scholarly journals Wolf Pack Algorithm for Unconstrained Global Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Hu-Sheng Wu ◽  
Feng-Ming Zhang
Keyword(s):  
The Tibetan Plateau ◽  
Distance Measurements ◽  
Artificial Fish Swarm Algorithm ◽  
Bee Colony ◽  
Distribution Mode ◽  
Artificial Fish Swarm ◽  
Winner Take All ◽  
Different Characteristics ◽  
The Impact ◽  
Generation Rule

The wolf pack unites and cooperates closely to hunt for the prey in the Tibetan Plateau, which shows wonderful skills and amazing strategies. Inspired by their prey hunting behaviors and distribution mode, we abstracted three intelligent behaviors, scouting, calling, and besieging, and two intelligent rules, winner-take-all generation rule of lead wolf and stronger-survive renewing rule of wolf pack. Then we proposed a new heuristic swarm intelligent method, named wolf pack algorithm (WPA). Experiments are conducted on a suit of benchmark functions with different characteristics, unimodal/multimodal, separable/nonseparable, and the impact of several distance measurements and parameters on WPA is discussed. What is more, the compared simulation experiments with other five typical intelligent algorithms, genetic algorithm, particle swarm optimization algorithm, artificial fish swarm algorithm, artificial bee colony algorithm, and firefly algorithm, show that WPA has better convergence and robustness, especially for high-dimensional functions.

PERFORMANCE EVALUATION OF CULTURAL ARTIFICIAL BEE COLONY AND CULTURAL ARTIFICIAL FISH SWARM ALGORITHM

2019 ◽  
Vol 6 (4) ◽  
pp. 43
Author(s):  
HADIR ADEBIYI BUSAYO ◽  
TIJANI SALAWUDEEN AHMED ◽  
FOLASHADE O. ADEBIYI RISIKAT ◽  
◽  
◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Artificial Bee Colony ◽  
Artificial Fish Swarm Algorithm ◽  
Bee Colony ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

Impact of Chatbot in Transforming the Face of Retailing- An Empirical Model of Antecedents and Outcomes

2019 ◽  
Vol 12 ◽  
Author(s):  
Kumari Anshu ◽  
Loveleen Gaur ◽  
Arun Solanki
Keyword(s):  
Research Work ◽  
Shopping Behavior ◽  
Model Framework ◽  
Shopping Experience ◽  
The Face ◽  
Strongly Agree ◽  
Different Characteristics ◽  
Work Done ◽  
The Impact ◽  
Self Administered Questionnaire

Chatbot has emerged as a significant resolution to the swiftly growing customer caredemands in recent times. Chatbot has emerged as one of the biggest technological disruption. Simply speaking, it is a software agent facilitating interaction between computers and humans in natural language. So basically, it is a simulated, intellectual dialogue agent functional in a range of consumer engagement circumstances. It is the easiest and simplest means enable interaction between the retailers and the customers. </p><p> • Purpose- Most of the research work done in this field is concerned with their technical aspects. The recent research on chatbot pay little attention to the impact it is creating on users’ experience. Through this work, author is making an effort to know the customer-oriented impact that the chatbot bear on the shoppers. The purpose of this study is to develop and empirically test a framework that identify the customer oriented attributes of chatbot and impact of these attributes on customers. </p><p> • Objectives- The study intends to bridge the gap between concepts and actual attributes and applications on the subject of Chatbot. The following research objectives can address the various aspects of Chatbot affecting the different characteristics of consumers shopping behaviors: a) Identify the various attributes of chatbot that bears an impression on consumer shopping behavior. b) Evaluate the impact of chatbot on consumer shopping behavior that leads to the development of chatbot usage and adoption among the customer. </p><p> • Design/Methodology/Approach – For the purpose of analysis, author has administered Factor analysis and Multiple regression using SPSS version 23 for identification of various attributes of Chatbot and knowing their impact on shoppers. A self-administered questionnaire from the review of literature is developed. Industry experts in the field of retailing and academician evaluate the questionnaire. Primary information from the respondents is gathered using this questionnaire. The questionnaire comprises of Likert scale on a scale of 1 to 5 where 1 stands for strongly disagree and 5 stands for strongly agree. Data is collected from 126 respondents, out of which 111 respondents were finally considered for study and analysis purpose. </p><p> • Findings – The empirical results show that the study identifies various attributes of chatbot like Trust, Usefulness, Satisfaction, Readiness to Use and Accessibility. It is also found that chatbot is really influencing the customers in providing them with shopping experience, which can be very helpful to the businesses for increasing the sales and creating repurchase intention among the customers. </p><p> • Originality/value – The recent research on chatbot pay little attention to the impact it is creating on customers who are actually interacting with it on regular basis. The research paper extends information for understanding and appreciating the customer oriented attributes of artificially intelligent Chatbot. In this regard, the author has developed a model framework and proposed the attributes identified. Through the work, author is also making an effort to test empirically the impact of the identified attributes on the shoppers.

scholarly journals Time Granularity Impact on Propagation of Disruptions in a System-of-Systems Simulation of Infrastructure and Business Networks

2021 ◽  
Vol 18 (8) ◽  
pp. 3922
Author(s):  
Mateusz Iwo Dubaniowski ◽  
Hans Rudolf Heinimann
Keyword(s):  
Recovery Time ◽  
System Of Systems ◽  
Future Research ◽  
Business Networks ◽  
Simulation Experiments ◽  
Huge Impact ◽  
High Level ◽  
Systems Simulation ◽  
Different Characteristics ◽  
The Impact

A system-of-systems (SoS) approach is often used for simulating disruptions to business and infrastructure system networks allowing for integration of several models into one simulation. However, the integration is frequently challenging as each system is designed individually with different characteristics, such as time granularity. Understanding the impact of time granularity on propagation of disruptions between businesses and infrastructure systems and finding the appropriate granularity for the SoS simulation remain as major challenges. To tackle these, we explore how time granularity, recovery time, and disruption size affect the propagation of disruptions between constituent systems of an SoS simulation. To address this issue, we developed a high level architecture (HLA) simulation of three networks and performed a series of simulation experiments. Our results revealed that time granularity and especially recovery time have huge impact on propagation of disruptions. Consequently, we developed a model for selecting an appropriate time granularity for an SoS simulation based on expected recovery time. Our simulation experiments show that time granularity should be less than 1.13 of expected recovery time. We identified some areas for future research centered around extending the experimental factors space.

scholarly journals Spatio-Temporal Variation Characteristics of Snow Depth and Snow Cover Days over the Tibetan Plateau

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 307
Author(s):  
Chi Zhang ◽  
Naixia Mou ◽  
Jiqiang Niu ◽  
Lingxian Zhang ◽  
Feng Liu
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Snow Depth ◽  
Feedback Effect ◽  
Effect Of Temperature ◽  
The Tibetan Plateau ◽  
Reanalysis Dataset ◽  
Spatio Temporal ◽  
The Impact ◽  
Main Factor

Changes in snow cover over the Tibetan Plateau (TP) have a significant impact on agriculture, hydrology, and ecological environment of surrounding areas. This study investigates the spatio-temporal pattern of snow depth (SD) and snow cover days (SCD), as well as the impact of temperature and precipitation on snow cover over TP from 1979 to 2018 by using the ERA5 reanalysis dataset, and uses the Mann–Kendall test for significance. The results indicate that (1) the average annual SD and SCD in the southern and western edge areas of TP are relatively high, reaching 10 cm and 120 d or more, respectively. (2) In the past 40 years, SD (s = 0.04 cm decade−1, p = 0.81) and SCD (s = −2.3 d decade−1, p = 0.10) over TP did not change significantly. (3) The positive feedback effect of precipitation is the main factor affecting SD, while the negative feedback effect of temperature is the main factor affecting SCD. This study improves the understanding of snow cover change and is conducive to the further study of climate change on TP.

scholarly journals Optimization of a Novel Urban Growth Simulation Model Integrating an Artificial Fish Swarm Algorithm and Cellular Automata for a Smart City

2021 ◽  
Vol 13 (4) ◽  
pp. 2338
Author(s):  
Xinxin Huang ◽  
Gang Xu ◽  
Fengtao Xiao
Keyword(s):  
Land Use ◽  
Sustainable Development ◽  
Urban Growth ◽  
Smart City ◽  
Urban Land ◽  
Urban Land Use ◽  
Growth Simulation ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Ca Model

As one of the 17 Sustainable Development Goals, it is sensible to analysis historical urban land use characteristics and project the potentials of urban sustainable development for a smart city. The cellular automaton (CA) model is the widely applied in simulating urban growth, but the optimum parameters of variables driving urban growth in the model remains to be continued to improve. We propose a novel model integrating an artificial fish swarm algorithm (AFSA) and CA for optimizing parameters of variables in the urban growth model and make a comparison between AFSA-CA and other five models, which is used to study a 40-year urban land growth of Wuhan. We found that the urban growth types from 1995 to 2015 appeared relatively consistent, mainly including infilling, edge-expansion and distant-leap types in Wuhan, which a certain range of urban land growth on the periphery of the central area. Additionally, although the genetic algorithms (GA)-CA model and the AFSA-CA model among the six models due to the distance variables, the parameter value of the GA-CA model is −15.5409 according to the fact that the population (POP) variable should be positively. As a result, the AFSA-CA model regardless of the initial parameter setting is superior to the GA-CA model and the GA-CA model is superior to all the other models. Finally, it is projected that the potentials of urban growth in Wuhan for 2025 and 2035 under three scenarios (natural urban land growth without any restrictions (NULG), sustainable urban land growth with cropland protection and ecological security (SULG), and economic urban land growth with sustainable development and economic development in the core area (EULG)) focus mainly on existing urban land and some new town centers based on AFSA-CA urban growth simulation model. An increasingly precise simulation can determine the potential increase area and quantity of urban land, providing a basis to judge the layout of urban land use for urban planners.

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