Flight Search Research Articles

Power Transformer Fault Diagnosis Based on Random Forest and Improved Particle Swarm Optimization–Backpropagation–AdaBoost

This paper proposes a novel fault diagnosis methodology for oil-immersed transformers to improve the diagnostic accuracy influenced by gas components in power transformer oil. Firstly, the Random Forest (RF) algorithm is utilized to evaluate and filter the raw data features, solving the problem of determining significant features in the dataset. Secondly, a multi-strategy Improved Particle Swarm Optimization (IPSO) is applied to optimize a double-hidden layer backpropagation neural network (BPNN), which overcomes the challenge of determining hyperparameters in the model. Four enhancement strategies, including SPM chaos mapping based on opposition-based learning, adaptive weight, spiral flight search, and crisscross strategies, are introduced based on traditional Particle Swarm Optimization (PSO) to enhance the model’s optimization capabilities. Lastly, AdaBoost is integrated to fortify the resilience of the IPSO-BP network. Ablation experiments demonstrate an enhanced convergence rate and model accuracy of IPSO. Case analysis using Dissolved Gas Analysis (DGA) samples compares the proposed IPSO–BP–AdaBoost model with other swarm intelligence optimization algorithms integrated with BPNN. The experimental findings highlight the superior diagnostic accuracy and classification performance of the IPSO–BP–AdaBoost model.

Quantum-Accelerated Flight Selection: Probing Grover's Algorithm and Quantum Device Efficiency

Current flight search platforms primarily consider four essential factors when planning a trip: departure/arrival dates, as well as the origin and destination locations. However, when additional parameters are added to this search, the problem shifts from a simple to a complex search, as the engine must sift through a massive dataset of flights, including information on airlines, flight routes, fees, and more. To address this challenge and improve flight search efficiency amidst data-intensive and resource-demanding environments, this paper proposes the use of Grover's search algorithm. This algorithm is demonstrated as the optimal solution for searches with increased constraints. The paper highlights the practical application of Grover's search algorithm across three datasets of assorted sizes, as well as various quantum hardware and simulators available in the NISQ era. Furthermore, this paper provides an in-depth understanding of the complexity of quantum circuit design, including the key phases of state encoding and amplitude amplification. The effectiveness of these approaches is evaluated through analysis of execution times and quantum measurement results. The aim is to showcase the potential of quantum computing in revolutionizing real-world search tasks, particularly in the realm of flight selection.

FLYWISE AIRWAYS WEBSITE

Abstract— The FLYWISE Airways website is a user-centric platform designed to streamline the booking process and enhance the overall travel experience for passengers. With its intuitive interface and robust functionality, the website allows users to easily search for flights, compare fares, and make secure online bookings. In addition to booking flights, the website also provides valuable information about FLYWISE Airways’ services, fleet, and destinations. The website’s responsive design ensures a seamless experience across all devices, making it easy for passengers to access the information they need, whenever they need it. With its focus on user convenience and accessibility, the FLYWISE Airways website is poised to become the go-to platform for travelers seeking a hassle-free booking experience. The FLYWISE Airways website is designed with mobile responsiveness in mind, ensuring that passengers can access the website from any device, including smartphones and tablets. This ensures that passengers can easily book flights and access important information while on the go. Keywords – Flight booking, air travel, online reservations, flight search, compare fares, secure booking, flight information, manage bookings, seat selection, extra baggage, in-flight meals, frequent flyer program, mobile responsiveness, user-friendly interface.

A discrete moth-flame optimization algorithm for multiple automated guided vehicles scheduling problem in a matrix manufacturing workshop

The growing need for customized and varied production has highlighted the significance of smart and automated factories in the manufacturing sector. In this particular context, the scheduling of multiple Automated Guided Vehicles (AGVs) plays a pivotal role in enhancing the efficiency of operations within an intelligent manufacturing shop. This study centers on the material handling process within a matrix manufacturing shop with the objective of identifying the most cost-effective transportation routes for materials. To accomplish the specified objective, this research aims to identify an optimal solution for reducing transportation costs. In particular, this study formulates a mixed-integer linear programming model and introduces a novel discrete variant of the moth-flame optimization (MFO) algorithm, named DMFO, to address the scheduling problem. The DMFO algorithm incorporates several significant enhancements. Firstly, a population initialization method is proposed, which combines a nearest-neighbor-based adaptive heuristic and a random sorting technique to ensure the formation of a well-structured population. Secondly, the flame generation mechanism and spiral flight search processes within the MFO have been redefined to achieve a more optimal balance between exploration and exploitation. A neighborhood search mechanism is subsequently devised, employing the concept of neighborhood relevance to accelerate the convergence process. Additionally, a heuristic approach is introduced to reduce the computational cost. Moreover, a population regeneration mechanism is proposed to avoid the algorithm falling into a local optimum. To validate the effectiveness of the DMFO, a comparative analysis is conducted using a dataset of 110 real-world factory instances. In this analysis, eight well-established optimization algorithms are employed. The simulation results consistently demonstrate that the relative percentage deviation (RPD) value of the DMFO tends to approach 0% more closely compared to other algorithms, thereby substantiating the effectiveness of the proposed algorithm.

Counterexample to the Lévy flight foraging hypothesis in the narrow capture framework

The Lévy flight foraging hypothesis asserts that biological organisms have evolved to employ (truncated) Lévy flight searches due to such strategies being more efficient than those based on Brownian motion. However, we provide here a concrete two-dimensional counterexample in which Brownian search is more efficient. In fact, we show that the efficiency of Lévy searches worsens the farther the Lévy flight tail index deviates from the Brownian limit. Our counterexample is based on the framework of the classic narrow capture problem in which a random search is performed for a small target within a confined search domain. Our results are obtained via three avenues: Monte Carlo simulations of the discrete search processes, finite-difference solutions, and a matched asymptotic analysis of the elliptic (pseudo)differential equations of the corresponding continuum limits. Asymptotic analysis of the Lévy search yields an expression for the average search time accurate to O(1), providing insights into how the latter is impacted by various features of the target and search domain. Published by the American Physical Society 2024

Enhancing the Usability, Visibility, and Responsiveness of an Airline Reservation System: A User-Centered Design Approach

This paper presents the idea, design, and prototype of a flight search and airline booking system based on the perspective of user-centered design. The system is first sketched roughly on paper in the form of a sketched plan and implemented through the proper system by connecting with the rapid API to develop a responsive web application. Booking travel tickets is a hassle and quite stressful because there is a chance that the webpages take time, and several decisions to make, hard to choose a discounted or less expensive flight, and the user will have to put in a lot of effort with many browser tabs may leave open. If a user is looking for the lowest travel options within a range of dates, they need to search a lot of websites looking for better options. As UX designers, it is our responsibility to do some user research and identify the problem areas, then we will recommend some design options based on the research findings. After that, we will create a wireframe and prototype before jumping into web design by collecting all the requirements and analyzing the problems. We will be focusing on UI controls such as location picker, date picker, color contrast, accessibility, and so on. In this paper, we present the design and development of a user-centered flight search and booking system for the airline industry. Our goal is to create a system that would meet the needs and preferences of a diverse set of users. This paper will summarize the design, development, and implementation of an airline reservation system. We have used bubble.io to design the overall system and MYSQL as the database management system for this webpage. Our objective is to upgrade the current website by improving the usability, visibility, and responsiveness of the functions that the user will experience while buying a flight ticket. We have generated and managed the design documentation and a perfect user-based online flight booking system.

Multi‐mode non‐linear inversion of Rayleigh wave dispersion curves with grey wolf optimization and cuckoo search algorithm

AbstractDispersion curve inversion is one of the core contents of Rayleigh wave data processing. However, the dispersion curve inversion has the characteristics of multi‐parameter, multi‐extremum as well as nonlinearity. In the face of Rayleigh wave data processing under complex seismic‐geological conditions, it is difficult to reconstruct an underground structure quickly and accurately apply a single global‐searching non‐linear inversion algorithm. For this reason, we proposed a strategy to invert multi‐order mode Rayleigh wave dispersion curves by combining with grey wolf optimization (GWO) and cuckoo search (CS) algorithms. On the basis of introducing the mechanism of iterative chaotic map with infinite collapses (ICMIC) and the strategy of dimension learning–based hunting (DLH), an improved GWO was developed that was called IDGWO (ICMIC and DLH GWO). After searching the near‐optimal region through IDGWO, the CS with a variable step‐size Lévy flight search mechanism was switched adaptively to complete the final inversion. The correctness of our method was verified by the multi‐order mode dispersion curve inversion of a six‐layer high‐velocity interlayer model. Then it was further applied to the processing of real seismic datasets. The research results show that our method fully utilizes the advantages of each of the two global‐searching non‐linear algorithms after integrating IDGWO and CS, while effectively balancing the ability between global search and local exploitation, further improving the convergence speed and inversion accuracy and having good anti‐noise performance.

Using tourism intelligence and big data to explain flight searches for tourist destinations: The case of the Costa Blanca (Spain)

Tourism intelligence and big data can improve the strategic management of tourist destinations through analysis of the environment. This study aims to explain the flight searches from London and Manchester to the Costa Blanca using variables from big data sources: online hotel satisfaction levels, flight price, hotel price, and temperature. Data for three years (2019, 2020 and 2021) are analyzed and the results are compared. The results show that online hotel satisfaction levels heterogeneously explain flight searches during these years. Hotel price positively explains searches from London. Flight price only influenced searches in 2019. Temperature in outbound destinations is the variable that best explains flight searches. This study contributes to the literature on strategic management of tourist destinations by explaining the potential tourist demand in the early decision-making stages of a trip. We highlight that the explanatory variables do not behave consistently during the years analyzed.

Generalization of navigation memory in honeybees.

Flying insects like the honeybee learn multiple features of the environment for efficient navigation. Here we introduce a novel paradigm in the natural habitat, and ask whether the memory of such features is generalized to novel test conditions. Foraging bees from colonies located in 5 different home areas were tested in a common area for their search flights. The home areas differed in the arrangements of rising natural objects or their lack, and in the existence or lack of elongated ground structures. The test area resembled partly or not at all the layout of landmarks in the respective home areas. In particular, the test area lacked rising objects. The search flights were tracked with harmonic radar and quantified by multiples procedures, extracting their differences on an individual basis. Random search as the only guide for searching was excluded by two model calculations. The frequencies of directions of flight sectors differed from both model calculations and between the home areas in a graded fashion. Densities of search flight fixes were used to create heat maps and classified by a partial least squares regression analysis. Classification was performed with a support vector machine in order to account for optimal hyperplanes. A rank order of well separated clusters was found that partly resemble the graded differences between the ground structures of the home areas and the test area. The guiding effect of elongated ground structures was quantified with respect to the sequence, angle and distance from these ground structures. We conclude that foragers generalize their specific landscape memory in a graded way to the landscape features in the test area, and argue that both the existence and absences of landmarks are taken into account. The conclusion is discussed in the context of the learning and generalization process in an insect, the honeybee, with an emphasis on exploratory learning in the context of navigation.

Assessing the effect of flight information presentation styles on the usability of airline web booking interface

Airline websites are expected to become the major channel for flight bookings in the coming decade. Unlike e-commerce websites, airline products are intangible and complex, with varying sales conditions attached. This study aimed at testing the usability of two types of flight search information presentation styles— a text-based and a graphic-based interface—via flight booking experiments in a laboratory environment. Results showed that the graphic-based interface could improve flight booking accuracy with higher user satisfaction. The graphic-based interface was found to be able to reduce flight booking time when the flight booking scenario was simple but was not able to help when a complex flight booking scenario was presented. The graphic-based interface also improved web page browsing efficiency by reducing eye-fixation duration. In addition, users' personal factors, such as booking experience and learning styles, were found to have a significant influence on users’ mental effort during the flight selection stage of booking.

International air travel attitude and travel planning lead times across 45 countries in response to the COVID-19 pandemic

This paper explores global differences in travel risk perception based on i) attitudes towards travel abroad, and ii) the time required to plan travel. Baseline data from 2019 is compared with data from 2020, the first year of the COVID-19 pandemic. A methodology based on Big Data is developed through the Skyscanner metasearch engine, working with 20,756 million flight searches and 1979 million flight picks worldwide. We conduct an exploratory analysis by region, followed by a cluster analysis of 45 countries. We argue that the findings respond to uncertainty avoidance, with clear differences between Europe, America and Asia-Pacific. This knowledge has marketing implications for tourist destinations in terms of what marketing messages to convey and the best time to introduce marketing campaigns for each country or group of countries, so that the opportunity for reactivation of tourism is maximised.

Output-only structural damage identification using hybrid Jaya and differential evolution algorithm with reference-free correlation functions

To solve the optimization-based structural damage identification problem, a novel hybrid algorithm based on Jaya and differential evolution algorithm (HJDEA) is proposed to detect, locate and quantify structural damages by effectively incorporating the powerful local exploitation capacity of Jaya algorithm and global exploration capability of differential evolution. Meanwhile, Hammersley sequence initialization and Lévy flight search mechanism are introduced into HJDEA to further improve convergence rate and refining the quality of the best solution. Four different algorithms, genetic algorithm, particle swarm optimization, Jaya and the proposed HJDEA are employed for comparative study. In addition, the objective function is established by adjacent acceleration correlation function so as to avoid false identification caused by defining improper reference point. The performance of the proposed damage identification strategy based on HJDEA and adjacent acceleration correlation function is investigated with numerical examples involving an 8-DOF lumped mass model and a cantilever beam, as well as an experimental study of the ASCE benchmark structure under white noise excitation. Results show that the proposed hybrid identification method is accurate, efficient and robust in the identification of the damage existence, location and severity of stiffness and mass parameters even with partial output-only responses and 20% noise-polluted measurements.

Asymmetric Lévy Flights Are More Efficient in Random Search

We study the first-arrival (first-hitting) dynamics and efficiency of a one-dimensional random search model performing asymmetric Lévy flights by leveraging the Fokker–Planck equation with a δ-sink and an asymmetric space-fractional derivative operator with stable index α and asymmetry (skewness) parameter β. We find exact analytical results for the probability density of first-arrival times and the search efficiency, and we analyse their behaviour within the limits of short and long times. We find that when the starting point of the searcher is to the right of the target, random search by Brownian motion is more efficient than Lévy flights with β≤0 (with a rightward bias) for short initial distances, while for β>0 (with a leftward bias) Lévy flights with α→1 are more efficient. When increasing the initial distance of the searcher to the target, Lévy flight search (except for α=1 with β=0) is more efficient than the Brownian search. Moreover, the asymmetry in jumps leads to essentially higher efficiency of the Lévy search compared to symmetric Lévy flights at both short and long distances, and the effect is more pronounced for stable indices α close to unity.

Distributed Resource Scheduling for Large-Scale MEC Systems: A Multiagent Ensemble Deep Reinforcement Learning With Imitation Acceleration

In large-scale mobile edge computing (MEC) systems, the task latency, and energy consumption are important for massive resource-consuming and delay-sensitive Internet of Things Devices (IoTDs). Against this background, we propose a distributed intelligent resource scheduling (DIRS) framework to minimize the sum of task latency and energy consumption for all IoTDs, which can be formulated as a mixed-integer nonlinear programming. The DIRS framework includes centralized training relying on the global information and distributed decision making by each agent deployed in each MEC server. Specifically, we first introduce a novel multiagent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent by partitioning the state space and also improve the performance of a single agent by combining decisions of all the agents. Second, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel Levy flight search. Finally, an imitation acceleration scheme is presented to pretrain all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience from a small amount of demonstration data. The simulation results in three typical scenarios demonstrate that the proposed DIRS framework is efficient and outperforms the existing benchmark schemes.

The hidden conversion funnel of mobile vs. desktop consumers

We compare mobile and desktop user progress through the conversion funnel. Using detailed log-files of an online flight search engine, we analyze consumer search behavior and model the stages consumers go through along the funnel in both mobile and desktop platforms. We ask: Do mobile and desktop users go through similar funnel stages? Are the funnel stages of both platforms characterized differently? To analyze the progress through the funnel, we use hidden Markov models (HMM) that capture latent funnel stages based on observable search-behavior variables indicative of convergence towards a purchase. We find that while similar stochastic processes characterize funnel stages on both platforms, the distribution of visits across stages in the two platforms is significantly different: a larger percentage of mobile visits resides in more advanced stages, indicating that mobile consumers use the website when they are more advanced in the funnel, where less cognitive effort is required.

A Discrete Moth-Flame Optimization With an $l_2$-Norm Constraint for Network Clustering

Complex network clustering problems have been gained great popularity and widespread researches recently, and plentiful optimization algorithms are aimed at this problem. Among these methods, the optimization methods aiming at multiple objectives can break the limitations (e.g., instability) of those optimizing single objective. However, one shortcoming stands out that these methods cannot balance the exploration and exploitation well. In another sentence, it fails to optimize solutions on the basis of the good solutions obtained so far. Inspired by nature, a new optimized method, named multi-objective discrete moth-flame optimization (DMFO) method is proposed to achieve such a tradeoff. Specifically, we redefine the simple flame generation (SFG) and the spiral flight search (SFS) processes with network topology structure to balance exploration and exploitation. Moreover, we present the DMFO in detail utilizing a Tchebycheff decomposition method with an <inline-formula><tex-math notation="LaTeX">$l_2$</tex-math></inline-formula>-norm constraint on the direction vector (2-Tch). Besides that, experiments are taken on both synthetic and real-world networks and the results demonstrate the high efficiency and promises of our DMFO when tackling dividing complex networks.

Investigating optimal unmanned aircraft systems flight plans for the detection of marine ingress

From the shutting down of coastal tourism industries, the mass destruction of aquaculture, to the clogging of power station water intakes, marine ingress events have the potential to cause widespread disruption along our coastlines. To gain the ability to respond to such events, efforts are being made to advance the understanding of bloom events which predominantly present as large aggregations of jellyfish, or detached aquatic macroalgaes in the water column. This paper investigates the optimal flight search patterns with a focus on marine ingress bloom detection from unmanned aircraft systems (UAS). The detection performance of four flight search patterns are examined against five different bloom shapes. Monte-Carlo simulations are deployed to assess probable performance of flight search pattern against variable bloom shapes. A total of 50,000 simulated flights were conducted, offering a maximum of 500 million marine ingress objects for possible detection. A two phased flight approach is proposed, with first phase flights conducted as area search strategies, and second phase flights as datum searches for scenarios where some information of possible bloom location is available. Parallel sweep was found to be the best performing generalist flight search pattern, closely followed by the phase two search pattern expanding square. Crossing barrier was found to be competitive but appeared to lend itself towards specific detection scenarios with sector search being a consistently poor performing flight search pattern. This paper also investigates the comparative performance of visual line of sight (VLOS), extended visual line of sight (EVLOS), and beyond visual line of sight (BVLOS) operations. Increase of total survey area was found to increase bloom detection frequency, with BVLOS operations the highest performer successfully increasing bloom detection by a factor of 3.7. This paper exhibits the first assessment of flight search patterns within the context of drone-based detection of marine ingress bloom events. This should facilitate the development of an early warning detection system that can provide reliable warning to coastal industries prior to a marine ingress event occurring.

The impact of COVID-19 on European tourists' attitudes to air travel and the consequences for tourist destination evoked set formation

We study how risk conditions derived from the COVID-19 pandemic may impact on both the desire to travel and intention to visit of tourists and, therefore, on different stages of the destination choice process. We analyse 5134 million flight searches and 379 million flight picks during 2020 for the 17 largest European tourism source markets. An unweighted index number is employed to measure the average variation for searches and picks, for the year 2020, in relation to the reference base period (year 2019). This is done for air travel in general and to Spain specifically. The study then proceeds to conduct an analysis of 17 international travel destinations that are in the evoked sets of the two largest outbound markets in Europe (Germany and UK). We also identify which markets are most favourable to Spain. The research design can inform cost-efficient marketing decisions in a situation of high uncertainty.

A Modified Cuckoo Search Algorithm and Its Applications in Function Optimization

A modified Cuckoo search algorithm (MCS) is proposed in this paper to improve the accuracy of the algorithm’s convergence by implementing random operators and adapt the adjustment mechanism of the Levy Flight search step length. Comparative experiments reveal that MCS can effectively adjust the search mechanism in the high-dimensional function optimization and converge to the optimal global value.

Nudging Consumers Toward Greener Air Travel by Adding Carbon to the Equation in Online Flight Search

This study explores the potential to promote lower-emissions air travel by providing consumers with information about the carbon emissions of alternative flight choices in the context of online flight search and booking. We surveyed over 450 employees of the University of California, Davis, asking them to choose among hypothetical flight options for university-related business trips. Emissions estimates for flight alternatives were prominently displayed alongside cost, layovers, and airport, and the lowest-emissions flight was labeled “Greenest Flight.” We found an impressive rate of willingness to pay for lower-emissions flights: around $200/ton of CO2e saved, a magnitude higher than that seen in carbon offsets programs, and consistent with findings from a prior study with a non-university-based sample. In a second step of analysis, we estimated the carbon and cost impacts if the university were to adopt a flight-search interface that prioritizes carbon emissions information and displays alternatives from multiple regional airports in their employee travel-booking portal. We estimated potential annual savings of 79 tons of CO2e, while reducing airfare costs by $56,000, mainly through an increased willingness of travelers to take advantage of cheaper nonstop (lower-emissions) flights from a more distant airport in the region over indirect flights from their preferred airport for medium-distance flights. Institutionalizing this strategy within organizations with large travel budgets could reduce personal and organizational carbon footprints. If implemented across major flight-search engines, it could potentially reduce the demand for higher-emissions flights, leading to an industry-wide impact on aviation emissions.