A benchmark test problem toolkit for multi-objective path optimization

Hu, Xiao-Bing; Zhang, Hailin; Zhang, Chi; Zhang, Ming-Kong; Li, Hang; Leeson, Mark S.

doi:10.1016/j.swevo.2018.11.009

Cited by 3 publications

(1 citation statement)

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“…In many applications, in addition to finding shortest path, there are other objectives that have to be considered in routing process. So, being forced to deal with problems with more than one objective has led researchers to multiobjective optimization . These objectives differ from one problem to another, for example in “safety” and in “smoothness” are two important criteria that have been considered in addition to length of the path.…”

Section: Introductionmentioning

confidence: 99%

Elite artificial bees' colony algorithm to solve robot's fuzzy constrained routing problem

Sori

Ebrahimnejad

Motameni

2019

Computational Intelligence

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One of the fundamental challenges of the robotics field is robot's movement. That is, why route planning is an eminent issue of robotics research and it is used to enhance autonomy of moving robots in complex environments. The objective of route planning problem is to find the shortest route without collide from initiation point to destination point so that the amount of energy consumption by robot would not exceed a predefined amount. Because neither the amount of energy consumption nor the robot's passed distance index cannot be measured precisely due to environmental conditions, and fuzzy data is used for modeling the problem and the problem would be called “Robot Fuzzy Constrained shortest Route” problem. The main contributions of this study are fivefold: (i) The mathematical model of fuzzy constrained shortest route problem (FCSRP) is formulated; (ii) An elite artificial bees' colony (EABC) algorithm is used to solve the robot's FSCRP; (iii) The proposed EABC algorithm is simulated with two fuzzy networks; (iv) The performance of the proposed approach is compared with the performance of genetic algorithm and particle swarm optimization algorithm; and (v) The results show the convergence speed of the EABC algorithm is higher than the existing algorithms.

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Section: Introductionmentioning

confidence: 99%