Social networking services have emerged as the main sources for real-time information about events happening. It has been observed that pertinent information gleaned from tweets during catastrophic events can be helpf...
详细信息
ISBN:
(纸本)9781665471015
Social networking services have emerged as the main sources for real-time information about events happening. It has been observed that pertinent information gleaned from tweets during catastrophic events can be helpful in a variety of ways. Therefore, it is necessary to create an automated microblog summarization system. The proposed approach JOWTS, confluence of a wide range of evolutionary computation techniques such as the well-known differential evolutionary algorithm JADE (DE/current-to-pbest/1), Opposition-based Learning (OBL) and whaleoptimization Algorithm (WOA), employs multi-objective optimization for microblog summarization. The summarization task is formulated as a multi-objective optimization problem and combination of objectives such as tweet length & importance of tweets (through tf-idf technique) in a dataset are optimized at the same time. For evaluation, datasets relevant to disaster events are employed and the results are compared to different alternative methodologies utilizing ROUGE measures. When compared against the contemporary evolutionary techniques, it was observed that JOWTS improves ROUGE-1, 2, L scores by 3.86%, 8.53% and 4.69% respectively.
To solve the limitations existing in the standard whaleoptimization algorithm (WOA), a convergence factor based on the sine function is designed to balance the exploration and development abilities of the WOA, furthe...
详细信息
To solve the limitations existing in the standard whaleoptimization algorithm (WOA), a convergence factor based on the sine function is designed to balance the exploration and development abilities of the WOA, furthermore, an improved opposition-based learning strategy is proposed to accelerate the convergence rate of the algorithm, in addition, the mutation operation based on the population diversity is employed to reduce the probability that the algorithm gets caught in the local optimum, the simulation result shows that the novel WOA presents a superior convergence rate and accuracy to the basic WOA.
Electric vehicle is one of the best ways to avoid pollution from the transport sectors. Charging stations are required to charge the battery of the vehicle. To cope up with increased power demand during EV charging, t...
详细信息
ISBN:
(数字)9781728141039
ISBN:
(纸本)9781728141046
Electric vehicle is one of the best ways to avoid pollution from the transport sectors. Charging stations are required to charge the battery of the vehicle. To cope up with increased power demand during EV charging, the power loss of the distribution system increases and voltage drops. To keep the voltage profile healthy and the power loss as minimum as possible, placement of the charging station at the optimal node is necessary. In this article, allocation of electric vehicle charging stations in IEEE 33 node radial distribution network has been done. To provide the charging facility in different location of an area, the distribution network has been divided into three areas. One charging station has been placed in every three different areas. The problem has been set as an optimization problem and solved using Grey Wolf optimization (GWO) and whaleoptimization Algorithm (WOA).
暂无评论