Implementation of Particle Swarm Optimization Algorithm in Crossdocking Distribution Problem

Purba, Samuel Ro Paian; Amarilies, Harummi Sekar; Rachmawati, Nur Layli; Redi, A.A.N Perwira

doi:10.26480/aim.01.2021.16.20

Cited by 6 publications

(4 citation statements)

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“…Thus, 20 parameters are considered as the controllable parameters. The adjustment range of these parameters is given in Table 2 [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43]. To design a robust PSS, the system's operating conditions are described based on active power (P), and reactive power (Q) at the terminal of the generators, and the load points of C 1 , C 2 , L 1 , and L 2 .…”

Section: Simulation and Analysis Of The Resultsmentioning

confidence: 99%

Intelligent Design of Multi-Machine Power System Stabilizers (PSSs) Using Improved Particle Swarm Optimization

et al. 2022

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In this paper, an improved version of the particle swarm optimization algorithm is proposed for the online tuning of power system stabilizers in a standard four-machine two-area power system to mitigate local and inter-area mode oscillations. Moreover, an innovative objective function is proposed for performing the optimization, which is a weight function of two functions. The first part of fitness is the function of the angular velocity deviation of the generators, and the other part is a function based on the percentage of undershoot and maximum overshoot, and also the damping time of the power system oscillations. The performance of the proposed stabilization method is compared with the genetic algorithm and bacteria foraging algorithm results. Simulations are made in three different power system operation conditions by changing the system load. The simulation results indicate the superiority of the proposed method over the genetic algorithm and bacteria foraging algorithm. In all the scenarios, power system oscillations are damped faster and with lower amplitude when the power system stabilizers coordinate with the proposed optimization method.

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Section: Simulation and Analysis Of The Resultsmentioning

confidence: 99%

Intelligent Design of Multi-Machine Power System Stabilizers (PSSs) Using Improved Particle Swarm Optimization

et al. 2022

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“…It addresses challenges using the notion of social collaboration. 48 This technique finds the near-optimal answer in the search space by combining many particles in a group motion. PSO method is a social search algorithm based on bird swarm social behavior.…”

Section: Methodsmentioning

confidence: 99%

“…The PSO is a strong random optimization tool inspired by intelligent bird swarm movements. It addresses challenges using the notion of social collaboration 48 . This technique finds the near‐optimal answer in the search space by combining many particles in a group motion.…”

Section: Methodsmentioning

confidence: 99%

A new method for task scheduling in fog‐based medical healthcare systems using a hybrid nature‐inspired algorithm

Wang

Arefzadeh

2022

Concurrency and Computation

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Summary The goal of the healthcare system is to offer a dependable and well‐organized solution for improving human's wellbeing. Examining a patient's history can assist clinicians in considering the patient's wants when building a healthcare system and providing service, resulting in increased patient satisfaction. Thus, healthcare is becoming a more competitive sector. Massive data volume, latency, response time, and security susceptibility are all difficulties resulting from this substantial increase in healthcare systems. As a famous distributed structure, fog computing might thus aid in the resolution of such problems. Processing parts are situated among end devices and cloud components in a fog computing infrastructure and run programs. This design is well suited to real‐time and low‐latency applications, like healthcare systems. Because task scheduling is an NP‐hard optimization issue in fog‐based medical healthcare systems, this work proposes a hybrid genetic algorithm and particle swarm optimization (GA‐PSO) strategy. A powerful single‐objective optimization technique is the GA‐PSO. Individuals in a novel generation are formed in GA‐PSO through mutation and crossover operations in GA‐PSO, which uses a redefined local optimization swarm. Hence, it may avoid local minimums and perform well in global searches. The study's goal in fog‐based medical healthcare systems is to lower the makespan and overall response time. The suggested technique is simulated in MATLAB and compared to the GA and PSO methods. The empirical findings confirmed the improved makespan, making the approach appropriate for medical and real‐time systems applications.

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“…An important point to note is that each individual in the colony represents one particle. PSO is an effective tool for solving global optimization problems based on two major components: iterations and search [38]. The major goal of PSO is that, within the swarm, the best global and optimal solution should be sought among the undetermined number of particles using cooperation and information sharing.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Hybridized Adaptive Neuro-Fuzzy Inference System with Metaheuristic Algorithms for Modeling Monthly Pan Evaporation

Adnan

Jaafari

Milan

et al. 2022

Water

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Precise estimation of pan evaporation is necessary to manage available water resources. In this study, the capability of three hybridized models for modeling monthly pan evaporation (Epan) at three stations in the Dongting lake basin, China, were investigated. Each model consisted of an adaptive neuro-fuzzy inference system (ANFIS) integrated with a metaheuristic optimization algorithm; i.e., particle swarm optimization (PSO), whale optimization algorithm (WOA), and Harris hawks optimization (HHO). The modeling data were acquired for the period between 1962 and 2001 (480 months) and were grouped into several combinations and incorporated into the hybridized models. The performance of the models was assessed using the root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe Efficiency (NSE), coefficient of determination (R2), Taylor diagram, and Violin plot. The results showed that maximum temperature was the most influential variable for evaporation estimation compared to the other input variables. The effect of periodicity input was investigated, demonstrating the efficacy of this variable in improving the models’ predictive accuracy. Among the models developed, the ANFIS-HHO and ANFIS-WOA models outperformed the other models, predicting Epan in the study stations with different combinations of input variables. Between these two models, ANFIS-WOA performed better than ANFIS-HHO. The results also proved the capability of the models when they were used for the prediction of Epan when given a study station using the data obtained for another station. Our study can provide insights into the development of predictive hybrid models when the analysis is conducted in data-scare regions.

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Implementation of Particle Swarm Optimization Algorithm in Crossdocking Distribution Problem

Cited by 6 publications

References 0 publications

Intelligent Design of Multi-Machine Power System Stabilizers (PSSs) Using Improved Particle Swarm Optimization

Intelligent Design of Multi-Machine Power System Stabilizers (PSSs) Using Improved Particle Swarm Optimization

A new method for task scheduling in fog‐based medical healthcare systems using a hybrid nature‐inspired algorithm

Hybridized Adaptive Neuro-Fuzzy Inference System with Metaheuristic Algorithms for Modeling Monthly Pan Evaporation

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