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MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
  • Q. Zhang, H. Li
  • Computer Science, Mathematics
  • IEEE Transactions on Evolutionary Computation
  • 1 December 2007
Decomposition is a basic strategy in traditional multiobjective optimization. However, it has not yet been widely used in multiobjective evolutionary optimization. This paper proposes aExpand
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Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II
  • H. Li, Q. Zhang
  • Computer Science, Mathematics
  • IEEE Trans. Evol. Comput.
  • 1 February 2009
Partly due to lack of test problems, the impact of the Pareto set (PS) shapes on the performance of evolutionary algorithms has not yet attracted much attention. This paper introduces a general classExpand
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Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters
Trial vector generation strategies and control parameters have a significant influence on the performance of differential evolution (DE). This paper studies whether the performance of DE can beExpand
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Multiobjective optimization Test Instances for the CEC 2009 Special Session and Competition
Multiobjective optimization Test Instances for the CEC 2009 Special Session and Competition Qingfu Zhang∗, Aimin Zhou∗, Shizheng Zhao†, Ponnuthurai Nagaratnam Suganthan†, Wudong Liu∗and SantoshExpand
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The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances
This paper describes the idea of MOEA/D and proposes a strategy for allocating the computational resource to different subproblems in MOEA/D. The new version of MOEA/D has been tested on all theExpand
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An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition
Achieving balance between convergence and diversity is a key issue in evolutionary multiobjective optimization. Most existing methodologies, which have demonstrated their niche on various practicalExpand
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RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm
  • Q. Zhang, A. Zhou, Y. Jin
  • Computer Science, Mathematics
  • IEEE Transactions on Evolutionary Computation
  • 1 February 2008
Under mild conditions, it can be induced from the Karush-Kuhn-Tucker condition that the Pareto set, in the decision space, of a continuous multiobjective optimization problem is a piecewiseExpand
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Multiobjective evolutionary algorithms: A survey of the state of the art
Abstract A multiobjective optimization problem involves several conflicting objectives and has a set of Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionaryExpand
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Decomposition of a Multiobjective Optimization Problem Into a Number of Simple Multiobjective Subproblems
This letter suggests an approach for decomposing a multiobjective optimization problem (MOP) into a set of simple multiobjective optimization subproblems. Using this approach, it proposes MOEA/D-M2M,Expand
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Expensive Multiobjective Optimization by MOEA/D With Gaussian Process Model
In some expensive multiobjective optimization problems (MOPs), several function evaluations can be carried out in a batch way. Therefore, it is very desirable to develop methods which can generateExpand
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