Abstract
The critical node detection problem (CNDP) refers to the identification of one or more nodes that have a significant impact on the entire complex network according to the importance of each node in a complex network. Most methods consider the CNDP as a single-objective optimization problem, which requires more prior knowledge to a certain extent. This paper proposes a membrane evolution algorithm MEA-CNDP to solve biobjective CNDP. MEA-CNDP includes a population initialization strategy based on the evaluation of decision variables, a strategy to transform the main objective, a strategy to update the membrane inherited pool, and four membrane evolutionary operators. The numerical experiments on 16 benchmark problems with random and logarithmic weights show that MEA-CNDP outperforms other algorithms in most cases. In particular, MEA-CNDP has unique advantages in dealing with large-scale sparse bi-CNDP.
Highlights
A crucial research direction in engineering design is to consider optimization problems
Considering I-multiobjective evolutionary algorithm based on decomposition (MOEA/D), I-MOEA/D-εC and their variants, only the best results among their various variants are considered in the following experiments, that is, in the mating pool selection strategy, the “-NP-EP” method is adopted, and the “-G” method is adopted in the replacement pool strategy
As a classical problem in combinatorial optimization, critical node detection problem (CNDP) is often studied as a single-objective optimization problem
Summary
A crucial research direction in engineering design is to consider optimization problems. Population-based evolutionary algorithm has shown gratifying results in solving optimization problems, and a new estimation of distribution algorithm (EDA) is introduced in [19]. It maintains a trade-off between run time and the number of evaluation points. Cardinalityconstrained critical node detection problem (CC-CNP) is devoted to minimizing the number of deleted nodes given the maximum connected component [22]. Is paper proposes a novel evolutionary algorithm based on MA for solving the biobjective critical node detection problem (bi-CNDP) model in [31], called MEACNDP for short. Aiming at bi-CNDP, a new population initialization strategy is proposed in MEACNDP, and the main objective transforming and membrane-to-subproblem matching strategy are adopted to improve the efficiency of MEA-CNDP. e main contributions of this paper include the following:
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