Fuzzy-based modified particle swarm optimization algorithm for shortest path problems

Dudeja, Chanchal

doi:10.1007/s00500-019-04112-1

Cited by 14 publications

(6 citation statements)

References 30 publications

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“…where ω is inertia weight; c1 and c2 are positive constants known as acceleration coefficients; r1 and r2 are two random variables with a uniform distribution between zero and one [11][12][13][14][15]. Thus, PSO accelerates each particle towards its PBest and GBest locations with randomly weighted accelerations at each step [16,17].…”

Section: Discussionmentioning

confidence: 99%

The Genetic Algorithm for Creating Travel Routes in Religious Tourism Destinations in Bogor Regency, West Java Province

Tandio

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Religious tourism is an activity that is commonly carried out in Indonesia. One of the activities of religious tourism is the community’s habit of visiting sacred graves. Bogor, a city with a long history of 540 years, has many of these sites that attract tourists. This religious tourism activity brings economic, social, and environmental potential and consequences. The distance between the Sacred Tombs scattered at various points requires efficient and effective destination route planning. The application of the optimization method with the Particle Swarm Optimization (PSO) Genetic Algorithm can be a solution for designing an effective travel route. It can be beneficial for improving the economy, in this case, increasing the sales turnover of Bogor small and medium enterprise businesses. The conclusion drawn from the research is that applying a genetic algorithm will create a more efficient route set and will have an impact on saving fuel costs, reducing emissions and providing benefits to the development of the economic sector.

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Section: Discussionmentioning

confidence: 99%

The Genetic Algorithm for Creating Travel Routes in Religious Tourism Destinations in Bogor Regency, West Java Province

Tandio

2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…Therefore, from relations (20), (22) and (25), it is concluded that u * r , v * t ,ȳ r * ih andx t * ih (∀h; (i, h) ∈ A, r = 1, . .…”

Section: Fuzzy Efficient Pathmentioning

confidence: 98%

“…The proposed algorithm by Tajdin et al [21] can compute a shortest path in a network having various types of fuzzy arc lengths. Moreover, the fuzzy-based modified particle swarm optimization algorithm [22] and elite artificial bees' colony algorithm [23] can be used for solving FSP problem. An overview of efficient algorithms for solving SP problems with various types of input data in junction with fuzzy, intuitionistic, vague, interval fuzzy, interval-valued intuitionistic fuzzy and neutrosophic sets can be found in Broumi et al [24].…”

Section: Introductionmentioning

confidence: 99%

Solving fuzzy multi-objective shortest path problem based on data envelopment analysis approach

Bagheri

Ebrahimnejad

Razavyan

et al. 2021

Complex Intell. Syst.

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The shortest path problem (SPP) is a special network structured linear programming problem that appears in a wide range of applications. Classical SPPs consider only one objective in the networks while some or all of the multiple, conflicting and incommensurate objectives such as optimization of cost, profit, time, distance, risk, and quality of service may arise together in real-world applications. These types of SPPs are known as the multi-objective shortest path problem (MOSPP) and can be solved with the existing various approaches. This paper develops a Data Envelopment Analysis (DEA)-based approach to solve the MOSPP with fuzzy parameters (FMOSPP) to account for real situations where input–output data include uncertainty of triangular membership form. This approach to make a connection between the MOSPP and DEA is more flexible to deal with real practical applications. To this end, each arc in a FMOSPP is considered as a decision-making unit with multiple fuzzy inputs and outputs. Then two fuzzy efficiency scores are obtained corresponding to each arc. These fuzzy efficiency scores are combined to define a unique fuzzy relative efficiency. Hence, the FMOSPP is converted into a single objective Fuzzy Shortest Path Problem (FSPP) that can be solved using existing FSPP algorithms.

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“…In the same area of fuzzy logic, Dudeja (2019) presented a study in which a modified fuzzy algorithm was applied to an empirical database. The main objective was to propose a novel strategy to reduce costs and time consumption using fuzzy rules to overcome the problem of the shortest path.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 spread algorithm in the international airport network-DetArpds

Guevara

Coronel

Maldonado

et al. 2023

PeerJ Computer Science

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Due to COVID-19, the spread of diseases through air transport has become an important issue for public health in countries globally. Moreover, mass transportation (such as air travel) was a fundamental reason why infections spread to all countries within weeks. In the last 2 years in this research area, many studies have applied machine learning methods to predict the spread of COVID-19 in different environments with optimal results. These studies have implemented algorithms, methods, techniques, and other statistical models to analyze the information in accuracy form. Accordingly, this study focuses on analyzing the spread of COVID-19 in the international airport network. Initially, we conducted a review of the technical literature on algorithms, techniques, and theorems for generating routes between two points, comprising an analysis of 80 scientific papers that were published in indexed journals between 2017 and 2021. Subsequently, we analyzed the international airport database and information on the spread of COVID-19 from 2020 to 2022 to develop an algorithm for determining airport routes and the prevention of disease spread (DetARPDS). The main objective of this computational algorithm is to generate the routes taken by people infected with COVID-19 who transited the international airport network. The DetARPDS algorithm uses graph theory to map the international airport network using geographic allocations to position each terminal (vertex), while the distance between terminals was calculated with the Euclidian distance. Additionally, the proposed algorithm employs the Dijkstra algorithm to generate route simulations from a starting point to a destination air terminal. The generated routes are then compared with chronological contagion information to determine whether they meet the temporality in the spread of the virus. Finally, the obtained results are presented achieving a high probability of 93.46% accuracy for determining the entire route of how the disease spreads. Above all, the results of the algorithm proposed improved different computational aspects, such as time processing and detection of airports with a high rate of infection concentration, in comparison with other similar studies shown in the literature review.

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Fuzzy-based modified particle swarm optimization algorithm for shortest path problems

Cited by 14 publications

References 30 publications

The Genetic Algorithm for Creating Travel Routes in Religious Tourism Destinations in Bogor Regency, West Java Province

The Genetic Algorithm for Creating Travel Routes in Religious Tourism Destinations in Bogor Regency, West Java Province

Solving fuzzy multi-objective shortest path problem based on data envelopment analysis approach

COVID-19 spread algorithm in the international airport network-DetArpds

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