An analysis of the migration rates for biogeography-based optimization

Guo, Weian; Wang, Lei; Wu, Qidi

doi:10.1016/j.ins.2013.07.018

Cited by 53 publications

(21 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, BBO will be employed to optimize the optimization in PHEV's charging scheme. For BBO, it has a more flexible way to recombine candidate solutions since for each recombination all candidates are involved, while for GA only one pair of candidates are in one recombination [43,44]. In addition, BBO is easily implemented since it has few parameters in programming, compared with several other heuristic algorithms such as ant colony optimization (ACO) [41] that contains around five parameters.…”

Section: Preliminary Of Heuristic Algorithmmentioning

confidence: 99%

A Personalized Rolling Optimal Charging Schedule for Plug-In Hybrid Electric Vehicle Based on Statistical Energy Demand Analysis and Heuristic Algorithm

et al. 2017

Self Cite

View full text Add to dashboard Cite

Abstract:To alleviate the emission of greenhouse gas and the dependence on fossil fuel, Plug-in Hybrid Electrical Vehicles (PHEVs) have gained an increasing popularity in current decades. Due to the fluctuating electricity prices in the power market, a charging schedule is very influential to driving cost. Although the next-day electricity prices can be obtained in a day-ahead power market, a driving plan is not easily made in advance. Although PHEV owners can input a next-day plan into a charging system, e.g., aggregators, day-ahead, it is a very trivial task to do everyday. Moreover, the driving plan may not be very accurate. To address this problem, in this paper, we analyze energy demands according to a PHEV owner's historical driving records and build a personalized statistic driving model. Based on the model and the electricity spot prices, a rolling optimization strategy is proposed to help make a charging decision in the current time slot. On one hand, by employing a heuristic algorithm, the schedule is made according to the situations in the following time slots. On the other hand, however, after the current time slot, the schedule will be remade according to the next tens of time slots. Hence, the schedule is made by a dynamic rolling optimization, but it only decides the charging decision in the current time slot. In this way, the fluctuation of electricity prices and driving routine are both involved in the scheduling. Moreover, it is not necessary for PHEV owners to input a day-ahead driving plan. By the optimization simulation, the results demonstrate that the proposed method is feasible to help owners save charging costs and also meet requirements for driving.

show abstract

Section: Preliminary Of Heuristic Algorithmmentioning

confidence: 99%

A Personalized Rolling Optimal Charging Schedule for Plug-In Hybrid Electric Vehicle Based on Statistical Energy Demand Analysis and Heuristic Algorithm

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…The emigration rate μ , for "model 7" and "model 8" are presented by equations (8) and (9) respectively [7] …”

Section: The Bbo Antenna Selection Algorithmmentioning

confidence: 99%

“…Ma in [6] showed that sinusoidal migration models generally outperform linear migration models like the one in original BBO algorithm. The authors in [7] extend the migration model performance analysis and they propose two nonlinear migration models, which they call model 7 and model 8.…”

Section: Introductionmentioning

confidence: 99%

Antenna selection for MIMO systems using biogeography based optimization

Fountoukidis

Siakavara

Goudos

et al. 2017

2017 International Workshop on Antenna Technology: Small Antennas, Innovative Structures, and Applications (iWAT)

View full text Add to dashboard Cite

“…Habib et al [21] develop BBO with migration and mutation operators for job scheduling problem. Guo et al [9] mathematically discuss the impact of migration rates on BBO, which is helpful to design migration models. They investigate the transition probability matrix of BBO.…”

Section: Related Workmentioning

confidence: 99%

“…4, which are LA 1 , LA 2 , and LA 3 for a habitat. The group of LA 1 has the cells 0, 1,4,5,8,9,12. The group of LA 2 has the cells 2, 3, 6, and 7.…”

Section: Group Mutation Of Habitatsmentioning

confidence: 99%

A grouping biogeography-based optimization for location area planning

Kim

Byeon²,

Lee

et al. 2015

Neural Comput & Applic

View full text Add to dashboard Cite

Location area planning (LAP) is a combinatorial optimization grouping problem for the cellular mobile network. We propose a novel grouping biogeographybased optimization (GBBO), which has suitable migration and mutation with generating good initial habitats to partition the optimal number of location areas. The migration is to move the whole cells of location area (LA) with a randomly selected cell between habitats for emigration and immigration, while the adjacent cell mutation is carried out between LAs within one habitat. These group migration and mutation mechanisms are available to maintain the grouping conditions. This proposed GBBO helps us to obtain the optimal number of location areas and the corresponding configuration of the partitioned network. We also illustrate the GBBO approach using the small, medium, and large size problems to compare with artificial bee colony, particle swarm optimization, and previous LAP methods. The experimental results show that our novel GBBO is robust to find the best configurations of LAP with much less computation time comparing with other considered methods.

show abstract

An analysis of the migration rates for biogeography-based optimization

Cited by 53 publications

References 40 publications

A Personalized Rolling Optimal Charging Schedule for Plug-In Hybrid Electric Vehicle Based on Statistical Energy Demand Analysis and Heuristic Algorithm

A Personalized Rolling Optimal Charging Schedule for Plug-In Hybrid Electric Vehicle Based on Statistical Energy Demand Analysis and Heuristic Algorithm

Antenna selection for MIMO systems using biogeography based optimization

A grouping biogeography-based optimization for location area planning

Contact Info

Product

Resources

About