A novel evolutionary algorithm with indirect representation and extended nearest neighbor constructive procedure for solving routing problems

2014 ◽  
Author(s):  
Jiri Kubalik ◽  
Michal Snizek
Keyword(s):  
Evolutionary Algorithm ◽  
Nearest Neighbor ◽  
Routing Problems

scholarly journals A Multi-Objective Evolutionary Algorithm Based on KNN-Graph for Traffic Network Attack

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1589
Author(s):  
Junhui Li ◽  
Shuai Wang ◽  
Hu Zhang ◽  
Aimin Zhou
Keyword(s):  
Evolutionary Algorithm ◽  
Nearest Neighbor ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
K Nearest Neighbor ◽  
New Approach ◽  
Network Attack ◽  
Multi Objective ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph

The research of vulnerability in complex network plays a key role in many real-world applications. However, most of existing work focuses on some static topological indexes of vulnerability and ignores the network functions. This paper addresses the network attack problems by considering both the topological and the functional indexes. Firstly, a network attack problem is converted into a multi-objective optimization network vulnerability problem (MONVP). Secondly to deal with MONVPs, a multi-objective evolutionary algorithm is proposed. In the new approach, a k-nearest-neighbor graph method is used to extract the structure of the Pareto set. With the obtained structure, similar parent solutions are chosen to generate offspring solutions. The statistical experiments on some benchmark problems demonstrate that the new approach shows higher search efficiency than some compared algorithms. Furthermore, the experiments on a subway system also suggests that the multi-objective optimization model can help to achieve better attach plans than the model that only considers a single index.

scholarly journals An Integrated Approach to Goal Selection in Mobile Robot Exploration

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1400 ◽  
Author(s):  
Miroslav Kulich ◽  
Jiří Kubalík ◽  
Libor Přeučil
Keyword(s):  
Mobile Robot ◽  
Evolutionary Algorithm ◽  
Autonomous Navigation ◽  
Nearest Neighbor ◽  
Optimization Problems ◽  
Optimal Path ◽  
Integrated Approach ◽  
Limited Range ◽  
Simulated Environment ◽  
Feasible Solutions

This paper deals with the problem of autonomous navigation of a mobile robot in an unknown2D environment to fully explore the environment as efficiently as possible. We assume a terrestrial mobilerobot equipped with a ranging sensor with a limited range and 360º field of view. The key part of theexploration process is formulated as the d-Watchman Route Problem which consists of two coupledtasks—candidate goals generation and finding an optimal path through a subset of goals—which aresolved in each exploration step. The latter has been defined as a constrained variant of the GeneralizedTraveling Salesman Problem and solved using an evolutionary algorithm. An evolutionary algorithmthat uses an indirect representation and the nearest neighbor based constructive procedure was proposedto solve this problem. Individuals evolved in this evolutionary algorithm do not directly code thesolutions to the problem. Instead, they represent sequences of instructions to construct a feasible solution.The problems with efficiently generating feasible solutions typically arising when applying traditionalevolutionary algorithms to constrained optimization problems are eliminated this way. The proposedexploration framework was evaluated in a simulated environment on three maps and the time needed toexplore the whole environment was compared to state-of-the-art exploration methods. Experimentalresults show that our method outperforms the compared ones in environments with a low density ofobstacles by up to 12.5%, while it is slightly worse in office-like environments by 4.5% at maximum.The framework has also been deployed on a real robot to demonstrate the applicability of the proposedsolution with real hardware.

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