In the real world, there exists a special category of multi-objective optimization problems with more than 1000 decision variables. However, only a few decision variables play a crucial role in optimizing the objectiv...
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In the real world, there exists a special category of multi-objective optimization problems with more than 1000 decision variables. However, only a few decision variables play a crucial role in optimizing the objective functions. Such problems are defined as sparselarge-scale multi-objective optimization problems (SLSMOPs). Due to the difficulty in effectively identifying the non-zero positions of decision variables, traditional evolutionary optimization algorithms suffer from slow convergence speed and poor convergence effect, which means it is unable to efficiently obtain the Pareto optimal solution set. To address this challenge, the Impact Factor Assisted Algorithm (IFA) is proposed, which adopts a novel initial population strategy to generate sparse populations. Meanwhile, the impact factor of each decision variable is calculated, serving as a key basis for measuring the importance of each decision variable. During the algorithm's operation, the impact factors are iteratively updated to rationally group decision variables and guide population evolution. This approach can accurately identify the positions of non-zero decision variables. The experimental results on eight benchmark and real-world problems indicate that the algorithm outperforms several existing sparselarge-scale multi-objective optimization algorithms (SLSMOEAs).
Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparselarge-scale multi-objective optimization problems(SLM-OPs)where most decision variables are *** a...
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Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparselarge-scale multi-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-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.
sparse multiobjective optimization problems are common in practical applications. Such problems are characterized by large-scale decision variables and sparse optimal solutions. General large-scale multiobjective opti...
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sparse multiobjective optimization problems are common in practical applications. Such problems are characterized by large-scale decision variables and sparse optimal solutions. General large-scale multiobjective optimization problems (LSMOPs) have been extensively studied for many years. They can be well solved by many excellent custom algorithms. However, when these algorithms are used to deal with sparse LSMOPs, they often encounter difficulties because the sparse nature of the problem is not considered. Therefore, aiming at sparse LSMOPs, an algorithm based on multiple sparse detection is proposed in this paper. The algorithm applies an adaptive sparse genetic operator that can generate sparse solutions by detecting the sparsity of individuals. To improve the deficiency of sparse detection caused by local detection, an enhanced sparse detection (ESD) strategy is proposed in this paper. The strategy uses binary coefficient vectors to integrate the masks of nondominated solutions. Essentially, the mask is globally and deeply optimized by coefficient vectors to enhance the sparsity of the solutions. In addition, the algorithm adopts an improved weighted optimization strategy to fully optimize the key nonzero variables to balance exploration and optimization. Finally, the proposed algorithm is named MOEA-ESD and is compared to the current state-of-the-art algorithm to verify its effectiveness.
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