Contemporary Technologies and Methods for Cross-Platform Application Development

Vassallo, Keith; Garg, Lalit; Prakash, Vijay; Ramesh, K.

doi:10.1166/jctn.2019.8261

Cited by 15 publications

(9 citation statements)

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“…Stochastic optimization algorithms have shown an acceptable ability to effectively solve optimization problems by providing appropriate solutions. These algorithms can be employed in various issues such as cloud computing ( Prakash & Bala, 2014a , 2014b ; Prakash, Bawa & Garg, 2021 ), cross-platform applications ( Vassallo et al, 2019 ), engineering ( Dehghani et al, 2020b ), energy commitment ( Dehghani, Montazeri & Malik, 2019 ) and other optimization challenges in science fields. Stochastic optimization algorithms can be divided into four types, namely evolutionary, swarm, physics and game-based optimization algorithms with respect to the central idea in their design.…”

Section: Literature Reviewmentioning

confidence: 99%

A new optimization algorithm based on average and subtraction of the best and worst members of the population for solving various optimization problems

Dehghani

Hubálovský

Trojovský

2022

PeerJ Computer Science

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In this paper, a novel evolutionary-based method, called Average and Subtraction-Based Optimizer (ASBO), is presented to attain suitable quasi-optimal solutions for various optimization problems. The core idea in the design of the ASBO is to use the average information and the subtraction of the best and worst population members for guiding the algorithm population in the problem search space. The proposed ASBO is mathematically modeled with the ability to solve optimization problems. Twenty-three test functions, including unimodal and multimodal functions, have been employed to evaluate ASBO’s performance in effectively solving optimization problems. The optimization results of the unimodal functions, which have only one main peak, show the high ASBO’s exploitation power in converging towards global optima. In addition, the optimization results of the high-dimensional multimodal functions and fixed-dimensional multimodal functions, which have several peaks and local optima, indicate the high exploration power of ASBO in accurately searching the problem-solving space and not getting stuck in nonoptimal peaks. The simulation results show the proper balance between exploration and exploitation in ASBO in order to discover and present the optimal solution. In addition, the results obtained from the implementation of ASBO in optimizing these objective functions are analyzed compared with the results of nine well-known metaheuristic algorithms. Analysis of the optimization results obtained from ASBO against the performance of the nine compared algorithms indicates the superiority and competitiveness of the proposed algorithm in providing more appropriate solutions.

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Section: Literature Reviewmentioning

confidence: 99%

A new optimization algorithm based on average and subtraction of the best and worst members of the population for solving various optimization problems

Dehghani

Hubálovský

Trojovský

2022

PeerJ Computer Science

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“…There also exist several other studies on native and cross platform development [16][17][18][19][20][21][22][23], however, none of them specifically targeted the comparison of the two approaches as we did in this paper.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Analysis of Mobile Application Development Approaches

Alsaid

Ahmed

Jan

et al. 2021

PPASA

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Over the last decade, there has been a significant increase in the development of mobile applications. The performance of the developed applications depends largely on the development approaches. There are two widely used approaches: (1) native, where the application is targeted and developed for a specific platform, (2) crossplatform, where the developed application runs on multiple platforms. This paper aims to address the question of which approach should be used in various scenarios. We have performed a detailed comparison of the two approaches by developing a mobile app using both approaches. Experiments are performed using Android and iOS, the two most well-known mobile Operating System. The criteria of deciding the best approach include performance, usability and support. Our results show that both approaches are viable depending on the requirements and type of the application to be developed, with native having an edge. Guidelines are presented at the end to help the developers in choosing the best approach. The fundamental differences and advantages of each approach are discussed.

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“…In addition, these applications can be run on different target platforms (operating systems and servers). These capabilities of application development environments are referred to as Cross-Platform Application Development [30]- [32] or Multi-Device Application Development [33].…”

Section: A Development Of An Application For Conducting Experimentsmentioning

confidence: 99%

Development of an Application for Interactive Research and Analysis of the N-Queens Problem

Kralev¹,

Kraleva²,

Chakalov³

2021

International Journal on Advanced Science, Engineering and Information Technology

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Contemporary Technologies and Methods for Cross-Platform Application Development

Cited by 15 publications

References 0 publications

A new optimization algorithm based on average and subtraction of the best and worst members of the population for solving various optimization problems

A new optimization algorithm based on average and subtraction of the best and worst members of the population for solving various optimization problems

A Comparative Analysis of Mobile Application Development Approaches

Development of an Application for Interactive Research and Analysis of the N-Queens Problem

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