scholarly journals MEA-CNDP: A Membrane Evolutionary Algorithm for Solving Biobjective Critical Node Detection Problem

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yaochang Xu ◽  
Ping Guo
Keyword(s):  
Complex Network ◽  
Large Scale ◽  
Benchmark Problems ◽  
Detection Problem ◽  
Critical Node ◽  
Decision Variables ◽  
Entire Complex ◽  
Evolution Algorithm ◽  
Evolutionary Operators ◽  
Critical Node Detection

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.

A Fuzzy Simulated Evolution Algorithm for Hard Problems

2014 ◽  
pp. 892-912
Author(s):  
Michael Mutingi
Keyword(s):  
Set Theory ◽  
Large Scale ◽  
Cell Formation ◽  
Computational Experiments ◽  
Benchmark Problems ◽  
Problem Complexity ◽  
Simulated Evolution ◽  
Solution Methods ◽  
Hard Problems ◽  
Evolution Algorithm

As problem complexity continues to increase in industry, developing efficient solution methods for solving hard problems, such as heterogeneous vehicle routing and integrated cell formation problems, is imperative. The focus of this chapter is to develop from the classical simulated evolution algorithm, a Fuzzy Simulated Evolution Algorithm (FSEA) that incorporates the concepts of fuzzy set theory, evolution, and constructive perturbation. The aim is to improve the search efficiency of the algorithm by enhancing the major phases of the algorithm through initialization, evaluation, selection, and reconstruction. Illustrative examples are provided to demonstrate the candidate application areas and to show the strength of the algorithm. Computational experiments are conducted based on benchmark problems in the literature. Results from the computational experiments demonstrate the strength of the algorithm. It is anticipated that the application of the FSEA metaheuristic can be extended to other hard large scale problems.

COMPUTING TWO LINCHPINS OF TOPOLOGICAL DEGREE BY A NOVEL DIFFERENTIAL EVOLUTION ALGORITHM

2005 ◽  
Vol 05 (03) ◽  
pp. 335-350 ◽  
Author(s):  
FEI GAO ◽  
HENG-QING TONG
Keyword(s):  
Differential Evolution ◽  
Topological Degree ◽  
Large Scale ◽  
Uniform Design ◽  
Lipschitz Constant ◽  
Differential Evolution Algorithm ◽  
Initial Point ◽  
Benchmark Problems ◽  
Initial Population ◽  
Evolution Algorithm

How to detect the topological degree (TD) of a function is of vital importance in investigating the existence and the number of zero values in the function, which is a topic of major significance in the theory of nonlinear scientific fields. Usually a sufficient refinement of the boundary of the polyhedron decided by Boult and Sikorski algorithm (BS) is needed as prerequisite when the well known method of Stenger and Kearfott is chosen for computing TD. However two linchpins are indispensable to BS, the parameter δ on the boundary of the polyhedron and an estimation of the Lipschitz constant K of the function, whose computations are analytically difficult. In this paper, through an appropriate scheme that transforms the problems of computing δ and K into searching optimums of two non-differentiable functions, a novel differential evolution algorithm (DE) combined with established techniques is proposed as an alternative method to computing δ and K. Firstly it uses uniform design method to generate the initial population in feasible field so as to have the property of large scale convergence, without better approximation of the unknown parameter as iterative initial point. Secondly, it restrains the normal DE's local convergence limitation virtually through deflection and stretching of objective function. The main advantages of the put algorithm are its simplicity and its ability to work by using function values solely. Finally, details of applying the proposed method into computing δ and K are given, and experimental results on two benchmark problems in contrast to the results reported have demonstrated the promising performance of the proposed algorithm in different scenarios.

The bi-objective critical node detection problem with minimum pairwise connectivity and cost: theory and algorithms

2019 ◽  
Vol 23 (23) ◽  
pp. 12729-12744 ◽  
Author(s):  
Juan Li ◽  
Panos M. Pardalos ◽  
Bin Xin ◽  
Jie Chen
Keyword(s):  
Detection Problem ◽  
Critical Node ◽  
Critical Node Detection

Efficient Benders decomposition for distance-based critical node detection problem

Omega ◽  
2020 ◽  
Vol 93 ◽  
pp. 102037 ◽  
Author(s):  
F. Hooshmand ◽  
F. Mirarabrazi ◽  
S.A. MirHassani
Keyword(s):  
Benders Decomposition ◽  
Detection Problem ◽  
Critical Node ◽  
Critical Node Detection

A Simple Genetic Algorithm for the Critical Node Detection Problem

2021 ◽  
pp. 124-133
Author(s):  
Mihai-Alexandru Suciu ◽  
Noémi Gaskó ◽  
Tamás Képes ◽  
Rodica Ioana Lung
Keyword(s):  
Genetic Algorithm ◽  
Detection Problem ◽  
Critical Node ◽  
Simple Genetic Algorithm ◽  
Critical Node Detection

Evolutionary Large-Scale Multi-Objective Optimization: A Survey

2022 ◽  
Vol 54 (8) ◽  
pp. 1-34
Author(s):  
Ye Tian ◽  
Langchun Si ◽  
Xingyi Zhang ◽  
Ran Cheng ◽  
Cheng He ◽  
...  
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Benchmark Problems ◽  
Future Research ◽  
Decision Variable ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Space Reduction ◽  
Decision Variables ◽  
Comprehensive Survey

Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in solving various optimization problems, but their performance may deteriorate drastically when tackling problems containing a large number of decision variables. In recent years, much effort been devoted to addressing the challenges brought by large-scale multi-objective optimization problems. This article presents a comprehensive survey of stat-of-the-art MOEAs for solving large-scale multi-objective optimization problems. We start with a categorization of these MOEAs into decision variable grouping based, decision space reduction based, and novel search strategy based MOEAs, discussing their strengths and weaknesses. Then, we review the benchmark problems for performance assessment and a few important and emerging applications of MOEAs for large-scale multi-objective optimization. Last, we discuss some remaining challenges and future research directions of evolutionary large-scale multi-objective optimization.

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