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Elite artificial bees' colony algorithm to solve robot's fuzzy constrained routing problem
Author(s) -
Abbaszadeh Sori Ali,
Ebrahimnejad Ali,
Motameni Homayun
Publication year - 2020
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12258
Subject(s) - robot , computer science , robotics , fuzzy logic , mathematical optimization , artificial intelligence , particle swarm optimization , convergence (economics) , genetic algorithm , algorithm , mathematics , economics , economic growth
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.