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An improved problem transformation algorithm for large-scale multi-objective optimization
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AI
DOI：10.1016/j.swevo.2024.101622.png)
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Abstract
En 中文
Solving Large -Scale Multi -Objective Optimization Problems (LSMOPs) is the major challenge in evolution computation. Due to the large number of decision variables involved, it is not easy for existing multi -objective evolutionary algorithms to search the entire decision variable space with limited computation resources. To address the above problem, a large-scale multi -objective evolutionary algorithm based on problem transformation, called LSMOEA/PT, is presented in this paper. LSMOEA/PT uses an improved problem transformation strategy to transform LSMOPs into small-scale multi -objective problems to accelerate convergence speed and reduce computational resources. Specifically, The transformation strategy selects reference solutions to initialize a weight population by applying the opposite individual method. After optimizing the weight population, a dual line strategy is implemented to combine the weight vectors with the decision variables from the original population, thereby generating a new population. The new population is combined with the original population for environment selection. In LSMOEA/PT, a double learning swarm optimizer is introduced to speed up the convergence of the population. A uniform distribution strategy is conducted to update transformed solutions after the problem transformation process, increasing the diversity of the population. Experimental results show that LSMOEA/PT is competitive with eleven state-of-the-art algorithms on large-scale multi -objective optimization test problems with up to 2000 decision variables.
Keywords:
Evolutionary multi-objective optimization
Large-Scale Multi-Objective Problems
Problem transformation
Double learning swarm optimizer
Uniform distribution strategy
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