A Multiobjective Brain Storm Optimization Algorithm Based on Decomposition

Dai, Cai; Lei, Xiujuan

doi:10.1155/2019/5301284

Cited by 13 publications

(5 citation statements)

References 35 publications

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“…The description of quality in MOPs is significantly more complex compared with that in the optimization of singleobjective problems. Hence, reasonable convergence and diversity are used as metrics to recognize better MOAs [21,29]. To confirm the performance of MOLPB we utilize the ZDT benchmarks.…”

Section: Resultsmentioning

confidence: 99%

Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems

Rahman¹,

Rashid

Ahmed

et al. 2022

Neural Comput & Applic

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In this work, a new multiobjective optimization algorithm called multiobjective learner performance-based behavior algorithm is proposed. The proposed algorithm is based on the process of transferring students from high school to college. The proposed technique produces a set of non-dominated solutions. To judge the ability and efficacy of the proposed multiobjective algorithm, it is evaluated against a group of benchmarks and five real-world engineering optimization problems. Additionally, to evaluate the proposed technique quantitatively, several most widely used metrics are applied. Moreover, the results are confirmed statistically. The proposed work is then compared with three multiobjective algorithms, which are MOWCA, NSGA-II, and MODA. Similar to the proposed technique, the other algorithms in the literature were run against the benchmarks, and the real-world engineering problems utilized in the paper. The algorithms are compared with each other employing descriptive, tabular, and graphical demonstrations. The results proved the ability of the proposed work in providing a set of non-dominated solutions, and that the algorithm outperformed the other participated algorithms in most of the cases.

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Section: Resultsmentioning

confidence: 99%

Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems

Rahman¹,

Rashid

Ahmed

et al. 2022

Neural Comput & Applic

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show abstract

“…Note that most of these algorithms adopt additional reference information (reference vectors, reference points, or weight vectors) during the environmental selection, which helps to maintain the diversity of the population. Due to the advantage with a better mathematical explanation, decomposition-based MOEAs have become very popular in recent years [21][22][23][24].…”

Section: Decomposition-based Moeas Decomposition-basedmentioning

confidence: 99%

Decomposition-Based Multiobjective Evolutionary Optimization with Adaptive Multiple Gaussian Process Models

Zhang

Gong

et al. 2020

Complexity

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In recent years, a number of recombination operators have been proposed for multiobjective evolutionary algorithms (MOEAs). One kind of recombination operators is designed based on the Gaussian process model. However, this approach only uses one standard Gaussian process model with fixed variance, which may not work well for solving various multiobjective optimization problems (MOPs). To alleviate this problem, this paper introduces a decomposition-based multiobjective evolutionary optimization with adaptive multiple Gaussian process models, aiming to provide a more effective heuristic search for various MOPs. For selecting a more suitable Gaussian process model, an adaptive selection strategy is designed by using the performance enhancements on a number of decomposed subproblems. In this way, our proposed algorithm owns more search patterns and is able to produce more diversified solutions. The performance of our algorithm is validated when solving some well-known F, UF, and WFG test instances, and the experiments confirm that our algorithm shows some superiorities over six competitive MOEAs.

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“…The proposed model above is a bi-level optimization problem and is a NP-hard optimization problem. Usual algorithms cannot solve this problem efficiently [9], [14], [46]. To solve the proposed bi-level mathematical model effectively, we propose an improved genetic algorithm and denote it as GAL.…”

Section: Proposed Genetic Algorithmmentioning

confidence: 99%

A New Bi-Level Mathematical Model and Algorithm for VONs Mapping Problem

et al. 2020

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Elastic optical networks (EONs) virtualization can allow the virtual optical networks (VONs) to utilize all the physical resources of EONs, and can attain a high performance of the networks. However, the optimal scheme for VONs mapping should be determined. To tackle these challenges, a bi-level mathematical model is established. leader's and follower's objectives are to minimize energy consumption and the maximum index of used frequency slots, respectively. The bi-level mathematical model can determine the optimal schemes of VONs mapping. To solve the mathematical model effectively, a uniform design method is applied to generate initial population for the lower level problem. In addition, To solve the whole model effectively, a tailor-made encoding, population initialization, genetic operators and local search operator are designed. An efficient genetic algorithm with local search operator is proposed for the bilevel mathematical model. To evaluate the mathematical model and the designed algorithm, a large number of experiments are performed on three kinds of the widely used networks, and the experimental results indicate that the effectiveness of the proposed bi-level mathematical model and designed algorithms.

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A Multiobjective Brain Storm Optimization Algorithm Based on Decomposition

Cited by 13 publications

References 35 publications

Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems

Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems

Decomposition-Based Multiobjective Evolutionary Optimization with Adaptive Multiple Gaussian Process Models

A New Bi-Level Mathematical Model and Algorithm for VONs Mapping Problem

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