Improved Evolutionary Operators for Sparse Large-Scale Multiobjective Optimization Problems

Kropp, Ian; Nejadhashemi, A. Pouyan; Deb, Kalyanmoy

doi:10.1109/tevc.2023.3256183

Cited by 9 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, owing to the sparsity of the correlation matrix of channels, the channel selection problem discussed in this paper also falls within the category of sparse large-scale MOPs. To assess the effectiveness of the proposed algorithm, TS-MOEA is compared with several advanced large-scale MOEAs, containing SpaseEA2 (Zhang et al, 2021 ), SLMEA (Tian et al, 2023 ), S-ECSO (Wang et al, 2022 ), CMMO (Ming et al, 2023 ), and S-NSGA-II (Kropp et al, 2023 ). Among these comparison algorithms, SparseEA2 is an effective sparse multi-objective optimization algorithm, whereas S-NSGA-II and S-ECSO are specialized for large-scale multi-objective optimization tasks.…”

Section: Resultsmentioning

confidence: 99%

“…The crossover probability is 1, while the mutation probability is 1/D; Kropp et al, 2023 Both crossover and mutation have a distribution index of 20.…”

Section: S-nsga-iimentioning

confidence: 99%

“…In the second type, SMOEAs combines the conventional framework of MOEAs (such as NSGA-II) and a hybrid encoding method of solutions. For example, S-NSGA-II (Kropp et al, 2023 ) introduces a novel set of evolutionary operators, which include Varied Striped Sparse Population Sampling (VSSPS), Sparse Simulated Binary Crossover (S-SBX), and Sparse Polynomial Mutation (S-PM), to address SLMOPs. The aforementioned operators demonstrate remarkable efficacy in solving SLMOPs, particularly when evaluated using HV.…”

Section: Backgroundsmentioning

confidence: 99%

See 2 more Smart Citations

Two-stage sparse multi-objective evolutionary algorithm for channel selection optimization in BCIs

Liu,

Wu,

et al. 2024

Front. Hum. Neurosci.

View full text Add to dashboard Cite

BackgroundChannel selection has become the pivotal issue affecting the widespread application of non-invasive brain-computer interface systems in the real world. However, constructing suitable multi-objective problem models alongside effective search strategies stands out as a critical factor that impacts the performance of multi-objective channel selection algorithms. This paper presents a two-stage sparse multi-objective evolutionary algorithm (TS-MOEA) to address channel selection problems in brain-computer interface systems.MethodsIn TS-MOEA, a two-stage framework, which consists of the early and late stages, is adopted to prevent the algorithm from stagnating. Furthermore, The two stages concentrate on different multi-objective problem models, thereby balancing convergence and population diversity in TS-MOEA. Inspired by the sparsity of the correlation matrix of channels, a sparse initialization operator, which uses a domain-knowledge-based score assignment strategy for decision variables, is introduced to generate the initial population. Moreover, a Score-based mutation operator is utilized to enhance the search efficiency of TS-MOEA.ResultsThe performance of TS-MOEA and five other state-of-the-art multi-objective algorithms has been evaluated using a 62-channel EEG-based brain-computer interface system for fatigue detection tasks, and the results demonstrated the effectiveness of TS-MOEA.ConclusionThe proposed two-stage framework can help TS-MOEA escape stagnation and facilitate a balance between diversity and convergence. Integrating the sparsity of the correlation matrix of channels and the problem-domain knowledge can effectively reduce the computational complexity of TS-MOEA while enhancing its optimization efficiency.

show abstract

Section: Resultsmentioning

confidence: 99%

“…The crossover probability is 1, while the mutation probability is 1/D; Kropp et al, 2023 Both crossover and mutation have a distribution index of 20.…”

Section: S-nsga-iimentioning

confidence: 99%

Section: Backgroundsmentioning

confidence: 99%

See 1 more Smart Citation

Two-stage sparse multi-objective evolutionary algorithm for channel selection optimization in BCIs

Liu,

Wu,

et al. 2024

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

Improving two-layer encoding of evolutionary algorithms for sparse large-scale multiobjective optimization problems

Jiang,

Wang,

Hong

et al. 2024

Complex Intell. Syst.

View full text Add to dashboard Cite

Sparse large-scale multiobjective problems (LSMOPs) are characterized as an NP-hard issue that undergoes a significant presence of zero-valued variables in Pareto optimal solutions. In solving sparse LSMOPs, recent studies typically employ a specialized two-layer encoding, where the low-level layer undertakes the optimization of zero variables and the high-level layer is in charge of non-zero variables. However, such an encoding usually puts the low-level layer in the first place and thus cannot achieve a balance between optimizing zero and non-zero variables. To this end, this paper proposes to build a two-way association between the two layers using a mutual preference calculation method and a two-way matching strategy. Essentially, the two-way association balances the influence of two layers on the encoded individual by relaxing the control of the low-level layer and enhancing the control of the high-level layer, thus reaching the balance between the optimizations of zero and non-zero variables. Moreover, we propose a new evolutionary algorithm equipped with the modules and compare it with several state-of-the-art algorithms on 32 benchmark problems. Extensive experiments verify its effectiveness, as the proposed modules can improve the two-layer encoding and help the algorithm achieve superior performance on sparse LSMOPs.

show abstract

A two-space-decomposition-based evolutionary algorithm for large-scale multiobjective optimization

Yin,

Cao

2023

Swarm and Evolutionary Computation

View full text Add to dashboard Cite

Improved Evolutionary Operators for Sparse Large-Scale Multiobjective Optimization Problems

Cited by 9 publications

References 28 publications

Two-stage sparse multi-objective evolutionary algorithm for channel selection optimization in BCIs

Two-stage sparse multi-objective evolutionary algorithm for channel selection optimization in BCIs

Improving two-layer encoding of evolutionary algorithms for sparse large-scale multiobjective optimization problems

A two-space-decomposition-based evolutionary algorithm for large-scale multiobjective optimization

Contact Info

Product

Resources

About