An Elite Group Guided Artificial Bee Colony Algorithm with a Modified Neighborhood Search

2018 ◽  
pp. 387-394
Author(s):  
Jiaxin Lu ◽  
Xinyu Zhou ◽  
Yong Ma ◽  
Mingwen Wang
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Neighborhood Search ◽  
Elite Group ◽  
Bee Colony

Enhancing the modified artificial bee colony algorithm with neighborhood search

2015 ◽  
Vol 21 (10) ◽  
pp. 2733-2743 ◽  
Author(s):  
Xinyu Zhou ◽  
Hui Wang ◽  
Mingwen Wang ◽  
Jianyi Wan
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Neighborhood Search ◽  
Bee Colony

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.

Artificial bee colony algorithm based on adaptive neighborhood search and Gaussian perturbation

2021 ◽  
Vol 100 ◽  
pp. 106955
Author(s):  
Songyi Xiao ◽  
Hui Wang ◽  
Wenjun Wang ◽  
Zhikai Huang ◽  
Xinyu Zhou ◽  
...  
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Neighborhood Search ◽  
Bee Colony ◽  
Gaussian Perturbation ◽  
Adaptive Neighborhood
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