A Fast Clustering Based Evolutionary Algorithm for Super-Large-Scale Sparse Multi-Objective Optimization

Tian, Ye; Feng, Yuandong; Zhang, Xingyi; Sun, Chuan

doi:10.1109/jas.2022.105437

Cited by 36 publications

(5 citation statements)

References 54 publications

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“…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; Tian et al, 2023 Both crossover and mutation have a distribution index of 20.…”

Section: Slmeamentioning

confidence: 99%

“…PM-MOEA (Tian et al, 2022 ) adopts pattern mining techniques to identify the maximal and minimal candidate sets of non-zero decision variables from the population and apply specialized genetic operators to these patterns to achieve dimensional reduction. SMEA (Tian et al, 2023 ) proposes an effective approach for addressing sparse large-scale multi-objective evolutionary problems. The algorithm optimizes the binary vectors of each solution to estimate the sparse distribution of optimal solutions and introduces a rapid clustering method for significantly reducing the dimensionality of the search space.…”

Section: Backgroundsmentioning

confidence: 99%

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

Liu,

Wu,

et al. 2024

Front. Hum. Neurosci.

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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.

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Section: Resultsmentioning

confidence: 99%

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

Section: Slmeamentioning

confidence: 99%

Section: Backgroundsmentioning

confidence: 99%

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

Liu,

Wu,

et al. 2024

Front. Hum. Neurosci.

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“…• Some other operators can be embedded to extend this framework to solve other kinds of MOPs. For example, the CSO [13] or other enhanced operators [52] can be embedded to solve large-scale CMOPs [53]. In addition, some advanced learning-based optimizers such as switching particle swarm optimizers [54], [55] can be used as actions to enhance performance.…”

Section: Discussionmentioning

confidence: 99%

Constrained Multiobjective Optimization via Multitasking and Knowledge Transfer

Ming

Gong

Gao

2024

IEEE Trans. Evol. Computat.

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Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention. Various constrained multi-objective optimization evolutionary algorithms (CMOEAs) have been developed with the use of different algorithmic strategies, evolutionary operators, and constraint-handling techniques. The performance of CMOEAs may be heavily dependent on the operators used, however, it is usually difficult to select suitable operators for the problem at hand. Hence, improving operator selection is promising and necessary for CMOEAs. This work proposes an online operator selection framework assisted by Deep Reinforcement Learning. The dynamics of the population, including convergence, diversity, and feasibility, are regarded as the state; the candidate operators are considered as actions; and the improvement of the population state is treated as the reward. By using a Q-Network to learn a policy to estimate the Q-values of all actions, the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance. The framework is embedded into four popular CMOEAs and assessed on 42 benchmark problems. The experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.

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“…In this section, we compared the performance of the DSAG algorithm with eight MOEAs on the DTLZ and MaF benchmark functions, including NSGA-III (Deb and Jain 2013), MOEA/D (Zhang and Li 2007), RVEA (Cheng, Jin et al 2016), MOEA/D-D (Li, Deb et al 2014), NSGA-II-SDR (Tian, Cheng et al 2018), 2REA-VCEM (Liang, Zeng et al 2021), DGEA (He, Cheng et al 2020), SLMEA (Tian, Feng et al 2022). Furthermore, we also recorded the IGD results of each algorithm in 30 independent experiments for different numbers of objective functions in each benchmark function.…”

Section: Compared With Excellent Traditional Moeasmentioning

confidence: 99%

A Many-Objective Optimization Evolutionary Algorithm Based on Double Surrogate-Assisted Adaptive Guiding Evolutionary Direction

Dali,

Yifei,

et al. 2023

Preprint

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In expensive Many-objective Optimization Problems, Surrogate-Assisted Evolutionary Algorithms (SAEAs) are often used to reduce the number of original Function Evaluations (FE). However, traditional SAEAs may suffer from long running times due to the complex computational demands of the surrogate models themselves. In this paper, we propose a novel algorithm called " A Many-Objective Optimization Evolutionary Algorithm Based on Double Surrogate-Assisted Adaptive Guiding Evolutionary Direction (DSAG)", aimed at further reducing the running time of SAEAs. The proposed algorithm employs two surrogate models: one for predicting the diversity of solutions and another for predicting the convergence value of solutions, and then sorts the solutions based on the prediction results. Afterwards, the algorithm adaptively adjusts the type and number of candidate solutions according to the "Convergence-Related Average Move Distance (CAMD)" proposed in this paper. This algorithm can adaptively bias towards either the convergence exploration stage or the diversity exploration stage. Compared with other classical surrogate-assisted evolutionary algorithms, the proposed algorithm first categorizes the decision variables and uses the categorized data to train two surrogate models, which makes the overall complexity and running time of the algorithm superior to others. Finally, we compared the performance of this algorithm on different problems using benchmark test functions to verify its ability to explore diversity and improve convergence. Then, we compared the running time of the algorithm with changes in the target dimension, decision variable dimension, and number of Original FE on the same test problems, and demonstrated the superior performance of the algorithm.

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A Fast Clustering Based Evolutionary Algorithm for Super-Large-Scale Sparse Multi-Objective Optimization

Cited by 36 publications

References 54 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

Constrained Multiobjective Optimization via Multitasking and Knowledge Transfer

A Many-Objective Optimization Evolutionary Algorithm Based on Double Surrogate-Assisted Adaptive Guiding Evolutionary Direction

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