Path Planning of Mobile Robot Using Improved Artificial Bee Colony Algorithm

2020 ◽  
Vol 38 (9A) ◽  
pp. 1384-1395
Author(s):  
Rakaa T. Kamil ◽  
Mohamed J. Mohamed ◽  
Bashra K. Oleiwi
Keyword(s):  
Mobile Robot ◽  
Comparative Research ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Dynamic Control ◽  
Computational Time ◽  
Bee Colony ◽  
Shortest Distance ◽  
Minimum Number ◽  
Cubic Polynomial

A modified version of the artificial Bee Colony Algorithm (ABC) was suggested namely Adaptive Dimension Limit- Artificial Bee Colony Algorithm (ADL-ABC). To determine the optimum global path for mobile robot that satisfies the chosen criteria for shortest distance and collision–free with circular shaped static obstacles on robot environment. The cubic polynomial connects the start point to the end point through three via points used, so the generated paths are smooth and achievable by the robot. Two case studies (or scenarios) are presented in this task and comparative research (or study) is adopted between two algorithm’s results in order to evaluate the performance of the suggested algorithm. The results of the simulation showed that modified parameter (dynamic control limit) is avoiding static number of limit which excludes unnecessary Iteration, so it can find solution with minimum number of iterations and less computational time. From tables of result if there is an equal distance along the path such as in case A (14.490, 14.459) unit, there will be a reduction in time approximately to halve at percentage 5%.

scholarly journals A Modification Artificial Bee Colony Algorithm for Optimization Problems

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Jun-Hao Liang ◽  
Ching-Hung Lee
Keyword(s):  
Mobile Robot ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Computational Effort ◽  
Function Optimization ◽  
Cooperative Strategy ◽  
Robot System ◽  
Bee Colony ◽  
And Performance

This paper presents a modified artificial bee colony algorithm (MABC) for solving function optimization problems and control of mobile robot system. Several strategies are adopted to enhance the performance and reduce the computational effort of traditional artificial bee colony algorithm, such as elite, solution sharing, instant update, cooperative strategy, and population manager. The elite individuals are selected as onlooker bees for preserving good evolution, and, then, onlooker bees, employed bees, and scout bees are operated. The solution sharing strategy provides a proper direction for searching, and the instant update strategy provides the newest information for other individuals; the cooperative strategy improves the performance for high-dimensional problems. In addition, the population manager is proposed to adjust population size adaptively according to the evolution situation. Finally, simulation results for optimization of test functions and tracking control of mobile robot system are introduced to show the effectiveness and performance of the proposed approach.

scholarly journals Evacuation Planning Optimization Based on a Multi-Objective Artificial Bee Colony Algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 110 ◽  
Author(s):  
Olive Niyomubyeyi ◽  
Petter Pilesjö ◽  
Ali Mansourian
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Spatial Optimization ◽  
Evacuation Planning ◽  
Computational Time ◽  
Neighborhood Search ◽  
Multi Objective ◽  
Bee Colony ◽  
Fitness Value ◽  
Conflicting Objectives

Evacuation is an important activity for reducing the number of casualties and amount of damage in disaster management. Evacuation planning is tackled as a spatial optimization problem. The decision-making process for evacuation involves high uncertainty, conflicting objectives, and spatial constraints. This study presents a Multi-Objective Artificial Bee Colony (MOABC) algorithm, modified to provide a better solution to the evacuation problem. The new approach combines random swap and random insertion methods for neighborhood search, the two-point crossover operator, and the Pareto-based method. For evacuation planning, two objective functions were considered to minimize the total traveling distance from an affected area to shelters and to minimize the overload capacity of shelters. The developed model was tested on real data from the city of Kigali, Rwanda. From computational results, the proposed model obtained a minimum fitness value of 5.80 for capacity function and 8.72 × 108 for distance function, within 161 s of execution time. Additionally, in this research we compare the proposed algorithm with Non-Dominated Sorting Genetic Algorithm II and the existing Multi-Objective Artificial Bee Colony algorithm. The experimental results show that the proposed MOABC outperforms the current methods both in terms of computational time and better solutions with minimum fitness values. Therefore, developing MOABC is recommended for applications such as evacuation planning, where a fast-running and efficient model is needed.

Parallel Artificial Bee Colony Algorithm for the Traveling Salesman Problem

2013 ◽  
Vol 756-759 ◽  
pp. 3254-3259 ◽  
Author(s):  
Kun Xu ◽  
Ming Yan Jiang ◽  
Dong Feng Yuan
Keyword(s):  
Combinatorial Optimization ◽  
Traveling Salesman Problem ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Fitness Function ◽  
Traveling Salesman ◽  
Computational Time ◽  
Bee Colony ◽  
Processor Node

Artificial Bee Colony Algorithm (ABCA) is a novel swarm intelligence algorithm which a colony of artificial bees cooperate in finding good solutions for numerical optimization problems and combinatorial optimization problems. Traveling Salesman Problem (TSP) is a famous combinatorial optimization problem which has been used in many fields such as network communication, transportation, manufacturing and logistics. However, it requires a considerably large amount of computational time and resources for solving TSP. To dealing with this problem, we present a Parallel Artificial Bee Colony Algorithm (PABCA) in several computers which operation system is Linux based on the Message Passing Interface (MPI). The entire artificial bee colony is divided into several subgroups by PABCA equally. Each subgroup performs an ABCA for TSP on each processor node, respectively. Each sub-colony on every processor node communicates the current best fitness function and parameters of current best fitness function according to ring topological structure during calculation process. Some well-known benchmark problems in TSP are used to evaluate the performance of ABCA and PABCA. Meanwhile, the performance of PABCA is compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Experimental results show that the PABCA can obtain solutions with equal precision and reduce the time of computation obviously in comparison with serial ABCA. And PABCA have much better performance in contrast with GA and PSO.

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