Evaluation and comparison of Genetic Algorithm and Bees Algorithm for location–allocation of earthquake relief centers

2016 ◽  
Vol 15 ◽  
pp. 94-107 ◽  
Author(s):  
Bahram Saeidian ◽  
Mohammad Saadi Mesgari ◽  
Mostafa Ghodousi
Keyword(s):  
Genetic Algorithm ◽  
Bees Algorithm ◽  
Location Allocation

scholarly journals Distributed Task Allocation in Swarms of Robots

Robotics ◽  
2013 ◽  
pp. 450-473 ◽  
Author(s):  
Aleksandar Jevtić ◽  
Diego Andina ◽  
Mo Jamshidi
Keyword(s):  
Genetic Algorithm ◽  
Task Allocation ◽  
Autonomous Robots ◽  
Average Distance ◽  
Bees Algorithm ◽  
Inherent Feature ◽  
Decentralized Decision Making ◽  
Target Allocation ◽  
Available Information ◽  
Deployment Cost

This chapter introduces a swarm intelligence-inspired approach for target allocation in large teams of autonomous robots. For this purpose, the Distributed Bees Algorithm (DBA) was proposed and developed by the authors. The algorithm allows decentralized decision-making by the robots based on the locally available information, which is an inherent feature of animal swarms in nature. The algorithm’s performance was validated on physical robots. Moreover, a swarm simulator was developed to test the scalability of larger swarms in terms of number of robots and number of targets in the robot arena. Finally, improved target allocation in terms of deployment cost efficiency, measured as the average distance traveled by the robots, was achieved through optimization of the DBA’s control parameters by means of a genetic algorithm.

Distributed Task Allocation in Swarms of Robots

2012 ◽  
pp. 170-193
Author(s):  
Aleksandar Jevtic ◽  
Diego Andina ◽  
Mo Jamshidi
Keyword(s):  
Genetic Algorithm ◽  
Task Allocation ◽  
Autonomous Robots ◽  
Average Distance ◽  
Bees Algorithm ◽  
Inherent Feature ◽  
Decentralized Decision Making ◽  
Target Allocation ◽  
Available Information ◽  
Deployment Cost

This chapter introduces a swarm intelligence-inspired approach for target allocation in large teams of autonomous robots. For this purpose, the Distributed Bees Algorithm (DBA) was proposed and developed by the authors. The algorithm allows decentralized decision-making by the robots based on the locally available information, which is an inherent feature of animal swarms in nature. The algorithm’s performance was validated on physical robots. Moreover, a swarm simulator was developed to test the scalability of larger swarms in terms of number of robots and number of targets in the robot arena. Finally, improved target allocation in terms of deployment cost efficiency, measured as the average distance traveled by the robots, was achieved through optimization of the DBA’s control parameters by means of a genetic algorithm.

scholarly journals Genetic algorithm method for solving the optimal allocation of response resources problem on the example of polish zone of the Baltic Sea / Metoda algorytmów genetycznych do rozwiązywania problemów optymalnej alokacji środków do zwalczania rozlewów olejowych na przykładzie polskiej strefy Morza Bałtyckiego

2016 ◽  
Vol 38 (1) ◽  
pp. 291-310
Author(s):  
Kinga Łazuga ◽  
Lucjan Gucma
Keyword(s):  
Genetic Algorithm ◽  
Optimal Allocation ◽  
Optimization Methods ◽  
Genetic Optimization ◽  
Location Allocation ◽  
Second Stage ◽  
Genetic Algorithm Method ◽  
Logistical Problem ◽  
The Baltic ◽  
Two Stages

Abstract The paper presents research related to optimal allocation of response vessels. Research belong to the logistical problem, location-allocation type (LA). Research is focused on vessels belongs to polish Search and Rescue. For the optimal allocation of resources used two-stages method wherein the first stage, using genetic optimization methods and consist in such allocation of response vessels to minimize costs of the spill at sea. In the second stage uses an accurate simulation model of oil spill combat action to verify the solutions obtained by genetic algorithm method.

Multi-objective storage location allocation optimization and simulation analysis of automated warehouse based on multi-population genetic algorithm

2018 ◽  
Vol 26 (4) ◽  
pp. 367-377 ◽  
Author(s):  
Yu-ling Jiao ◽  
Xiao-cui Xing ◽  
Peng Zhang ◽  
Liang-cheng Xu ◽  
Xin-Ran Liu
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Dynamic Simulation ◽  
Population Genetic ◽  
Physical Parameters ◽  
Location Allocation ◽  
Multi Objective ◽  
Storage Location ◽  
Population Genetic Algorithm ◽  
The Mathematical Model

Aiming at the requirement of working efficiency and security of automated warehouse and taking the operation time of outbound–inbound, the equivalent center of gravity of overall shelf and the degree of relative accumulation of related products as the multi-objective functions, the mathematical model is constructed for multi-objective storage location allocation optimization. According to the simple weighted genetic algorithm, it is easily prone to the problem of immature convergence when solving multi-objective programming problems. So, the multi-population genetic algorithm is proposed to solve the mathematical model of storage location allocation optimization. Combining with the experiment data of toy car assembly and automated warehouse, the results of the automated warehouse storage location allocation are obtained. FlexSim dynamic simulation model is established based on the storage location allocation solution, the physical parameters of automated warehouse and the experimental requirements plan of vehicle model assembly. The operation effect of the model and the utilization rate of the equipment are analyzed. The result of multi-population genetic algorithm is more reasonable and effective. It is proved that the result of multi-population genetic algorithm is superior to the result of simple weighted genetic algorithm, which provides an effective method for storage location allocation optimization and outbound–inbound dynamic simulation.

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