Pub Date : 2020-03-20DOI: 10.1504/ijsi.2020.106406
M. Ariyaratne, T. Fernando, S. Weerakoon
The most acquainted methods to find root approximations of nonlinear equations and systems; numerical methods possess disadvantages such as necessity of acceptable initial guesses and the differentiability of the functions. Even having such qualities, for some univariate nonlinear equations and systems, approximations of roots is not possible with numerical methods. Research are geared towards finding alternate approaches, which are successful where numerical methods fail. One of the most disadvantageous properties in such approaches is inability of finding more than one approximation at a time. On the other hand these methods are incorporated with algorithm specific parameters which should be set properly in order to achieve good results. We present a modified firefly algorithm handling the problem as an optimisation problem, which is capable of giving multiple root approximations simultaneously within a reasonable state space while tuning the parameters of the proposed algorithm by itself, using a self-tuning framework. Differentiability and the continuity of the functions and the close initial guesses are needless to solve nonlinear systems using the proposed approach. Benchmark systems found in the literature were used to test the new algorithm. The root approximations and the tuned parameters obtained along with the statistical analysis illustrate the viability of the method.
{"title":"A self-tuning algorithm to approximate roots of systems of nonlinear equations based on the firefly algorithm","authors":"M. Ariyaratne, T. Fernando, S. Weerakoon","doi":"10.1504/ijsi.2020.106406","DOIUrl":"https://doi.org/10.1504/ijsi.2020.106406","url":null,"abstract":"The most acquainted methods to find root approximations of nonlinear equations and systems; numerical methods possess disadvantages such as necessity of acceptable initial guesses and the differentiability of the functions. Even having such qualities, for some univariate nonlinear equations and systems, approximations of roots is not possible with numerical methods. Research are geared towards finding alternate approaches, which are successful where numerical methods fail. One of the most disadvantageous properties in such approaches is inability of finding more than one approximation at a time. On the other hand these methods are incorporated with algorithm specific parameters which should be set properly in order to achieve good results. We present a modified firefly algorithm handling the problem as an optimisation problem, which is capable of giving multiple root approximations simultaneously within a reasonable state space while tuning the parameters of the proposed algorithm by itself, using a self-tuning framework. Differentiability and the continuity of the functions and the close initial guesses are needless to solve nonlinear systems using the proposed approach. Benchmark systems found in the literature were used to test the new algorithm. The root approximations and the tuned parameters obtained along with the statistical analysis illustrate the viability of the method.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"20 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2020-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77765854","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 : 2020-01-01DOI: 10.1504/ijsi.2020.10033809
Rajneesh Sharma, Ambreesh Kumar, P. Pandey, Ayush Singh, V. Upadhyaya
{"title":"Fractional order ant colony control with genetic algorithm assisted initialisation","authors":"Rajneesh Sharma, Ambreesh Kumar, P. Pandey, Ayush Singh, V. Upadhyaya","doi":"10.1504/ijsi.2020.10033809","DOIUrl":"https://doi.org/10.1504/ijsi.2020.10033809","url":null,"abstract":"","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"54 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78814172","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 : 2020-01-01DOI: 10.1504/ijsi.2020.10032977
M. Lalwani, N. K. Swarnkar, Rizwana Khokhar
{"title":"A novel control approach of DC motor drive with optimisation techniques","authors":"M. Lalwani, N. K. Swarnkar, Rizwana Khokhar","doi":"10.1504/ijsi.2020.10032977","DOIUrl":"https://doi.org/10.1504/ijsi.2020.10032977","url":null,"abstract":"","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"20 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73449208","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 : 2020-01-01DOI: 10.1504/ijsi.2020.10032976
P. Jain, Akash Saxena, Rajesh Kumar
{"title":"Application and development of improved meta-heuristic for making profitable bidding strategy in a day-ahead energy market under step-wise bidding scenario","authors":"P. Jain, Akash Saxena, Rajesh Kumar","doi":"10.1504/ijsi.2020.10032976","DOIUrl":"https://doi.org/10.1504/ijsi.2020.10032976","url":null,"abstract":"","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"15 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76962995","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 : 2020-01-01DOI: 10.1504/ijsi.2020.10032994
Vesna Šešum Čavić
{"title":"A survey of swarm-inspired metaheuristics in P2P systems: some theoretical considerations and hybrid forms","authors":"Vesna Šešum Čavić","doi":"10.1504/ijsi.2020.10032994","DOIUrl":"https://doi.org/10.1504/ijsi.2020.10032994","url":null,"abstract":"","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"16 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74494532","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 : 2019-12-12DOI: 10.1504/ijsi.2019.10025728
S. Sahu, S. Behera
This paper presents an improved technique to regulate the pitch angle of a wind turbine benchmark model (WTBM) implemented in MATLAB SIMULINK environment. As the model is nonlinear in nature, to accomplish the desired power production level in the constant power region, an adaptive controller is implemented. It takes care of the pitch control with online estimates of the plant parameters that are susceptible to change due to disturbances. Here, the controller design is based on the pole-placement methodology for a self-tuning controller (STC). Location of the desired pair of poles is defined by the damping factor and natural frequency. The selection of these parameters is performed by utilising particle swarm optimisation (PSO), constriction factor-based PSO (CFBPSO), genetic algorithm (GA), modified grey wolf optimisation (MGWO) and improved sine cosine algorithm (ISCA) and the results are put side by side for a consistent set of algorithm parameters. A Monte Carlo simulation has been carried out for comparison of the algorithms. The achieved results show the improvement in performance by employing ISCA for pole-placement of an adaptive STC controller.
{"title":"Improved pole-placement for adaptive pitch control","authors":"S. Sahu, S. Behera","doi":"10.1504/ijsi.2019.10025728","DOIUrl":"https://doi.org/10.1504/ijsi.2019.10025728","url":null,"abstract":"This paper presents an improved technique to regulate the pitch angle of a wind turbine benchmark model (WTBM) implemented in MATLAB SIMULINK environment. As the model is nonlinear in nature, to accomplish the desired power production level in the constant power region, an adaptive controller is implemented. It takes care of the pitch control with online estimates of the plant parameters that are susceptible to change due to disturbances. Here, the controller design is based on the pole-placement methodology for a self-tuning controller (STC). Location of the desired pair of poles is defined by the damping factor and natural frequency. The selection of these parameters is performed by utilising particle swarm optimisation (PSO), constriction factor-based PSO (CFBPSO), genetic algorithm (GA), modified grey wolf optimisation (MGWO) and improved sine cosine algorithm (ISCA) and the results are put side by side for a consistent set of algorithm parameters. A Monte Carlo simulation has been carried out for comparison of the algorithms. The achieved results show the improvement in performance by employing ISCA for pole-placement of an adaptive STC controller.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"24 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2019-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74580794","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.2019.10025726
S. Pattanayak, B. B. Choudhury
The latest moves in trajectory planning for autonomous mobile robots are directed towards a popular investigation work. This paper introduces modified particle swarm optimisation technique called as adaptive particle swarm optimisation (APSO) and particle swarm optimisation (PSO) for trajectory length optimisation. For estimating the trajectory length of the robot, nine numbers of obstacles is selected between start and goal point in a static environment. Lastly a comparison is established between these two approaches, to identify the approach that affords shortest trajectory length in a least computation time and shortest possible travel time. Simulation result shows that APSO contributes towards curtail trajectory length at a lesser computational and travel time as compared to PSO.
{"title":"Trajectory planning of an autonomous mobile robot","authors":"S. Pattanayak, B. B. Choudhury","doi":"10.1504/ijsi.2019.10025726","DOIUrl":"https://doi.org/10.1504/ijsi.2019.10025726","url":null,"abstract":"The latest moves in trajectory planning for autonomous mobile robots are directed towards a popular investigation work. This paper introduces modified particle swarm optimisation technique called as adaptive particle swarm optimisation (APSO) and particle swarm optimisation (PSO) for trajectory length optimisation. For estimating the trajectory length of the robot, nine numbers of obstacles is selected between start and goal point in a static environment. Lastly a comparison is established between these two approaches, to identify the approach that affords shortest trajectory length in a least computation time and shortest possible travel time. Simulation result shows that APSO contributes towards curtail trajectory length at a lesser computational and travel time as compared to PSO.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"72 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2019-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84181797","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.2019.10025729
K. Srikanth
Swarm intelligence has been one of the leading techniques used by researchers worldwide for optimisation. In this paper, the fine tuning of the update equations for the swarm are done based on linkage of particle motion with a electromagnetic field and also under the influence of strategic delays. The motion of a particle in a search space is confined to free space in general, however if restricted the solution under the envelope of a magnetic field, the algorithm better converges within a electromagnetic field. Simulation studies have been performed on the triple inverted pendulum case study which showed that stability was achieved with ease when compared to classical methods of control.
{"title":"Enhanced electromagnetic swarm yields better optimisation","authors":"K. Srikanth","doi":"10.1504/ijsi.2019.10025729","DOIUrl":"https://doi.org/10.1504/ijsi.2019.10025729","url":null,"abstract":"Swarm intelligence has been one of the leading techniques used by researchers worldwide for optimisation. In this paper, the fine tuning of the update equations for the swarm are done based on linkage of particle motion with a electromagnetic field and also under the influence of strategic delays. The motion of a particle in a search space is confined to free space in general, however if restricted the solution under the envelope of a magnetic field, the algorithm better converges within a electromagnetic field. Simulation studies have been performed on the triple inverted pendulum case study which showed that stability was achieved with ease when compared to classical methods of control.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"156 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2019-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73252301","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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