A hybrid algorithm using particle swarm optimization for solving transportation problem

Singh, Gurwinder; Singh, Amarinder

doi:10.1007/s00521-019-04656-1

Cited by 22 publications

(11 citation statements)

References 38 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]. The calculation of this method starts with calculating the distance between the points of the sacred graves and the centers of micro and medium enterprises.…”

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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“…Searching on PSO continues until the specified number of iterations is achieved or the optimum error value is reached. 31,32 Each particle has its speed in PSO and increases this speed compared to the particle that is better than it. In each iteration, this speed is recalculated using the best results.…”

Section: Psomentioning

confidence: 99%

“…It is called global best or gbest. Searching on PSO continues until the specified number of iterations is achieved or the optimum error value is reached 31,32 …”

Section: The Control Strategymentioning

confidence: 99%

Design of intelligence‐based optimized adaptive fuzzy PID controllers for a two chamber microbial fuel cell

Eren

Demir

2022

Asia-Pacific J Chem Eng

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The output voltage of microbial fuel cells (MFCs) needs to be controlled effectively to improve the operating efficiency of MFC system against the load variations, disturbances, and uncertainties. The output voltage control is purposed to provide the MFC with an appropriate amount of fuel required to maintain the stability of output voltage. In the applications, the MFC output voltage can be requested to track the variable reference voltage or to track the constant output voltage under changing conditions such as load change. For this purpose, two adaptive fuzzy PID (FPID) controllers powered by optimization algorithms were designed in this study to keep the output voltage value of the MFC at the desired values. This study extends the previous works by using optimization algorithms, such as particle swarm optimization (PSO) and gray wolf optimization (GWO) algorithms, to adjust fuzzy logic parameters, which are used for tuning Proportional‐Integral‐Derivative (PID) controllers. The optimization‐based adaptive FPID controllers play an active role in efficient, fast, and robust tracking of the reference voltage value with its adaptive structure. The designed adaptive control algorithms were tested under different cases, which involve the variation of the reference voltage and load conditions. The results show that both of these controllers can effectively control the output voltage of a MFC by regulating the flow rate of the fuel. In addition, the performances of the designed controller strategies according to the results obtained vary according to the reference signal. While PSO‐based FPID gives more successful results for positive step changes, GWO‐based FPID gives more successful results for negative step changes. These two control strategies show more efficient, robust, successful, and faster performances compared to the traditional PID controller.

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“…Jordehi and Jasni [27] have reviewed these encoding/decoding techniques which keep the solution particles in the feasible solution space. Recently, Singh & Singh [28] has hybridized PSO to solve TP and resolved this issue by incorporating additional modules into the basic PSO. Thus, in this paper, a similar approach has been adopted to solve the time-minimization transportation problem.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

A modified Particle Swarm Optimization Algorithm to solve Time Minimization Transportation Problem

Singh*,

Singh

2020

IJRTE

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When the supply of items need urgent/earliest delivery to the destinations, the Time Minimization Transportation Problems (TMTPs) are indispensable. Traditionally these problems have been solved using the exact techniques, however, the (meta) heuristic techniques have provided a great breakthrough in search space exploration. Particle Swarm Optimization is one such meta-heuristic technique that has been applied on a wide variety of continuous optimization problems. For discrete problems, either the mathematical model of problem or the solution procedure has been changed. In this paper, the PSO has been modified to incorporate the discrete nature of variables and the non-linearity of the objective function. The proposed PSO is tested on the problems available in the literature and the optimal solutions are obtained efficiently. The exhaustive search capability of proposed PSO is established by obtaining alternate optimal solutions and the combinations of the allocated cells that are beyond ( ) m n   1 in number. This proposed solution technique, therefore, provides an effective alternate to the analytical techniques for decision making in the logistic systems.

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A hybrid algorithm using particle swarm optimization for solving transportation problem

Cited by 22 publications

References 38 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

Design of intelligence‐based optimized adaptive fuzzy PID controllers for a two chamber microbial fuel cell

A modified Particle Swarm Optimization Algorithm to solve Time Minimization Transportation Problem

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