Pub Date : 2019-12-06DOI: 10.1504/ijsi.2019.10025730
Devendra Potnuru, A. S. Tummala
This paper presents a recently proposed grasshopper algorithm for speed control of BLDC motor drive in closed loop. The main objective of this paper is to obtain optimal PID gains of speed controller at different operating conditions. The efficient PID tuning is based on minimisation of integral square error which is the objective function of this optimisation problem. The PID controller is used for speed control of the BLDC motor drive. The drive has been simulated in MATLAB/Simulink environment and is tested at different reference speeds.
{"title":"Implementation of grasshopper optimisation algorithm for closed loop speed control a BLDC motor drive","authors":"Devendra Potnuru, A. S. Tummala","doi":"10.1504/ijsi.2019.10025730","DOIUrl":"https://doi.org/10.1504/ijsi.2019.10025730","url":null,"abstract":"This paper presents a recently proposed grasshopper algorithm for speed control of BLDC motor drive in closed loop. The main objective of this paper is to obtain optimal PID gains of speed controller at different operating conditions. The efficient PID tuning is based on minimisation of integral square error which is the objective function of this optimisation problem. The PID controller is used for speed control of the BLDC motor drive. The drive has been simulated in MATLAB/Simulink environment and is tested at different reference speeds.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"158 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2019-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73479858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-06DOI: 10.1504/ijsi.2019.10025735
Janmenjoy Nayak, Kanithi Vakula, P. Dinesh, B. Naik
Algorithm simulated by the social behaviour of understandable agents has become prominent amid the researchers in modern years. Researchers have advanced profuse algorithms by replicating the swarming behaviour of different creatures. Spider monkey optimisation (SMO) algorithm is a novel swarm intelligence based optimization which is a replica of spider monkey's foraging behaviour. Spider monkeys have been classified as animals with fusion-fission social structure, where they pursued to split themselves from huge to lesser hordes and vice-versa depends upon the accessibility of food. SMO and its variants have successful in dealing with difficult authentic world optimization problems due to its elevated effectiveness. This paper depicts a useful analysis of SMO, its variants, applications, advancements, usage levels and performance issues in various popular yet trending domains with a deep perspective. The key motto behind this analytical point of view is to inspire the practitioners and researchers to innovate new solutions.
{"title":"Spider monkey optimisation: state of the art and advances","authors":"Janmenjoy Nayak, Kanithi Vakula, P. Dinesh, B. Naik","doi":"10.1504/ijsi.2019.10025735","DOIUrl":"https://doi.org/10.1504/ijsi.2019.10025735","url":null,"abstract":"Algorithm simulated by the social behaviour of understandable agents has become prominent amid the researchers in modern years. Researchers have advanced profuse algorithms by replicating the swarming behaviour of different creatures. Spider monkey optimisation (SMO) algorithm is a novel swarm intelligence based optimization which is a replica of spider monkey's foraging behaviour. Spider monkeys have been classified as animals with fusion-fission social structure, where they pursued to split themselves from huge to lesser hordes and vice-versa depends upon the accessibility of food. SMO and its variants have successful in dealing with difficult authentic world optimization problems due to its elevated effectiveness. This paper depicts a useful analysis of SMO, its variants, applications, advancements, usage levels and performance issues in various popular yet trending domains with a deep perspective. The key motto behind this analytical point of view is to inspire the practitioners and researchers to innovate new solutions.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"9 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2019-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88194854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-06DOI: 10.1504/ijsi.2019.10025731
Tulasichandra Sekhar Gorripotu, R. Pilla
This manuscript presents a novel black hole optimised (BHO) proportional derivative-proportional integral derivative controller (PD-PID) is provided for the optimal solution of the frequency regulation of hybrid power system. At first, a two area power system is considered in which area-1 having thermal, distributed units and in area-2 includes thermal, hydel and nuclear units. Appropriate nonlinearities such boiler dynamics, governor dead band (GDB) and generation rate constraint (GRC) are considered. In the next step, PD-PID controller is considered as a secondary controller and its preeminence is shown by comparing with proportional integral derivate (PID) and proportional integral double derivate (PIDD) controllers for the same model having integral time multiplied absolute error (ITAE) as an error function. Finally, sensitivity of the proposed controller is investigated over a wide variation of system parameters and loading condition. For more examination of the proposed controller is also analysed under random step load and sinusoidal disturbances.
{"title":"Black hole optimised cascade proportional derivative-proportional integral derivative controller for frequency regulation in hybrid distributed power system","authors":"Tulasichandra Sekhar Gorripotu, R. Pilla","doi":"10.1504/ijsi.2019.10025731","DOIUrl":"https://doi.org/10.1504/ijsi.2019.10025731","url":null,"abstract":"This manuscript presents a novel black hole optimised (BHO) proportional derivative-proportional integral derivative controller (PD-PID) is provided for the optimal solution of the frequency regulation of hybrid power system. At first, a two area power system is considered in which area-1 having thermal, distributed units and in area-2 includes thermal, hydel and nuclear units. Appropriate nonlinearities such boiler dynamics, governor dead band (GDB) and generation rate constraint (GRC) are considered. In the next step, PD-PID controller is considered as a secondary controller and its preeminence is shown by comparing with proportional integral derivate (PID) and proportional integral double derivate (PIDD) controllers for the same model having integral time multiplied absolute error (ITAE) as an error function. Finally, sensitivity of the proposed controller is investigated over a wide variation of system parameters and loading condition. For more examination of the proposed controller is also analysed under random step load and sinusoidal disturbances.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"33 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2019-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88539807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-23DOI: 10.1504/IJSI.2019.10018583
Zahid Hussain Wani, S. Quadri
Software cost estimation is the forecast of development effort and time needed to develop a software project. Estimating software cost is endlessly proving to be a difficult problem and thus catches the attention of many researchers. Recently, the usage of meta-heuristic techniques for software cost estimation is increasingly growing. In this paper, we are proposing a technique consisting of functional link artificial neural network model and particle swarm optimisation algorithm as its training algorithm. Functional link artificial neural network is a high order feedforward artificial neural network consisting of an input layer and an output layer. It reduces the computational complexity and has got the fast learning ability. Particle swarm optimisation does optimisation by iteratively improving a candidate solution. The proposed model has been evaluated on promising datasets using magnitude of relative error and its median as a measure of performance index to simply weigh the obtained quality of estimation.
{"title":"An improved particle swarm optimisation-based functional link artificial neural network model for software cost estimation","authors":"Zahid Hussain Wani, S. Quadri","doi":"10.1504/IJSI.2019.10018583","DOIUrl":"https://doi.org/10.1504/IJSI.2019.10018583","url":null,"abstract":"Software cost estimation is the forecast of development effort and time needed to develop a software project. Estimating software cost is endlessly proving to be a difficult problem and thus catches the attention of many researchers. Recently, the usage of meta-heuristic techniques for software cost estimation is increasingly growing. In this paper, we are proposing a technique consisting of functional link artificial neural network model and particle swarm optimisation algorithm as its training algorithm. Functional link artificial neural network is a high order feedforward artificial neural network consisting of an input layer and an output layer. It reduces the computational complexity and has got the fast learning ability. Particle swarm optimisation does optimisation by iteratively improving a candidate solution. The proposed model has been evaluated on promising datasets using magnitude of relative error and its median as a measure of performance index to simply weigh the obtained quality of estimation.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"12 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2019-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76246909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-23DOI: 10.1504/IJSI.2019.10018596
P. Swathypriyadharsini, K. Premalatha
The nature inspired meta-heuristic algorithms have ubiquitous nature in nearly every aspect, where computational intelligence is applied. This paper focuses on the comparative study of two commonly used robust bio inspired optimisation algorithms namely cuckoo search and particle swarm optimisation for triclustering the microarray gene expression data. Triclustering broadens the clustering technique by extracting the subset of genes that are highly co-expressed over a subset of conditions and across a subset of time points. Both the algorithms are applied to three real life three dimensional datasets. The performances of the algorithms are compared using the mean square residue as a fitness function and it is also compared with other triclustering algorithms. The experiment results prove that cuckoo search algorithm has better computational efficiency than particle swarm optimisation algorithm.
{"title":"Comparison of cuckoo search and particle swarm optimisation in triclustering temporal gene expression data","authors":"P. Swathypriyadharsini, K. Premalatha","doi":"10.1504/IJSI.2019.10018596","DOIUrl":"https://doi.org/10.1504/IJSI.2019.10018596","url":null,"abstract":"The nature inspired meta-heuristic algorithms have ubiquitous nature in nearly every aspect, where computational intelligence is applied. This paper focuses on the comparative study of two commonly used robust bio inspired optimisation algorithms namely cuckoo search and particle swarm optimisation for triclustering the microarray gene expression data. Triclustering broadens the clustering technique by extracting the subset of genes that are highly co-expressed over a subset of conditions and across a subset of time points. Both the algorithms are applied to three real life three dimensional datasets. The performances of the algorithms are compared using the mean square residue as a fitness function and it is also compared with other triclustering algorithms. The experiment results prove that cuckoo search algorithm has better computational efficiency than particle swarm optimisation algorithm.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"85 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2019-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73974054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-23DOI: 10.1504/IJSI.2019.10018603
A. Khandelwal, A. Bhargava, Ajay Sharma, Harish Sharma
Transmission network expansion planning (TNEP) problem has been continuously solved for many years still the cost effective, reliable, and optimise solution is always desirable. The TNEP has been solved by various conventional and non conventional strategies. The strategy to find the solution of TNEP by classical mathematical optimisation techniques is tedious, slow and inefficient. In recent years, nature inspired algorithms (NIAs) have proven their importance to provide the solutions of the TNEP problem over classical mathematical optimisation techniques. This paper presents a review on the key contributions of the state-of-art NIAs to solve the TNEP problem. Further, the TNEP system specific significant works presented in the literature are summarised for easy understanding of the readers. The readers can get a brief description of the considered NIAs algorithms which has been applied to solve various systems of TNEP problem and they can also identify the significant NIA which is being applied for specific TNEP system.
{"title":"Transmission network expansion planning using state-of-art nature inspired algorithms: a survey","authors":"A. Khandelwal, A. Bhargava, Ajay Sharma, Harish Sharma","doi":"10.1504/IJSI.2019.10018603","DOIUrl":"https://doi.org/10.1504/IJSI.2019.10018603","url":null,"abstract":"Transmission network expansion planning (TNEP) problem has been continuously solved for many years still the cost effective, reliable, and optimise solution is always desirable. The TNEP has been solved by various conventional and non conventional strategies. The strategy to find the solution of TNEP by classical mathematical optimisation techniques is tedious, slow and inefficient. In recent years, nature inspired algorithms (NIAs) have proven their importance to provide the solutions of the TNEP problem over classical mathematical optimisation techniques. This paper presents a review on the key contributions of the state-of-art NIAs to solve the TNEP problem. Further, the TNEP system specific significant works presented in the literature are summarised for easy understanding of the readers. The readers can get a brief description of the considered NIAs algorithms which has been applied to solve various systems of TNEP problem and they can also identify the significant NIA which is being applied for specific TNEP system.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"41 1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2019-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82849501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-23DOI: 10.1504/IJSI.2019.10018580
B. Bepari, Ankit Ati
The present investigation includes optimisation for enhanced surface quality during finishing of Calmax-635 die steel through GRA coupled with PSO. GRA converts multiple objectives into single objective domain. However, it yields discrete parametric combination within the problem space and fetches quasi-optimal solution. Whereas, PSO obtains optimal solution if the fitness function is available. To obtain the fitness function for Calmax-635 die steel, a full factorial DOE was conducted for parameters like, spindle speed, feed rate and depth of cut all at three levels. With the help of ANOVA, a fitness function was obtained within the problem space. Thus, when PSO was coupled with GRA, the optimised process parameters became 5,660.6 rpm, 579.4 mm/min, 0.105 mm, respectively and the roughness values obtained were 0.862 µm, 6.591 µm and 4.638 µm for Ra, Rmax and Rz, respectively. Therefore, the proposed methodology reveals an avenue for optimisation in absentia of fitness function.
目前的研究包括通过GRA和PSO对Calmax-635模具钢精加工过程中的表面质量进行优化。GRA将多目标转化为单目标域。然而，它在问题空间内产生离散的参数组合，并获得拟最优解。而当适应度函数可用时，粒子群算法得到最优解。为了得到Calmax-635模具钢的适应度函数，对主轴转速、进给速度和切削深度等参数进行了三层次的全因子DOE分析。通过方差分析，得到了问题空间内的适应度函数。因此，当PSO与GRA耦合时，优化后的工艺参数分别为5660.6 rpm、579.4 mm/min和0.105 mm, Ra、Rmax和Rz的粗糙度值分别为0.862µm、6.591µm和4.638µm。因此，所提出的方法揭示了在缺乏适应度函数的情况下优化的途径。
{"title":"Concurrent parametric optimisation of single pass end milling through GRA coupled with PSO for Calmax-635 die steel","authors":"B. Bepari, Ankit Ati","doi":"10.1504/IJSI.2019.10018580","DOIUrl":"https://doi.org/10.1504/IJSI.2019.10018580","url":null,"abstract":"The present investigation includes optimisation for enhanced surface quality during finishing of Calmax-635 die steel through GRA coupled with PSO. GRA converts multiple objectives into single objective domain. However, it yields discrete parametric combination within the problem space and fetches quasi-optimal solution. Whereas, PSO obtains optimal solution if the fitness function is available. To obtain the fitness function for Calmax-635 die steel, a full factorial DOE was conducted for parameters like, spindle speed, feed rate and depth of cut all at three levels. With the help of ANOVA, a fitness function was obtained within the problem space. Thus, when PSO was coupled with GRA, the optimised process parameters became 5,660.6 rpm, 579.4 mm/min, 0.105 mm, respectively and the roughness values obtained were 0.862 µm, 6.591 µm and 4.638 µm for Ra, Rmax and Rz, respectively. Therefore, the proposed methodology reveals an avenue for optimisation in absentia of fitness function.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"57 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2019-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91048698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-23DOI: 10.1504/IJSI.2019.10018582
V. Anireh, E. N. Osegi
In this paper, we present an open-source software tool 'ABC-PLOSS', which is developed for use in optimisation processes. Path-loss optimisation deals with searching for the best set of operator-specific parameters in telecommunication that gives the least cost of operation. It is a primary issue that challenges mobile communication operators, particularly the global system mobile (GSM) operators in tuning mobile-base station networks for efficient and reliable operation. The tool uses a sequential processor architecture based on a swarm intelligence algorithm called artificial bee colony (ABC) and the cost-231 Hata path-loss model as cost function for path-loss minimisation (PLM). Using the ABC-PLOSS framework, the ABC algorithm is compared with two other existing and popular artificial intelligent (AI) algorithms called the genetic algorithm (GA) and particle swarm optimisation (PSO). Results of simulation studies show that this tool is indeed useful as it gives a competitive or lower path-loss estimate when compared with conventional techniques. It also shows that it is possible for the ABC to attain an estimated seven-fold and two-fold path-loss improvement over the GA and the PSO techniques respectively.
{"title":"ABC-PLOSS: a software tool for path-loss minimisation in GSM telecom networks using artificial bee colony algorithm","authors":"V. Anireh, E. N. Osegi","doi":"10.1504/IJSI.2019.10018582","DOIUrl":"https://doi.org/10.1504/IJSI.2019.10018582","url":null,"abstract":"In this paper, we present an open-source software tool 'ABC-PLOSS', which is developed for use in optimisation processes. Path-loss optimisation deals with searching for the best set of operator-specific parameters in telecommunication that gives the least cost of operation. It is a primary issue that challenges mobile communication operators, particularly the global system mobile (GSM) operators in tuning mobile-base station networks for efficient and reliable operation. The tool uses a sequential processor architecture based on a swarm intelligence algorithm called artificial bee colony (ABC) and the cost-231 Hata path-loss model as cost function for path-loss minimisation (PLM). Using the ABC-PLOSS framework, the ABC algorithm is compared with two other existing and popular artificial intelligent (AI) algorithms called the genetic algorithm (GA) and particle swarm optimisation (PSO). Results of simulation studies show that this tool is indeed useful as it gives a competitive or lower path-loss estimate when compared with conventional techniques. It also shows that it is possible for the ABC to attain an estimated seven-fold and two-fold path-loss improvement over the GA and the PSO techniques respectively.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"21 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2019-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89076280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
George M. Cavalcanti-Júnior, Fernando Buarque de Lima-Neto, C. Bastos-Filho
Swarm Intelligence algorithms have been extensively applied to solve optimization problems. However, in some domains even well-established techniques such as Particle Swarm Optimization (PSO) may not present the necessary ability to generate diversity during the process of the swarm convergence. Indeed, this is the major difficulty to use PSO to tackle dynamic problems. Many efforts to overcome this weakness have been made. One of them is through the hybridization of the PSO with other algorithms. For example, the Volitive PSO is a hybrid algorithm that presents as good performance on dynamic problems by applying a very interesting feature, the collective volitive operator, which was extracted from the Fish School Search algorithm and embedded into PSO. In this paper, the authors investigated further hybridizations in line with the Volitive PSO approach. This time they used the Heterogeneous PSO instead of the PSO, and named this novel approach Volitive HPSO. In the paper, the authors investigate the influence of the collective volitive operator (of FSS) in the HPSO. The results show that this operator significantly improves HPSO performance when compared to the non-hybrid approaches of PSO and its variations in dynamic environments.
{"title":"On the Analysis of HPSO Improvement by Use of the Volitive Operator of Fish School Search","authors":"George M. Cavalcanti-Júnior, Fernando Buarque de Lima-Neto, C. Bastos-Filho","doi":"10.4018/JSIR.2013010103","DOIUrl":"https://doi.org/10.4018/JSIR.2013010103","url":null,"abstract":"Swarm Intelligence algorithms have been extensively applied to solve optimization problems. However, in some domains even well-established techniques such as Particle Swarm Optimization (PSO) may not present the necessary ability to generate diversity during the process of the swarm convergence. Indeed, this is the major difficulty to use PSO to tackle dynamic problems. Many efforts to overcome this weakness have been made. One of them is through the hybridization of the PSO with other algorithms. For example, the Volitive PSO is a hybrid algorithm that presents as good performance on dynamic problems by applying a very interesting feature, the collective volitive operator, which was extracted from the Fish School Search algorithm and embedded into PSO. In this paper, the authors investigated further hybridizations in line with the Volitive PSO approach. This time they used the Heterogeneous PSO instead of the PSO, and named this novel approach Volitive HPSO. In the paper, the authors investigate the influence of the collective volitive operator (of FSS) in the HPSO. The results show that this operator significantly improves HPSO performance when compared to the non-hybrid approaches of PSO and its variations in dynamic environments.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"4 1","pages":"62-77"},"PeriodicalIF":1.1,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4018/JSIR.2013010103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70506185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/ijsir.2021040103
Shadab Irfan, Rajesh Kumar Dhanaraj
There is an incredible change in the world wide web, and the users face difficulty in accessing the needed information as per their need. Different algorithms are devised at each step of the information retrieval process, and it is observed that ranking is one of the core ingredients of any search engine that plays a major role in arranging the information. In this regard, different measures are adopted for ranking the web pages by using content, structure, or log data. The BeeRank algorithm is proposed that provides quality results, which is inspired by the artificial bee colony algorithm for web page ranking and uses both the structural and content approach for calculating the rank value and provides better results. It also helps the users in finding the relevant web pages by minimizing the computational complexity of the process and achieves the result in minimum time duration. The working is illustrated and is compared with the traditional PageRank algorithm that incorporates only structural links, and the result shows an improvement in ranking and provides user-specific results.
{"title":"BeeRank","authors":"Shadab Irfan, Rajesh Kumar Dhanaraj","doi":"10.4018/ijsir.2021040103","DOIUrl":"https://doi.org/10.4018/ijsir.2021040103","url":null,"abstract":"There is an incredible change in the world wide web, and the users face difficulty in accessing the needed information as per their need. Different algorithms are devised at each step of the information retrieval process, and it is observed that ranking is one of the core ingredients of any search engine that plays a major role in arranging the information. In this regard, different measures are adopted for ranking the web pages by using content, structure, or log data. The BeeRank algorithm is proposed that provides quality results, which is inspired by the artificial bee colony algorithm for web page ranking and uses both the structural and content approach for calculating the rank value and provides better results. It also helps the users in finding the relevant web pages by minimizing the computational complexity of the process and achieves the result in minimum time duration. The working is illustrated and is compared with the traditional PageRank algorithm that incorporates only structural links, and the result shows an improvement in ranking and provides user-specific results.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70470738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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