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Information-assisted solution generation based dual population constrained multi-objective evolutionary algorithm
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AI
DOI：10.1007/s10586-026-06492-0.png)
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Abstract
En 中文
In solving constrained multi-objective optimization problems (CMOPs), not only is the optimization of the population in the objective space important, but the solutions in the decision space also play a crucial role. However, current constrained multi-objective evolutionary algorithms (CMOEAs) often neglect the impact of the solution generation process on algorithm performance. Therefore, this paper proposes an information-assisted solution generation based dual population constrained multi-objective evolutionary algorithm, called GSCMO. The information includes entropy information reflecting the population distribution and neighborhood information reflecting the proximity relationships between solutions. In GSCMO, we design a global solution generation strategy for the main population based on entropy information. This strategy dynamically optimizes the mutation strength, focusing on enhancing solution diversity and maintaining extensive exploration of the population in the decision space, thereby improving the algorithm’s ability to explore potential feasible solution regions. At the same time, we develop a local solution generation strategy for the auxiliary population based on neighborhood information. This strategy uses Manhattan distance to determine the neighborhood of each individual, selects individuals from the neighborhood as parents, and generates high-quality individuals, thus enhancing the algorithm’s local search capability. Extensive experimental results demonstrate that GSCMO has high competitiveness compared with some state-of-the-art methods.
Keywords:
Decision space
Dual population
Entropy information
Neighborhood information
Constrained multi-objective optimization
Journal
C
IF:
4.1
Papers:
5.0K
Citations:
7.5K
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