assisted Multi-objective Evolutionary Optimization
2021Solving many-objective optimization problems is challenging due to the increase in the number of objectives. The challenges include the increased complexity in the structure of the Pareto front, the increased number of solutions needed to represent the Pareto front, and the selection of solutions.
Yaochu Jin, Handing Wang, Chaoli Sun
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Guidance in evolutionary multi-objective optimization
Advances in Engineering Software, 2001zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Branke, Jürgen +2 more
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Dynamic Evolutionary Multi-objective Optimization
2009Many real-world systems include time-varying components and, very often, the environment in which they operate is in a constant state of flux. For problems involving such dynamic systems, the fitness landscape changes to reflect the time-varying requirements of the systems.
Chi-Keong Goh, Kay Chen Tan
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Noisy Evolutionary Multi-objective Optimization
2009In the previous chapter, we have described the multi-objective optimization problem and the challenges that it entails. However, the formulation presented in equation (1.1) assumes that the objectives can be found deterministically, which is hardly the case in many real world problems.
Chi-Keong Goh, Kay Chen Tan
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Robust Evolutionary Multi-objective Optimization
2009Branke [30] considered robust optimization as a special case of dynamic optimization, where solutions cannot be adapted fast enough to keep in pace with environmental changes. In such cases, it would be desirable to find solutions that perform reasonably well within some range of change.
Chi-Keong Goh, Kay Chen Tan
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Dynamic Multi-objective Optimization Evolutionary Algorithm
Third International Conference on Natural Computation (ICNC 2007), 2007A new evolutionary algorithm for Dynamic multiobjective optimization is proposed in this paper. First, the time period is divided into several random subperiods. In each subperiod, the problem is approximated by a static multi- objective optimization problem.
Chun-an Liu, Yuping Wang
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Preference-Based Evolutionary Multi-objective Optimization
2012 Eighth International Conference on Computational Intelligence and Security, 2012Evolutionary Multi-objective Optimization (EMO) approaches have been amply applied to find a representative set of Pareto-optimal solutions in the past decades. Although there are advantages of getting the range of each objective and the shape of the entire Pareto front for an adequate decision-making, the task of choosing a preferred set of Pareto ...
Zhenhua Li, Hai-Lin Liu
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A Hybrid Framework for Evolutionary Multi-Objective Optimization
IEEE Transactions on Evolutionary Computation, 2013Evolutionary multi-objective optimization algorithms are widely used for solving optimization problems with multiple conflicting objectives. However, basic evolutionary multi-objective optimization algorithms have shortcomings, such as slow convergence to the Pareto optimal front, no efficient termination criterion, and a lack of a theoretical ...
Sindhya, Karthik +3 more
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Recent Developments in Evolutionary Multi-Objective Optimization
2010By now evolutionary multi-objective optimization (EMO) is an established and a growing field of research and application with numerous texts and edited books, commercial software, freely downloadable codes, a biannual conference series running successfully since 2001, special sessions and workshops held at all major evolutionary computing conferences ...
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Dynamical multi-objective optimization evolutionary algorithm
SPIE Proceedings, 2003A dynamical multi-objective evolutionary algorithm (DMOEA) is proposed. It is the first study of the dynamical evolutionary algorithm (DEA) in multi-objective optimization process. All individuals called as particles in a population evolve through a new selection mechanism. We combine the selection mechanism in DEA and the elitists strategy in existing
Shengwu Xiong +3 more
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