Pigeon optimization algorithm: A novel approach for solving optimization problems

Goel, Shruti

doi:10.1109/icdmic.2014.6954259

Cited by 20 publications

(11 citation statements)

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“…Here, evaluation was done using the responses collected under different factors including lean awareness, employee involvement, management commitment, lean technology, OP and OS. Further, the betterment of the proposed HPS-PO model was compared over other traditional optimization models like Backtracking Search Algorithm (BSA; Hassan & Rashid, 2020), PSO (Jordon, et al, 2019) and POA (Goel, 2014). Moreover, the presented scheme is validated over the existing classifiers such as Bayesian Network (BN; Bos, et al, 2020), RF (Li, et al, 2020) and SVM (Gu, et al, 2019) for each considered factors.…”

Section: Methodsmentioning

confidence: 99%

“…Even though, the existing PSO model (Jordon, et al, 2019) results with precise estimations; it also involves few drawbacks like"slow convergence and reduced internal memory". Therefore, to prevail over the drawbacks of existing PSO, the concept of POA (Goel, 2014) is merged with it to introduce a new model termed as HPS-PO scheme. Hybrid optimization algorithms have been reported to be promising for certain search problems (Beno, et al, 2014).…”

Section: Proposed Hps-po Algorithmmentioning

confidence: 99%

“…Thus, the enhanced performance of the adopted model is validated in terms of error analysis. (Jordon, et al, 2019) 2.0234 0.039413 191.02 PSO (Jordon, et al, 2019) 1.1301 0.02811 279.25 POA (Goel, 2014) algorithm. Finally, a precise analysis was made for validating the enhancement of presented model over traditional schemes.…”

Section: Error Analysismentioning

confidence: 99%

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Intelligent Prediction Model: Optimized Neural Network for Lean Manufacturing Technology

Jobin

2021

JER is an international, peer-reviewed journal that publishes f

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Lean manufacturing (LM) is a method, which focuses on reducing wastes and increasing the productivity within manufacturing firms. Several analyses were performed on LM technology depending on minimal lead times, enhanced quality and reduced operating costs. However, limitation exists in understanding its role to develop managing commitment, worker involvement and in turn its organizational performance. This paper intends to propose a new Neural Network (NN) based intelligent prediction framework. The initial process is manual labeling or response validation, which is carried out by utilizing the responses attained for each questions under each factors including lean awareness, employee involvement, management commitment, lean technology, Organizational Performance (OP) and Organizational Support (OS). Subsequently, NN is exploited for prediction process, where the features (received responses) are given as input and the labeling values attained are set as target. Further, in order to improve the prediction performance, the NN training is performed by a new Hybrid Particle Swarm and Pigeon Optimization (HPS-PO) algorithm via tuning the optimal weights. In fact, the proposed algorithm is the combination of Particle Swarm Optimization (PSO) and Pigeon Optimization Algorithm (POA), respectively. Finally, the performance of the proposed model is examined over conventional methods in terms of prediction analysis and error analysis.

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Section: Methodsmentioning

confidence: 99%

Section: Proposed Hps-po Algorithmmentioning

confidence: 99%

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Intelligent Prediction Model: Optimized Neural Network for Lean Manufacturing Technology

Jobin

2021

JER is an international, peer-reviewed journal that publishes f

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“…In recent years, with the unprecedented development of bionic optimization, a series of novel algorithms have emerged [9][10][11][12][13][14][15][16][17]. These include the teaching and learning optimization algorithm (TLBO, 2011) and its variants, the grey wolf optimizer (GWO, 2014) and its variants, the pigeon swarm algorithm (PSA, 2014), the whale optimization algorithm (WOA, 2016) and the bird swarm algorithm (BSA, 2016).…”

Section: Introductionmentioning

confidence: 99%

On-Off Control of Range Extender in Extended-Range Electric Vehicle using Bird Swarm Intelligence

Feng

2019

Electronics

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The bird swarm algorithm (BSA) is a bio-inspired evolution approach to solving optimization problems. It is derived from the foraging, defense, and flying behavior of bird swarm. This paper proposed a novel version of BSA, named as BSAII. In this version, the spatial distance from the center of the bird swarm instead of fitness function value is used to stand for their intimacy of relationship. We examined the performance of two different representations of defense behavior for BSA algorithms, and compared their experimental results with those of other bio-inspired algorithms. It is evident from the statistical and graphical results highlighted that the BSAII outperforms other algorithms on most of instances, in terms of convergence rate and accuracy of optimal solution. Besides the BSAII was applied to the energy management of extended-range electric vehicles (E-REV). The problem is modified as a constrained global optimal control problem, so as to reduce engine burden and exhaust emissions. According to the experimental results of two cases for the new European driving cycle (NEDC), it is found that turning off the engine ahead of time can effectively reduce its uptime on the premise of completing target distance. It also indicates that the BSAII is suitable for solving such constrained optimization problem.

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“…Fast and provide the better performance High waiting delay. Pigeon Optimization Based Load Balance [19] The algorithm based on the behaviour of pigeon. The vision radius of pigeon is used to find the best VM..…”

Section: Related Work Of Load Balancing Techniquesmentioning

confidence: 99%

Review of Load Balancing is Distributed Computing and Bat Algorithm

Rani¹,

Kaur²

2017

IJARCSSE

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management of the resources in the various workloads is the key factor of the performance of the distributed computing infrastructure such as (cloud computing, cluster computing and grid computing). Maximizing the resource utilization and enhance the overall performance of the distributed computing infrastructure distribute the workload among the available resource. We can say that the performance is also dependent on the policy of load balancing if load balancing policy is good its leads to better performance and maximize the resource utilization. Grid computing is collection of heterogeneous system from the multiple administrative domains which are geographically distributed to preform parallel processing on demand and provide single coherent view to the user. It suffers from three main challenges scalability, adaptability and heterogeneity. In this paper we provides a detailed study of various load balancing techniques used in the distributed computing which provides you better understanding of various load balancing algorithm with the distributed computing infrastructure.Keyword: Load balancing, grid computing, cluster computing, cloud computing, distributed computing. I. INTRODUCTIONDistributed computing is the most favourable area computer science in which the resources are physically distributed and connected via network and perform communication the help of message passing techniques to perform a common goal the user is always unknown about that processing is going on parallel multiple system he always think the process is going on single system because distributed computing provide single coherent view to the user. Applications of the distributed computing are (cloud computing, grid computing, peer to peer and cluster computing).[1] Distributed computing always able to run those applications which is especially written for run in distributed environment we cannot run all the application on distributed infrastructure and message passing is performed for communication with the help of various methods such as RPC (Remote Procedure Call), HTTP and message ques.[2] Challenges in the distributed computing is the maintain various kind of transparency such as location transparency, migration transparency and relocation transparency.Cloud computing is mechanism of providing on demand services for the various purpose such as (storage, processing and infrastructure). It is an application of the distributed computing it is used internet based communication system means user can demand for the service by using internet and cloud computing provides the resource on demand as per the need of the user. Cloud computing is the pool of the resources where all users are sharing the resources transparently they are not aware of that any other user is also using the same resource each user things the resource used by them is only dedicated to them this is due to transparency maintain by the cloud service provider with the help of virtualization and they also offer virtualization on hardware level not only applicatio...

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Pigeon optimization algorithm: A novel approach for solving optimization problems

Cited by 20 publications

References 1 publication

Intelligent Prediction Model: Optimized Neural Network for Lean Manufacturing Technology

Intelligent Prediction Model: Optimized Neural Network for Lean Manufacturing Technology

On-Off Control of Range Extender in Extended-Range Electric Vehicle using Bird Swarm Intelligence

Review of Load Balancing is Distributed Computing and Bat Algorithm

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