Ant Colony Optimization With Double Selections for Solving Integrated Scheduling Problem in Manufacturer

Abusini, Sobri; Sukmarini, Mita Akbar; Karim, Corina

doi:10.21776/ub.jemis.2019.007.01.4

Cited by 2 publications

(4 citation statements)

References 23 publications

(21 reference statements)

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“…The set of input and output data were collected from a previous study [21]. based on travelling salesman problem [13]. This new algorithm was stimulated from the characteristics of an ant to discover the shortest distance to food source.…”

Section: Experimental Data Collectionmentioning

confidence: 99%

“…The ant is chosen probabilistically from a solution to achieve, which is proportional to weight, to develop a new solution using Eq. (2) [13]:…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

“…Ant Colony Optimization (ACO) algorithm is one successful examples of SIA that has been developed by M. Dorigo [12]. The ACO algorithm is inspired by the behavior of an ant colony in finding the shortest path to food source [13]. Based on a previous study done by Abusini and his colleagues (2019), they implemented the ACO algorithm to solve problems faced by integrated scheduling production and distribution [13].…”

Section: Introductionmentioning

confidence: 99%

“…The ACO algorithm is inspired by the behavior of an ant colony in finding the shortest path to food source [13]. Based on a previous study done by Abusini and his colleagues (2019), they implemented the ACO algorithm to solve problems faced by integrated scheduling production and distribution [13]. Additionally, Zeron and his friends (2014) investigated the method to improve air bone pollution forecasting by using ACO [14].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Modelling of flexible beam based on ant colony optimization and cuckoo search algorithms

Ali¹,

Fadzilan²,

Shaari³

et al. 2021

J. vibroeng.

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Flexible beam structure is usually applied in various fields of engineering and industrial. There are few points of interest using flexible structure and one of the advantages is that its lighter compared to a rigid structure. Besides that, flexible beam also can save cost, reduce energy consumption, and improve operation safety. However, flexible beam structures are too sensitive and susceptible to with unwanted vibration that would cause damage or degradation to the structure system. Hence, to overcome the problem, appropriate modelling and controller for such systems should be developed. Currently, there are plenty of methods that have been developed by researchers to suppress undesired vibration. Based on previous studies, most researchers nowadays use system identification (SI) as a modelling technique to develop a dynamic model of flexible structure via swarm intelligence algorithm (SIA). Therefore, two type of algorithms was used in this work for modelling development of flexible beam structure, which are ant colony optimization (ACO) and cuckoo search algorithm (CSA). Based on the comparative results, CSA achieved the lowest mean square error (MSE) value of 6.1547×10 -9 meanwhile ACO recorded a MSE of 1.0728×10 -8 . Moreover, CSA was deduced to be the best model for flexible beam structure because it achieved 95 % confidence level in correlation test and has excellent stability in pole-zero diagram system. Thus, CSA is a suitable algorithm to represent the real behavior of flexible beam structure in a system.

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Section: Experimental Data Collectionmentioning

confidence: 99%

“…The ant is chosen probabilistically from a solution to achieve, which is proportional to weight, to develop a new solution using Eq. (2) [13]:…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modelling of flexible beam based on ant colony optimization and cuckoo search algorithms

Ali¹,

Fadzilan²,

Shaari³

et al. 2021

J. vibroeng.

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Hymenopteran Colony Stream Clustering Algorithm and Comparison with Particle Swarm Optimization and Genetic Optimization Clustering

Parafe

Venkatesan

Panner

2021

j comput theor nanosci

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Stream is endlessly inbound sequence of information, streamed information is unbounded and every information are often examined one time. Streamed information are often noisy and therefore the variety of clusters within the information and their applied mathematics properties will change over time, wherever random access to the information isn’t possible and storing all the arriving information is impractical. When applying data set processing techniques and specifically stream clustering Algorithms to real time information streams, limitation in execution time and memory have to be oblige to be thought-about carefully. The projected hymenopteran colony stream clustering Algorithmic is a clustering Algorithm which forms cluster according to density variation, in which clusters are separated by high density features from low density feature region with mounted movement of hymenopteran. Result shows that it created denser cluster than antecedently projected Algorithmic program. And with mounted movement of ants conjointly it decreases the loss of data points. And conjointly the changed radius formula of cluster is projected so as to increase performance of model to create it a lot of dynamic with continuous flow of information. And also we changed probability formula for pick up and drop to reduce oulier. Results from hymenopteran experiments conjointly showed that sorting is disbursed in 2 phases, a primary clustering episode followed by a spacing part. In this paper, we have also compared proposed Algorithm with particle swarm optimization and genetic optimization using DBSCAN and k -means clustering.

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Ant Colony Optimization With Double Selections for Solving Integrated Scheduling Problem in Manufacturer

Cited by 2 publications

References 23 publications

Modelling of flexible beam based on ant colony optimization and cuckoo search algorithms

Modelling of flexible beam based on ant colony optimization and cuckoo search algorithms

Hymenopteran Colony Stream Clustering Algorithm and Comparison with Particle Swarm Optimization and Genetic Optimization Clustering

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