A novel adaptive control strategy for decomposition-based multiobjective algorithm

Lin, Qiuzhen; Tang, Chaoyu; Ma, Yueping; Du, Zhihua; Li, Jianqiang; Chen, Jianyong; Ming, Zhong

doi:10.1016/j.cor.2016.08.012

Cited by 19 publications

(5 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…None of the solutions can be improved without affecting another objective value (Mukhopadhyay et al, 2015). Hence, selecting a final solution is an important task for the MODCPs (Lin et al, 2017). The three conventional methods to obtain the final solution are the independent objective method, the approach of finding the knee of the non-dominated front, and the ensemble of non-dominated solutions (Mukhopadhyay et al, 2015).…”

Section: Obtaining Final Clustering Resultsmentioning

confidence: 99%

Evolutionary state‐based novel multi‐objective periodic bacterial foraging optimization algorithm for data clustering

Chang

Tang²,

Niu

2021

Expert Systems

View full text Add to dashboard Cite

Clustering divides objects into groups based on similarity. However, traditional clustering approaches are plagued by their difficulty in dealing with data with complex structure and high dimensionality, as well as their inability in solving multi-objective data clustering problems. To address these issues, an evolutionary state-based novel multi-objective periodic bacterial foraging optimization algorithm (ES-NMPBFO) is proposed in this article. The algorithm is designed to alleviate the high-computing complexity of the standard bacterial foraging optimization (BFO) algorithm by introducing periodic BFO. Moreover, two learning strategies, global best individual (gbest) and personal historical best individual (pbest), are used in the chemotaxis operation to enhance the convergence speed and guide the bacteria to the optimum position. Two elimination-dispersal operations are also proposed to prevent falling into local optima and improve the diversity of solutions. The proposed algorithm is compared with five other algorithms on six validity indexes in two data clustering cases comprising nine general benchmark datasets and four credit risk assessment datasets.The experimental results suggest that the proposed algorithm significantly outperforms the competing approaches. To further examine the effectiveness of the proposed strategies, two variants of ES-NMPBFO were designed, and all three forms of ES-NMPBFO were tested. The experimental results show that all of the proposed strategies are conducive to the improvement of solution quality, diversity and convergence.

show abstract

Section: Obtaining Final Clustering Resultsmentioning

confidence: 99%

Evolutionary state‐based novel multi‐objective periodic bacterial foraging optimization algorithm for data clustering

Chang

Tang²,

Niu

2021

Expert Systems

View full text Add to dashboard Cite

show abstract

“…A DE mutation strategy is picked up from a candidate's DE pool according to a probability that depends on the successful rate to produce better solutions. In MOEA/D-CDE [24], an adaptive composite operator selection (ACOS) strategy was presented for MOEA/D. Four evolutionary operator pools are used in ACOS and their advantages are combined to provide stronger exploratory capabilities.…”

Section: (4) Adaptive Control Methodsmentioning

confidence: 99%

“…By this way, all the subproblems are tackled simultaneously, and the final solutions of the subproblems form an approximation to the Pareto set (PS) of the original MOP. In recent years, it has greatly attracted the interest of scientific researchers and thus numbers of MOEA/ D variant strategies such as dynamical resource allocation [15,16], enhanced evolutionary operators [17,18], preselection [19,20], adaptive control methods [21][22][23][24], and matching strategies [25,26] have been designed to further improve its performance.…”

Section: Introductionmentioning

confidence: 99%

A multiobjective evolutionary algorithm based on surrogate individual selection mechanism

Chen

Sheng

2019

Pers Ubiquit Comput

View full text Add to dashboard Cite

Recently, classification-based preselection (CPS) strategy for evolutionary multiobjective optimization has been found to be very effective and efficient for solving complicated multiobjective optimization problems (MOPs). However, this strategy can only classify the candidate solutions into different categories, but it is difficult to find out which one is the best. In order to overcome this shortcoming, we propose a surrogate individual selection mechanism for multiobjective evolutionary algorithm based on decomposition. In this mechanism, we get the best one from candidate solution set by surrogate model, which mitigates the risk of using CPS strategy. Furthermore, we generate candidate solution set through a new offspring generation strategy, which can improve the quality of the candidate solutions. Based on typical multiobjective evolutionary algorithm MOEA/D, we design a new algorithm framework, called MOEA/D-SISM, through integrating the proposed surrogate individual selection mechanism. We compare MOEA/D-SISM with other state-of-theart multiobjective evolutionary algorithms (MOEAs), and experimental results show that our proposed algorithm obtains the best performance.

show abstract

“…Based on the FRRMAB, many other AOS variants have been developed afterwards, such as [138][139][140][141]. In addition to AOS, [142][143][144] also consider adaptively control the parameters associated with the reproduction operator in order to achieve the best algorithm setup. Rather than adaptively selecting the "appropriate" reproduction operator on the fly, another line of research (e.g., [145][146][147]) is to build an ensemble of reproduction operators and use them simultaneously.…”

Section: Adaptive Operator Selection and Hyper-heuristicsmentioning

confidence: 99%

Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities

2021

Preprint

View full text Add to dashboard Cite

Decomposition has been the mainstream approach in the classic mathematical programming for multi-objective optimization and multi-criterion decision-making. However, it was not properly studied in the context of evolutionary multi-objective optimization until the development of multi-objective evolutionary algorithm based on decomposition (MOEA/D). In this article, we present a comprehensive survey of the development of MOEA/D from its origin to the current state-of-the-art approaches. In order to be self-contained, we start with a step-by-step tutorial that aims to help a novice quickly get onto the working mechanism of MOEA/D. Then, selected major developments of MOEA/D are reviewed according to its core design components including weight vector settings, subproblem formulations, selection mechanisms and reproduction operators. Besides, we also overviews some further developments for constraint handling, computationally expensive objective functions, preference incorporation, and real-world applications. In the final part, we shed some lights on emerging directions for future developments.

show abstract

A novel adaptive control strategy for decomposition-based multiobjective algorithm

Cited by 19 publications

References 51 publications

Evolutionary state‐based novel multi‐objective periodic bacterial foraging optimization algorithm for data clustering

Evolutionary state‐based novel multi‐objective periodic bacterial foraging optimization algorithm for data clustering

A multiobjective evolutionary algorithm based on surrogate individual selection mechanism

Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities

Contact Info

Product

Resources

About