Traditional large-scalemulti-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparselarge-scalemulti-objective optimization problems(SLM-OPs)where most decision variables are *** a...
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Traditional large-scalemulti-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparselarge-scalemulti-objective optimization problems(SLM-OPs)where most decision variables are *** a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec ***,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables ***,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is *** data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between *** by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these *** mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast ***-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparselarge-scalemulti-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.
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