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A novel multiobjective evolutionary algorithm based on regression analysis.

Song Z, Wang M, Dai G, Vasile M - ScientificWorldJournal (2015)

Bottom Line: The result shows that MMEA-RA outperforms RM-MEDA and NSGA-II on the test instances with variable linkages.At the same time, MMEA-RA has higher efficiency than the other two algorithms.A few shortcomings of MMEA-RA have also been identified and discussed in this paper.

View Article: PubMed Central - PubMed

Affiliation: School of Computer, China University of Geosciences, Wuhan 430074, China.

ABSTRACT
As is known, the Pareto set of a continuous multiobjective optimization problem with m objective functions is a piecewise continuous (m - 1)-dimensional manifold in the decision space under some mild conditions. However, how to utilize the regularity to design multiobjective optimization algorithms has become the research focus. In this paper, based on this regularity, a model-based multiobjective evolutionary algorithm with regression analysis (MMEA-RA) is put forward to solve continuous multiobjective optimization problems with variable linkages. In the algorithm, the optimization problem is modelled as a promising area in the decision space by a probability distribution, and the centroid of the probability distribution is (m - 1)-dimensional piecewise continuous manifold. The least squares method is used to construct such a model. A selection strategy based on the nondominated sorting is used to choose the individuals to the next generation. The new algorithm is tested and compared with NSGA-II and RM-MEDA. The result shows that MMEA-RA outperforms RM-MEDA and NSGA-II on the test instances with variable linkages. At the same time, MMEA-RA has higher efficiency than the other two algorithms. A few shortcomings of MMEA-RA have also been identified and discussed in this paper.

No MeSH data available.


Related in: MedlinePlus

Illustration of the geometric meaning of expression (3).
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Related In: Results  -  Collection


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fig2: Illustration of the geometric meaning of expression (3).

Mentions: The geometric meaning of expression (3) is that a 3-dimensional line l can be seen as the intersecting line of two planes m1: x = az + c and m2: y = bz + d. Figure 2 illustrates this meaning.


A novel multiobjective evolutionary algorithm based on regression analysis.

Song Z, Wang M, Dai G, Vasile M - ScientificWorldJournal (2015)

Illustration of the geometric meaning of expression (3).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4385692&req=5

fig2: Illustration of the geometric meaning of expression (3).
Mentions: The geometric meaning of expression (3) is that a 3-dimensional line l can be seen as the intersecting line of two planes m1: x = az + c and m2: y = bz + d. Figure 2 illustrates this meaning.

Bottom Line: The result shows that MMEA-RA outperforms RM-MEDA and NSGA-II on the test instances with variable linkages.At the same time, MMEA-RA has higher efficiency than the other two algorithms.A few shortcomings of MMEA-RA have also been identified and discussed in this paper.

View Article: PubMed Central - PubMed

Affiliation: School of Computer, China University of Geosciences, Wuhan 430074, China.

ABSTRACT
As is known, the Pareto set of a continuous multiobjective optimization problem with m objective functions is a piecewise continuous (m - 1)-dimensional manifold in the decision space under some mild conditions. However, how to utilize the regularity to design multiobjective optimization algorithms has become the research focus. In this paper, based on this regularity, a model-based multiobjective evolutionary algorithm with regression analysis (MMEA-RA) is put forward to solve continuous multiobjective optimization problems with variable linkages. In the algorithm, the optimization problem is modelled as a promising area in the decision space by a probability distribution, and the centroid of the probability distribution is (m - 1)-dimensional piecewise continuous manifold. The least squares method is used to construct such a model. A selection strategy based on the nondominated sorting is used to choose the individuals to the next generation. The new algorithm is tested and compared with NSGA-II and RM-MEDA. The result shows that MMEA-RA outperforms RM-MEDA and NSGA-II on the test instances with variable linkages. At the same time, MMEA-RA has higher efficiency than the other two algorithms. A few shortcomings of MMEA-RA have also been identified and discussed in this paper.

No MeSH data available.


Related in: MedlinePlus