Easy Particle Swarm Optimization for Nonlinear Constrained Optimization Problems

Tseng, Hsuan-Yu; Chu, Pao-Hsien; Lu, Hao-Chun; Tsai, Ming-Jyh

doi:10.1109/access.2021.3110708

Cited by 13 publications

(9 citation statements)

References 44 publications

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“…The objective function is minf 39 (x) = V , and constraints are shown in (11), where g 1 is the lower limit constraint on the number of teeth, g 2 and g 3 are the lower and upper limit constraints on the tooth width factor, g 4 is the lower limit constraint on the modulus, g 5 is the upper limit constraint on the diameter of the first gear, g 6 and g 7 are the lower and upper limit constraints on the diameter of the first axis, g 8 and g 9 are the lower and upper limits of the diameter of the second shaft, g 10 is the lower limit constraint of the box thickness, g 11 is the contact stress constraint of the first gear, g 12 and g 13 are the bending stress constraints of the two gears, g 14 and g 15 are the bending stress constraints of the two shafts.…”

Section: Optimization Design Of Single-stage Cylindrical Gear Reducermentioning

confidence: 99%

“…Researchers have developed many types of evolutionary algorithms to solve COPs [9], [10], [11], [12]. The genetic algorithm (GA) is a powerful type of evolutionary algorithm that can solve the non-convex optimization problem because GA keeps a population of solutions and it not only utilizes information in the current population but also explores new areas in the search region.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Genetic Algorithm for Constrained Optimization Problems

Wang

2023

IEEE Access

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The mathematical form of many optimization problems in engineering is constrained optimization problems. In this paper, an improved genetic algorithm based on two-direction crossover and grouped mutation is proposed to solve constrained optimization problems. In addition to making full use of the direction information of the parent individual, the two-direction crossover adds an additional search direction and finally searches in the better direction of the two directions, which improves the search efficiency. The grouped mutation divides the population into two groups and uses mutation operators with different properties for each group to give full play to the characteristics of these mutation operators and improve the search efficiency. In experiments on the IEEE CEC 2017 competition on constrained real-parameter optimization and ten real-world constrained optimization problems, the proposed algorithm outperforms other state-of-the-art algorithms. Finally, the proposed algorithm is used to optimize a singlestage cylindrical gear reducer.

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Section: Optimization Design Of Single-stage Cylindrical Gear Reducermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Genetic Algorithm for Constrained Optimization Problems

Wang

2023

IEEE Access

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“…In [ 18 ], an easy particle was suggested, which is motivated by the effect of a lazy ant in the ant colony for PSO to address the constraint issue in Nonlinear Constrained Optimization (NCO) problems. The easy particle is very convenient to embed in the current PSO-based techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Data Dissemination in VANETs Using Particle Swarm Optimization

Desai

El-Ocla

Purohit

2023

Sensors

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A vehicular Ad-Hoc Network (VANET) is a type of Mobile Ad-Hoc Networks (MANETs) that uses wireless routers inside each vehicle to act as a node. The need for effective solutions to urban traffic congestion issues has increased recently due to the growing number of automobile connections in the car communications system. To ensure a high level of service and avoid unsafe situations brought on by congestion or a broadcast storm, data dissemination in a VANET network requires an effective approach. Effective multi-objective optimization methods are required to tackle this because of the implied competing nature of multi-metric approaches. A meta-heuristic technique with a high level of solution interactions can handle efficient optimization. To accomplish this, a meta-heuristic search algorithm particle optimization was chosen. In this paper, we have created a network consisting of vehicles as nodes. The aim is to send emergency messages immediately to the stationary nodes. The normal messages will be sent to the FIFO queue. To send these messages to a destination node, multiple routes were found using Time delay-based Multipath Routing (TMR) method, and to find the optimal and secure path Particle Swarm Optimization (PSO) is used. Our method is compared with different optimization methods such as Ant Colony Optimization (ACO), Firefly Optimization (FFO), and Enhanced Flying Ant Colony Optimization (EFACO). Significant improvements in terms of throughput and packet loss ratio, reduced end-to-end delay, rounding overhead ratio, and the energy consumption are revealed by the experimental results.

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“…The list of hyperparameters on CNN is in PSO is a general optimization method that belongs to the straightforward metaheuristic. It searches for the optimal solution in search space within the swarm [36]. PSO has several advantages: better convergence, high efficient computing, and low use of computing resources [37].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

PSO based Hyperparameter tuning of CNN Multivariate Time- Series Analysis

Utama

Wibawa

Muladi

et al. 2022

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Convolutional Neural Network (CNN) is an effective Deep Learning (DL) algorithm that solves various image identification problems. The use of CNN for time-series data analysis is emerging. CNN learns filters, representations of repeated patterns in the series, and uses them to forecast future values. The network performance may depend on hyperparameter settings. This study optimizes the CNN architecture based on hyperparameter tuning using Particle Swarm Optimization (PSO), PSO-CNN. The proposed method was evaluated using multivariate time-series data of electronic journal visitor datasets. The CNN equation in image and time-series problems is the input given to the model for processing numbers. The proposed method generated the lowest RMSE (1.386) with 178 neurons in the fully connected and 2 hidden layers. The experimental results show that the PSO-CNN generates an architecture with better performance than ordinary CNN.

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Easy Particle Swarm Optimization for Nonlinear Constrained Optimization Problems

Abstract: Hsuan-Yu Tseng and Pao-Hsien Chu made equal contributions and serve as co-first authors for this publication.

Cited by 13 publications

References 44 publications

An Improved Genetic Algorithm for Constrained Optimization Problems

An Improved Genetic Algorithm for Constrained Optimization Problems

Data Dissemination in VANETs Using Particle Swarm Optimization

PSO based Hyperparameter tuning of CNN Multivariate Time- Series Analysis

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