Bat Algorithm: Literature Review and Applications

Bat algorithm (BA) is a bio-inspired algorithm developed by Yang in 2010 and BA has been found to be very efficient. As a result, the literature has expanded significantly in the last 3 years. This paper provides a timely review of the bat algorithm and its new variants. A wide range of diverse applications and case studies are also reviewed and summarized briefly here. Further research topics are also discussed.Comment: 10 page

A Contextual-Bandit Approach to Personalized News Article Recommendation

Personalized web services strive to adapt their services (advertisements, news articles, etc) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamically changing pools of content, rendering traditional collaborative filtering methods inapplicable. Second, the scale of most web services of practical interest calls for solutions that are both fast in learning and computation. In this work, we model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks. The contributions of this work are three-fold. First, we propose a new, general contextual bandit algorithm that is computationally efficient and well motivated from learning theory. Second, we argue that any bandit algorithm can be reliably evaluated offline using previously recorded random traffic. Finally, using this offline evaluation method, we successfully applied our new algorithm to a Yahoo! Front Page Today Module dataset containing over 33 million events. Results showed a 12.5% click lift compared to a standard context-free bandit algorithm, and the advantage becomes even greater when data gets more scarce.Comment: 10 pages, 5 figure

Do long-duration GRBs follow star formation?

We compare the luminosity function and rate inferred from the BATSE long bursts peak flux distribution with those inferred from the Swift peak flux distribution. We find that both the BATSE and the Swift peak fluxes can be fitted by the same luminosity function and the two samples are compatible with a population that follows the star formation rate. The estimated local long GRB rate (without beaming corrections) varies by a factor of five from 0.05 Gpc^(-3)yr^(-1) for a rate function that has a large fraction of high redshift bursts to 0.27 Gpc^(-3)yr^(-1) for a rate function that has many local ones. We then turn to compare the BeppoSax/HETE2 and the Swift observed redshift distributions and compare them with the predictions of the luminosity function found. We find that the discrepancy between the BeppoSax/HETE2 and Swift observed redshift distributions is only partially explained by the different thresholds of the detectors and it may indicate strong selection effects. After trying different forms of the star formation rate (SFR) we find that the observed Swift redshift distribution, with more observed high redshift bursts than expected, is inconsistent with a GRB rate that simply follows current models for the SFR. We show that this can be explained by GRB evolution beyond the SFR (more high redshift bursts). Alternatively this can also arise if the luminosity function evolves and earlier bursts were more luminous or if strong selection effects affect the redshift determination.Comment: 15 pages, 8 figures, accepted for publication in JCA

Multi objective optimization in charge management of micro grid based multistory carpark

Distributed power supply with the use of renewable energy sources and intelligent energy flow management has undoubtedly become one of the pressing trends in modern power engineering, which also inspired researchers from other fields to contribute to the topic. There are several kinds of micro grid platforms, each facing its own challenges and thus making the problem purely multi objective. In this paper, an evolutionary driven algorithm is applied and evaluated on a real platform represented by a private multistory carpark equipped with photovoltaic solar panels and several battery packs. The algorithm works as a core of an adaptive charge management system based on predicted conditions represented by estimated electric load and production in the future hours. The outcome of the paper is a comparison of the optimized and unoptimized charge management on three different battery setups proving that optimization may often outperform a battery setup with larger capacity in several criteria.Web of Science117art. no. 179