Pub Date : 2018-01-01DOI: 10.1504/ijbic.2018.10014476
Parul Agarwal, S. Mehta
{"title":"ABC_DE_FP: a novel hybrid algorithm for complex continuous optimisation problems.","authors":"Parul Agarwal, S. Mehta","doi":"10.1504/ijbic.2018.10014476","DOIUrl":"https://doi.org/10.1504/ijbic.2018.10014476","url":null,"abstract":"","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"33 1","pages":"46-61"},"PeriodicalIF":3.5,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89727826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-23DOI: 10.1504/IJBIC.2017.085894
Héctor D. Menéndez, F. E. B. Otero, David Camacho
The growing amount of data demands new analytical methodologies to extract relevant knowledge. Clustering is one of the most competitive techniques in this context. Using a dataset as a starting point, clustering techniques blindly group the data by similarity. Among the different areas, manifold identification is currently gaining importance. Spectral-based methods, which are one of the main used methodologies, are sensitive to metric parameters and noise. In order to solve these problems, new bio-inspired techniques have been combined with different heuristics to perform the cluster selection, in particular for dense datasets, featured by areas of higher density. This paper extends a previous algorithm named spectral-based ant colony optimisation clustering (SACOC), used for manifold identification. We focus on improving it through the Nystrom extension for dealing with dense data problems. We evaluated the new approach, called SACON, comparing it against online clustering algorithms and the Nystrom extension of spectral clustering.
{"title":"Extending the SACOC algorithm through the Nystrom method for Dense Manifold Data Analysis","authors":"Héctor D. Menéndez, F. E. B. Otero, David Camacho","doi":"10.1504/IJBIC.2017.085894","DOIUrl":"https://doi.org/10.1504/IJBIC.2017.085894","url":null,"abstract":"The growing amount of data demands new analytical methodologies to extract relevant knowledge. Clustering is one of the most competitive techniques in this context. Using a dataset as a starting point, clustering techniques blindly group the data by similarity. Among the different areas, manifold identification is currently gaining importance. Spectral-based methods, which are one of the main used methodologies, are sensitive to metric parameters and noise. In order to solve these problems, new bio-inspired techniques have been combined with different heuristics to perform the cluster selection, in particular for dense datasets, featured by areas of higher density. This paper extends a previous algorithm named spectral-based ant colony optimisation clustering (SACOC), used for manifold identification. We focus on improving it through the Nystrom extension for dealing with dense data problems. We evaluated the new approach, called SACON, comparing it against online clustering algorithms and the Nystrom extension of spectral clustering.","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"33 1","pages":"127-135"},"PeriodicalIF":3.5,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83588686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-01-01DOI: 10.1504/IJBIC.2016.10004315
Lydia Boudjeloud-Assala, Ta Minh Thuy
The k-means algorithm is a popular clustering algorithm. However, while k-means is convenient to implement, it produces solutions that are locally optimal. It depends on the number of clusters k and initialisation seeds. We introduce a method that can be used directly as a clustering algorithm or as an initialisation of the k-means algorithm based on the cluster number optimisation. The problem is the number of parameters required to find an optimal solution. We propose to apply diversity of population maintained through different evolutionary sub-populations and to apply the elitist strategy to select only the best concurrent solution. We also propose a new mutation strategy according to the neighbourhood search. This cooperative strategy allows us to find the global optimal solution for clustering tasks and optimal cluster seeds. We conduct numerical experiments to evaluate the effectiveness of the proposed algorithms on multi-class datasets, overlapped datasets and large-size datasets.
{"title":"A clustering algorithm based on elitist evolutionary approach","authors":"Lydia Boudjeloud-Assala, Ta Minh Thuy","doi":"10.1504/IJBIC.2016.10004315","DOIUrl":"https://doi.org/10.1504/IJBIC.2016.10004315","url":null,"abstract":"The k-means algorithm is a popular clustering algorithm. However, while k-means is convenient to implement, it produces solutions that are locally optimal. It depends on the number of clusters k and initialisation seeds. We introduce a method that can be used directly as a clustering algorithm or as an initialisation of the k-means algorithm based on the cluster number optimisation. The problem is the number of parameters required to find an optimal solution. We propose to apply diversity of population maintained through different evolutionary sub-populations and to apply the elitist strategy to select only the best concurrent solution. We also propose a new mutation strategy according to the neighbourhood search. This cooperative strategy allows us to find the global optimal solution for clustering tasks and optimal cluster seeds. We conduct numerical experiments to evaluate the effectiveness of the proposed algorithms on multi-class datasets, overlapped datasets and large-size datasets.","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"42 1","pages":"258-266"},"PeriodicalIF":3.5,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78414614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-01-01DOI: 10.1504/IJBIC.2016.10004295
S. Salkuti
{"title":"Optimal Power Flow using Clustered Adaptive Teaching Learning Based Optimization","authors":"S. Salkuti","doi":"10.1504/IJBIC.2016.10004295","DOIUrl":"https://doi.org/10.1504/IJBIC.2016.10004295","url":null,"abstract":"","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"164 9","pages":"1"},"PeriodicalIF":3.5,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72588692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-01-01DOI: 10.1504/IJBIC.2016.10004308
S. Salkuti
{"title":"Application of Swarm Intelligent Techniques with Mixed Variables to Solve Optimal Power Flow Problems","authors":"S. Salkuti","doi":"10.1504/IJBIC.2016.10004308","DOIUrl":"https://doi.org/10.1504/IJBIC.2016.10004308","url":null,"abstract":"","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"74 1","pages":"1"},"PeriodicalIF":3.5,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74665267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-01-01DOI: 10.1504/IJBIC.2016.10004342
Soheila Sadeghiram
Nature inspired meta-heuristic algorithms have been widely used in order to find efficient solutions for optimisation problems, and granted results have been achieved. Particle swarm optimisation (PSO) algorithm is one of the most utilised algorithms in recent years, which has indicated acceptable efficiency. On the other hand, bacterial foraging optimisation algorithm (BFOA) is relatively new compared to other meta-heuristic algorithms, and like PSO has shown a good ability to solve different optimisation problems. Genetic algorithms (GAs) are a well-known group of meta-heuristic algorithms which have been in use earlier than the other in various research fields. In this paper, we compare the efficiency of BFOA and PSO algorithms in an identical condition by minimising different test functions (from two to 20 dimensional). In this experiment, GA is used as a basic method in comparing the two algorithms. The methodology and results are presented. Although results verify the accurate convergency of both algorithms, the efficiency of BFOA on high-dimensional functions is dramatically better than that of PSO.
{"title":"Bacterial foraging optimisation algorithm, particle swarm optimisation and genetic algorithm: a comparative study","authors":"Soheila Sadeghiram","doi":"10.1504/IJBIC.2016.10004342","DOIUrl":"https://doi.org/10.1504/IJBIC.2016.10004342","url":null,"abstract":"Nature inspired meta-heuristic algorithms have been widely used in order to find efficient solutions for optimisation problems, and granted results have been achieved. Particle swarm optimisation (PSO) algorithm is one of the most utilised algorithms in recent years, which has indicated acceptable efficiency. On the other hand, bacterial foraging optimisation algorithm (BFOA) is relatively new compared to other meta-heuristic algorithms, and like PSO has shown a good ability to solve different optimisation problems. Genetic algorithms (GAs) are a well-known group of meta-heuristic algorithms which have been in use earlier than the other in various research fields. In this paper, we compare the efficiency of BFOA and PSO algorithms in an identical condition by minimising different test functions (from two to 20 dimensional). In this experiment, GA is used as a basic method in comparing the two algorithms. The methodology and results are presented. Although results verify the accurate convergency of both algorithms, the efficiency of BFOA on high-dimensional functions is dramatically better than that of PSO.","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"53 1","pages":"275"},"PeriodicalIF":3.5,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79238412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-01-01DOI: 10.1504/IJBIC.2012.044931
Lydia Boudjeloud-Assala
Usual visualisation techniques for multidimensional datasets, such as parallel coordinates and scatterplot matrices, do not scale well to high numbers of dimensions. A common approach to solve this problem is dimensionality selection. Existing dimensionality selection techniques usually select pertinent dimension subsets that are significant to the user without loose of information. We present concrete cooperation between automatic algorithms, interactive algorithms and visualisation tools: the evolutionary algorithm is used to obtain optimal dimension subsets which represent the original dataset without loosing information for unsupervised mode (clustering or outlier detection). The last effective cooperation is a visualisation tool used to present the user interactive evolutionary algorithm results and let him actively participate in evolutionary algorithm searching with more efficiency resulting in a faster evolutionary algorithm convergence. We have implemented our approach and applied it to real dataset to confirm this approach is effective for supporting the user in the exploration of high dimensional datasets.
{"title":"Visual interactive evolutionary algorithm for high dimensional outlier detection and data clustering problems","authors":"Lydia Boudjeloud-Assala","doi":"10.1504/IJBIC.2012.044931","DOIUrl":"https://doi.org/10.1504/IJBIC.2012.044931","url":null,"abstract":"Usual visualisation techniques for multidimensional datasets, such as parallel coordinates and scatterplot matrices, do not scale well to high numbers of dimensions. A common approach to solve this problem is dimensionality selection. Existing dimensionality selection techniques usually select pertinent dimension subsets that are significant to the user without loose of information. We present concrete cooperation between automatic algorithms, interactive algorithms and visualisation tools: the evolutionary algorithm is used to obtain optimal dimension subsets which represent the original dataset without loosing information for unsupervised mode (clustering or outlier detection). The last effective cooperation is a visualisation tool used to present the user interactive evolutionary algorithm results and let him actively participate in evolutionary algorithm searching with more efficiency resulting in a faster evolutionary algorithm convergence. We have implemented our approach and applied it to real dataset to confirm this approach is effective for supporting the user in the exploration of high dimensional datasets.","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"26 1","pages":"6-13"},"PeriodicalIF":3.5,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84705748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-10-01DOI: 10.1504/IJBIC.2010.036162
M. Aicha, Bessedik Malika, D. Habiba
GCP is a well-known combinatorial problem that admits several generalisations from which the T-colouring (GTCP). Given a graph G and sets T of positive integers associated to the edges of G, a T-colouring of G is an assignment of colours to its vertices so the assigned colours distances do not exist in the associated set T. Since this problem is NP-Complete, only few heuristics are implemented for restricted conditions on the sets T. The ant colony optimisation (ACO) has been successfully applied to different problems [SAL08]. Nevertheless, no attempt of ACO has been published for the T-colouring problem. We introduce, in this paper, two hybrid evolutionary approaches combining an ACO algorithm and a tabu search for the GTCP. These approaches are experimented for the general and restricted cases of the GTCP with different parameter's settings. The results are encouraging and show often better results than those published.
{"title":"Two hybrid ant algorithms for the general T-colouring problem","authors":"M. Aicha, Bessedik Malika, D. Habiba","doi":"10.1504/IJBIC.2010.036162","DOIUrl":"https://doi.org/10.1504/IJBIC.2010.036162","url":null,"abstract":"GCP is a well-known combinatorial problem that admits several generalisations from which the T-colouring (GTCP). Given a graph G and sets T of positive integers associated to the edges of G, a T-colouring of G is an assignment of colours to its vertices so the assigned colours distances do not exist in the associated set T. Since this problem is NP-Complete, only few heuristics are implemented for restricted conditions on the sets T. The ant colony optimisation (ACO) has been successfully applied to different problems [SAL08]. Nevertheless, no attempt of ACO has been published for the T-colouring problem. We introduce, in this paper, two hybrid evolutionary approaches combining an ACO algorithm and a tabu search for the GTCP. These approaches are experimented for the general and restricted cases of the GTCP with different parameter's settings. The results are encouraging and show often better results than those published.","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"51 1","pages":"353-362"},"PeriodicalIF":3.5,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85964486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1504/ijbic.2023.10048934
Kwok Tai Chui, Xinyu Zhang, Mingbo Zhao, R. Liu
{"title":"Bio-inspired Algorithms for Cybersecurity","authors":"Kwok Tai Chui, Xinyu Zhang, Mingbo Zhao, R. Liu","doi":"10.1504/ijbic.2023.10048934","DOIUrl":"https://doi.org/10.1504/ijbic.2023.10048934","url":null,"abstract":"","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"58 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91230834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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