An artificial bee colony algorithm with variable neighborhood search and tabu list for long-term carpooling problem with time window

2019 ◽  
Vol 85 ◽  
pp. 105814 ◽  
Author(s):  
Sheng Su ◽  
Fangzheng Zhou ◽  
Haijie Yu
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Variable Neighborhood Search ◽  
Artificial Bee Colony ◽  
Time Window ◽  
Neighborhood Search ◽  
Tabu List ◽  
Bee Colony

scholarly journals Metaheuristic algorithm for solving the multi-objective vehicle routing problem with time window and drones

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142092003
Author(s):  
Yun-qi Han ◽  
Jun-qing Li ◽  
Zhengmin Liu ◽  
Chuang Liu ◽  
Jie Tian
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Time Window ◽  
Total Energy Consumption ◽  
Routing Problem ◽  
Bee Colony

In some special rescue scenarios, the needed goods should be transported by drones because of the landform. Therefore, in this study, we investigate a multi-objective vehicle routing problem with time window and drone transportation constraints. The vehicles are used to transport the goods and drones to customer locations, while the drones are used to transport goods vertically and timely to the customer. Three types of objectives are considered simultaneously, including minimization of the total energy consumption of the trucks, total energy consumption of the drones, and the total number of trucks. An improved artificial bee colony algorithm is designed to solve the problem. In the proposed algorithm, each solution is represented by a two-dimensional vector, and the initialization method based on the Push-Forward Insertion Heuristic is embedded. To enhance the exploitation abilities, an improved employed heuristic is developed to perform detailed local search. Meanwhile, a novel scout bee strategy is presented to improve the global search abilities of the proposed algorithm. Several instances extended from the Solomon instances are used to test the performance of the proposed improved artificial bee colony algorithm. Experimental comparisons with the other efficient algorithms in the literature verify the competitive performance of the proposed algorithm.

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 A Multistrategy Artificial Bee Colony Algorithm Enlightened by Variable Neighborhood Search

2019 ◽  
Vol 2019 ◽  
pp. 1-19
Author(s):  
Wan-li Xiang ◽  
Yin-zhen Li ◽  
Rui-chun He ◽  
Xue-lei Meng ◽  
Mei-qing An
Keyword(s):  
Variable Neighborhood Search ◽  
Artificial Bee Colony ◽  
Search Strategy ◽  
Random Search ◽  
Search Strategies ◽  
Neighborhood Change ◽  
Neighborhood Search ◽  
Bee Colony ◽  
Opposition Based Learning ◽  
Change Of Variable

Artificial bee colony (ABC) has a good exploration ability against its exploitation ability. For enhancing its comprehensive performance, we proposed a multistrategy artificial bee colony (ABCVNS for short) based on the variable neighborhood search method. First, a search strategy candidate pool composed of two search strategies, i.e., ABC/best/1 and ABC/rand/1, is proposed and employed in the employed bee phase and onlooker bee phase. Second, we present another search strategy candidate pool which consists of the original random search strategy and the opposition-based learning method. Then, it is used to further balance the exploration and exploitation abilities in the scout bee phase. Last but not least, motivated by the scheme of neighborhood change of variable neighborhood search, a simple yet efficient choice mechanism of search strategies is presented. Subsequently, the effectiveness of ABCVNS is carried out on two test suites composed of fifty-eight problems. Furthermore, comparisons among ABCVNS and several famous methods are also carried out. The related experimental results clearly demonstrate the effectiveness and the superiority of ABCVNS.

scholarly journals Artificial Bee Colony Algorithm for Fresh Food Distribution without Quality Loss by Delivery Route Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Sapna Katiyar ◽  
Rijwan Khan ◽  
Santosh Kumar
Keyword(s):  
Artificial Intelligence ◽  
Food Quality ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Time Window ◽  
Food Distribution ◽  
Quality Loss ◽  
Bee Colony ◽  
Fresh Food ◽  
Nature Inspired Algorithm

This paper enlightens the use of artificial intelligence (AI) for distribution of fresh foods by searching more viable route to keep intact the food attributes. In recent years, very hard-hitting competition is for food industries because of the individuals living standards and their responsiveness for fresh food products demand within stipulated time period. Food industry deals with the extensive kind of activities such as food processing, food packaging and distribution, and instrumentation and control. To meet market demand, customer satisfaction, and maintaining its own brand and ranking on global scale, artificial intelligence can play a vibrant role in decision-making by providing analytical solutions with adjusting available resources. Therefore, by integrating innovative technologies for fresh food distribution, potential benefits have been increased, and simultaneously risk associated with the food quality is reduced. Time is a major factor upon which food quality depends; hence, time required to complete the task must be minimized, and it is achieved by reducing the distance travelled; so, path optimization is the key for the overall task. Swarm intelligence (SI) is a subfield of artificial intelligence and consists of many algorithms. SI is a branch of nature-inspired algorithm, having a capability of global search, and gives optimized solution for real-time problems adaptive in nature. An artificial bee colony (ABC) optimization and cuckoo search (CS) algorithm also come into the category of SI algorithm. Researchers have implemented ABC algorithm and CS algorithm to optimize the distribution route for fresh food delivery in time window along with considering other factors: fixed number of delivery vehicles and fixed cost and fuel by covering all service locations. Results show that this research provides an efficient approach, i.e., artificial bee colony algorithm for fresh food distribution in time window without penalty and food quality loss.

scholarly journals Multiobjective Cognitive Cooperative Jamming Decision-Making Method Based on Tabu Search-Artificial Bee Colony Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Fang Ye ◽  
Fei Che ◽  
Lipeng Gao
Keyword(s):  
Tabu Search ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Local Optimum ◽  
Tabu List ◽  
Cooperative Jamming ◽  
Bee Colony ◽  
Local Optimal Solution ◽  
The One

For the future information confrontation, a single jamming mode is not effective due to the complex electromagnetic environment. Selecting the appropriate jamming decision to coordinately allocate the jamming resources is the development direction of the electronic countermeasures. Most of the existing studies about jamming decision only pay attention to the jamming benefits, while ignoring the jamming cost. In addition, the conventional artificial bee colony algorithm takes too many iterations, and the improved ant colony (IAC) algorithm is easy to fall into the local optimal solution. Against the issue, this paper introduces the concept of jamming cost in the cognitive collaborative jamming decision model and refines it as a multiobjective one. Furthermore, this paper proposes a tabu search-artificial bee colony (TSABC) algorithm to cognitive cooperative-jamming decision. It introduces the tabu list into the artificial bee colony (ABC) algorithm and stores the solution that has not been updated after a certain number of searches into the tabu list to avoid meeting them when generating a new solution, so that this algorithm reduces the unnecessary iterative process, and it is not easy to fall into a local optimum. Simulation results show that the search ability and probability of finding the optimal solution of the new algorithm are better than the other two. It has better robustness, which is better in the “one-to-many” jamming mode.

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.

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