Objective reduction based on nonlinear correlation information entropy

Wang, Handing; Yao, Xin

doi:10.1007/s00500-015-1648-y

Cited by 70 publications

(35 citation statements)

References 56 publications

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“…Existing research on MaOPs can be roughly divided into four categories, objective reduction [32], [33], incorporation of preferences [34], modified dominance relationships, and introduction of additional selection criteria. In case there is a strong correlation between objectives, some objectives can be removed [35].…”

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confidence: 99%

Diversity Assessment in Many-Objective Optimization

Wang

Jin

Yao

2017

IEEE Trans. Cybern.

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213

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Abstract-Maintaining diversity is one important aim of multiobjective optimization. However, diversity for many-objective optimization problems is less straightforward to define than for multi-objective optimization problems. Inspired by measures for biodiversity, we propose a new diversity metric for manyobjective optimization, which is an accumulation of the dissimilarity in the population, where an Lp-norm-based (p < 1) distance is adopted to measure the dissimilarity of solutions. Empirical results demonstrate our proposed metric can more accurately assess the diversity of solutions in various situations. We compare the diversity of the solutions obtained by four popular many-objective evolutionary algorithms using the proposed diversity metric on a large number of benchmark problems with two to ten objectives. The behaviors of different diversity maintenance methodologies in those algorithms are discussed in depth based on the experimental results. Finally, we show that the proposed diversity measure can also be employed for enhancing diversity maintenance or reference set generation in many-objective optimization.

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confidence: 99%

Diversity Assessment in Many-Objective Optimization

Wang

Jin

Yao

2017

IEEE Trans. Cybern.

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213

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“…Reference [20] transformed MaOPs into several single-objective optimization problems by aggregation functions with a series of weight vectors, but its performance on MaOPs with highly correlated objectives is modest. An objective reduction approach was proposed in [21] to select the most conflicting objectives during the execution of MOEAs, and simulation results showed that this approach improved the performance of Pareto-based MOEAs significantly. However, this objective reduction approach might be ineffective for MaOPs without redundant objectives.…”

Section: Introductionmentioning

confidence: 99%

Many-objective optimization for coordinated operation of integrated electricity and gas network

Kou

Zheng

2017

J. Mod. Power Syst. Clean Energy

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This paper develops a many-objective optimization model, which contains objectives representing the interests of the electricity and gas networks, as well as the distributed district heating and cooling units, to coordinate the benefits of all parties participated in the integrated energy system (IES). In order to solve the many-objective optimization model efficiently, an improved objective reduction (IOR) approach is proposed, aiming at acquiring the smallest set of objectives. The IOR approach utilizes the Spearman's rank correlation coefficient to measure the relationship between objectives based on the Pareto-optimal front captured by the multi-objective group search optimizer with adaptive covariance and Lévy flights algorithm, and adopts various strategies to reduce the number of objectives gradually. Simulation studies are conducted on an IES consisting of a modified IEEE 30-bus electricity network and a 15-node gas network. The results show that the many-objective optimization problem is transformed into a bi-objective formulation by the IOR. Furthermore, our approach improves the overall quality of dispatch solutions and alleviates the decision making burden.

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“…The conflict might be global or local [141][142][143]. For locally conflicting objectives, they are conflicting with each other in some regions but not in other regions.…”

Section: Relationship Between Objectivesmentioning

confidence: 99%

A mini-review on preference modeling and articulation in multi-objective optimization: current status and challenges

Wang

Olhofer

Jin

2017

Complex Intell. Syst.

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128

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Evolutionary multi-objective optimization aims to provide a representative subset of the Pareto front to decision makers. In practice, however, decision makers are usually interested in only a particular part of the Pareto front of the multi-objective optimization problem. This is particularly true when the number of objectives becomes large. Over the past decade, preference-based multi-objective optimization has attracted increasing attention from both academia and industry due to its significance in both theory and practice. Significant progress has been made in evolutionary multiobjective optimization and multi-criteria decision communities, although many open issues still remain to be addressed. This paper provides a concise review on preference-based multi-objective optimization, including various preference modeling methods and existing preference-based optimization methods, as well as a brief discussion of the main future challenges.

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Objective reduction based on nonlinear correlation information entropy

Cited by 70 publications

References 56 publications

Diversity Assessment in Many-Objective Optimization

Diversity Assessment in Many-Objective Optimization

Many-objective optimization for coordinated operation of integrated electricity and gas network

A mini-review on preference modeling and articulation in multi-objective optimization: current status and challenges

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