The discrete gradient evolutionary strategy method for global optimization
- Authors: Abbas, Hussein , Bagirov, Adil , Zhang, Jiapu
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at the Congress on Evolutionary Computation CEC 2003, Canberra : 8th December, 2003
- Full Text:
- Reviewed:
- Description: Global optimization problems continue to be a challenge in computational mathematics. The field is progressing in two streams: deterministic and heuristic approaches. In this paper, we present a hybrid method that uses the discrete gradient method, which is a derivative free local search method, and evolutionary strategies. We show that the hybridization of the two methods is better than each of them in isolation.
- Description: E1
- Description: 2003000440
An optimization-based approach to patient grouping for acute healthcare in Australia
- Authors: Bagirov, Adil , Churilov, Leonid
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at Computational Science - ICCS 2003 Conference, Melbourne : 2nd June, 2003
- Full Text: false
- Reviewed:
- Description: The problem of cluster analysis is formulated as a problem of nonsmooth, nonconvex optimization, and an algorithm for solving the cluster analysis problem based on the nonsmooth optimization techniques is developed. The issues of applying this algorithm to large data sets are discussed and a feature selection procedure is demonstrated. The algorithm is then applied to a hospital data set to generate new knowledge about different patterns of patients resource consumption.
- Description: E1
- Description: 2003000434
A global optimisation approach to classification in medical diagnosis and prognosis
- Authors: Bagirov, Adil , Rubinov, Alex , Yearwood, John , Stranieri, Andrew
- Date: 2001
- Type: Text , Conference paper
- Relation: Paper presented at 34th Hawaii International Conference on System Sciences, HICSS-34, Maui, Hawaii, USA : 3rd-6th January 2001
- Full Text:
- Description: In this paper global optimisation-based techniques are studied in order to increase the accuracy of medical diagnosis and prognosis with FNA image data from the Wisconsin Diagnostic and Prognostic Breast Cancer databases. First we discuss the problem of determining the most informative features for the classification of cancerous cases in the databases under consideration. Then we apply a technique based on convex and global optimisation to breast cancer diagnosis. It allows the classification of benign cases and malignant ones and the subsequent diagnosis of patients with very high accuracy. The third application of this technique is a method that calculates centres of clusters to predict when breast cancer is likely to recur in patients for which cancer has been removed. The technique achieves higher accuracy with these databases than reported elsewhere in the literature.
- Description: 2003003950
A nonsmooth optimization approach to sensor network localization
- Authors: Bagirov, Adil , Lai, Daniel , Palaniswami, M.
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, ISSNIP 2007, Melbourne, Victoria : 3rd-6th December 2007 p. 727-732
- Relation: http://purl.org/au-research/grants/arc/DP0666061
- Full Text:
- Description: In this paper the problem of localization of wireless sensor network is formulated as an unconstrained nonsmooth optimization problem. We minimize a distance objective function which incorporates unknown sensor nodes and nodes with known positions (anchors) in contrast to popular semidefinite programming (SDP) methods which use artificial objective functions. We study the main properties of the objective function in this problem and design an algorithm for its minimization. Our algorithm is a derivative-free discrete gradient method that allows one to find a near global solution. The algorithm can handle a large number of sensors in the network. This paper contains the theory of our proposed formulation and algorithm while experimental results are included in later work.
- Description: 2003004949
Penalty functions with a small penalty parameter : Numerical experiments
- Authors: Bagirov, Adil , Rubinov, Alex
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at Industrial Optimization Conference 2003, Perth : 30th September, 2002
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000432
Optimisation of operations of a water distribution system for reduced power usage
- Authors: Bagirov, Adil , Ugon, Julien , Barton, Andrew , Briggs, Steven
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at 9th National Conference on Hydraulics in Water Engineering: Hydraulics 2008, Darwin, Northern Territory : 22nd-26th September 2008
- Full Text: false
- Description: There are many improvements to operation that can be made to a water distribution system once it has been constructed and placed in ground. Pipes and associated storages and pumps are typically designed to meet average peak daily demands, offer some capacity for growth, and also allow for some deterioration of performance over time. However, the 'as constructed' performance of the pipeline is invariably different to what was designed on paper, and this is particularly so for anything other than design flows, such as during times of water restrictions when there are significantly reduced flows. Because of this, there remain significant benefits to owners and operators for the adaptive and global optimisation of such systems. The present paper uses the Ouyen subsystem of the Northern Mallee Pipeline, in Victoria, as a case study for the development of an optimisation model. This has been done with the intent of using this model to reduce costs and provide better service to customers on this system. The Ouyen subsystem consists of 1600 km of trunk and distribution pipeline servicing an area of 456,000 Ha. The system includes 2 fixed speed pumps diverting water from the Murray River at Liparoo into two 150 ML balancing storages at Ouyen, 4 variable speed pumps feeding water from the balancing storages into the pipeline system, 2 variable speed pressure booster pumps and 5 town balancing storages. When considering all these components of the system, power consumption becomes an important part of the overall operation. The present paper considers a global optimisation model to minimise power consumption while maintaining reasonable performance of the system. The main components of the model are described including the network structure and the costs functions associated with the system. The final model presents the cost functions associated with the pump scheduling, including the penalties descriptions associated with maintaining appropriate storages levels and pressure bounds within the water distribution network.
- Description: 2003006758
Modified global k-means algorithm for clustering in gene expression data sets
- Authors: Bagirov, Adil , Mardaneh, Karim
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at Intelligent Systems for Bioinformatics 2006, proceedings of the AI 2006 Workshop on Intelligent Systems of Bioinformatics, Hobart, Tasmania : 4th December, 2006
- Full Text:
- Reviewed:
- Description: Clustering in gene expression data sets is a challenging problem. Different algorithms for clustering of genes have been proposed. However due to the large number of genes only a few algorithms can be applied for the clustering of samples. k-means algorithm and its different variations are among those algorithms. But these algorithms in general can converge only to local minima and these local minima are significantly different from global solutions as the number of clusters increases. Over the last several years different approaches have been proposed to improve global search properties of k-means algorithm and its performance on large data sets. One of them is the global k-means algorithm. In this paper we develop a new version of the global k-means algorithm: the modified global k-means algorithm which is effective for solving clustering problems in gene expression data sets. We present preliminary computational results using gene expression data sets which demonstrate that the modified k-means algorithm improves and sometimes significantly results by k-means and global k-means algorithms.
- Description: E1
- Description: 2003001713
Optimization of feed forward MLPs using the discrete gradient method
- Authors: Bagirov, Adil , Yearwood, John , Ghosh, Ranadhir
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at CIMCA 2004: International Conference on Computational Intelligence for Modelling, Control & Automation, Gold Coast, Queensland : 12th July, 2004
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000845
Nonsmooth optimisation approach to data classification
- Authors: Bagirov, Adil , Soukhoroukova, Nadejda
- Date: 2001
- Type: Text , Conference paper
- Relation: Paper presented at Post-graduate ADFA Conference for Computer Science, PACCS01, Canberra, Australian Capital Territory : 14th July 2001
- Full Text:
- Description: We reduce the supervised classification to solving a nonsmooth optimization problem. The proposed method allows one to solve classification problems for databases with arbitrary number of classes. Numerical experiments have been carried out with databases of small and medium size. We present their results and provide comparison of these results with ones obtained by other algorithms of classification based on the optimization techniques. Results of numerical experiments show effectiveness of the proposed algorithms.
- Description: 2003003668
A novel approach to optimal pump scheduling in water distribution systems
- Authors: Bagirov, Adil , Barton, Andrew , Mala-Jetmarova, Helena , Al Nuaimat, Alia , Ahmed, S. T. , Sultanova, Nargiz , Yearwood, John
- Date: 2012
- Type: Text , Conference paper
- Relation: 14th Water Distribution Systems Analysis Conference 2012, WDSA 2012 Vol. 1; Adelaide, Australia; 24th-27th September; p. 618-631
- Relation: http://purl.org/au-research/grants/arc/LP0990908
- Full Text: false
- Reviewed:
- Description: The operation of a water distribution system is a complex task which involves scheduling of pumps, regulating water levels of storages, and providing satisfactory water quality to customers at required flow and pressure. Pump scheduling is one of the most important tasks of the operation of a water distribution system as it represents the major part of its operating costs. In this paper, a novel approach for modeling of pump scheduling to minimize energy consumption by pumps is introduced which uses pump's start/end run times as continuous variables. This is different from other approaches where binary integer variables for each hour are typically used which is considered very impractical from an operational perspective. The problem is formulated as a nonlinear programming problem and a new algorithm is developed for its solution. This algorithm is based on the combination of the grid search with the Hooke-Jeeves pattern search method. The performance of the algorithm is evaluated using literature test problems applying the hydraulic simulation model EPANet.
- Description: E1
Comparison of metaheuristic algorithms for pump operation optimization
- Authors: Bagirov, Adil , Ahmed, S. T. , Barton, Andrew , Mala-Jetmarova, Helena , Al Nuaimat, Alia , Sultanova, Nargiz
- Date: 2012
- Type: Text , Conference paper
- Relation: 14th Water Distribution Systems Analysis Conference 2012, WDSA 2012 Vol. 2; Adelaide, Australia; 24th-27th September 2012; p. 886-896
- Relation: http://purl.org/au-research/grants/arc/LP0990908
- Full Text: false
- Reviewed:
- Description: Pumping cost constitutes the main part of the overall operating cost of water distribution systems. There are different optimization formulations of the pumping cost minimization problem including those with application of continuous and integer programming approaches. To date mainly various metaheuristics have been applied to solve this problem. However, the comprehensive comparison of those metaheuristics has not been done. Such a comparison is important to identify strengths and weaknesses of different algorithms which reflects on their performance. In this paper, we present a methodology for comparative analysis of widely used metaheuristics for solving the pumping cost minimization problem. This methodology includes the following comparison criteria: (a) the "optimal solution" obtained; (b) the efficiency; and (c) robustness. Algorithms applied are: particle swarm optimization, artificial bee colony and firefly algorithms. These algorithms were applied to one test problem available in the literature. The results obtained demonstrate that the artificial bee colony is the most robust and the firefly is the most efficient and accurate algorithm for this test problem. Funding :ARC
A new modified global k-means algorithm for clustering large data sets
- Authors: Bagirov, Adil , Ugon, Julien , Webb, Dean
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at XIIIth International Conference : Applied Stochastic Models and Data Analysis, ASMDA 2009, Vilnius, Lithuania : 30th June - 3rd July 2009 p. 1-5
- Full Text: false
- Description: The k-means algorithm and its variations are known to be fast clustering algorithms. However, they are sensitive to the choice of starting points and inefficient for solving clustering problems in large data sets. Recently, in order to resolve difficulties with the choice of starting points, incremental approaches have been developed. The modified global k-means algorithm is based on such an approach. It iteratively adds one cluster center at a time. Numerical experiments show that this algorithm considerably improve the k-means algorithm. However, this algorithm is not suitable for clustering very large data sets. In this paper, a new version of the modified global k-means algorithm is proposed. We introduce an auxiliary cluster function to generate a set of starting points spanning different parts of the data set. We exploit information gathered in previous iterations of the incremental algorithm to reduce its complexity.
- Description: 2003007558
An incremental approach for the construction of a piecewise linear classifier
- Authors: Bagirov, Adil , Ugon, Julien , Webb, Dean
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at XIIIth International Conference : Applied Stochastic Models and Data Analysis, ASMDA 2009, Vilnius, Lithuania : 30th June - 3rd July 2009 p. 507–511
- Relation: https://purl.org/au-research/grants/arc/DP0666061
- Full Text: false
- Description: In this paper the problem of finding piecewise linear boundaries between sets is considered and is applied for solving supervised data classification problems. An algorithm for the computation of piecewise linear boundaries, consisting of two main steps, is proposed. In the first step sets are approximated by hyperboxes to find so-called “indeterminate” regions between sets. In the second step sets are separated inside these “indeterminate” regions by piecewise linear functions. These functions are computed incrementally starting with a linear function. Results of numerical experiments are reported. These results demonstrate that the new algorithm requires a reasonable training time and it produces consistently good test set accuracy on most data sets comparing with mainstream classifiers.
- Description: 2003007559
Subgradient smoothing method for nonsmooth nonconvex optimization
- Authors: Bagirov, Adil , Sultanova, N. , Taheri, S. , Ozturk, G.
- Date: 2021
- Type: Text , Conference paper
- Relation: 5th International Conference on Numerical Analysis and Optimization: Theory, Methods, Applications and Technology Transfer, NAOV, Muscan, 6-9 January 2020 Vol. 354, p. 57-79
- Full Text: false
- Reviewed:
- Description: In this chapter an unconstrained nonsmooth nonconvex optimization problem is considered and a method for solving this problem is developed. In this method the subproblem for finding search directions is reduced to the unconstrained minimization of a smooth function. This is achieved by using subgradients computed in some neighborhood of a current iteration point and by formulating the search direction finding problem to the minimization of the convex piecewise linear function over the unit ball. The hyperbolic smoothing technique is applied to approximate the minimization problem by a sequence of smooth problems. The convergence of the proposed method is studied and its performance is evaluated using a set of nonsmooth optimization academic test problems. In addition, the method is implemented in GAMS and numerical results using different solvers from GAMS are reported. The proposed method is compared with a number of nonsmooth optimization methods. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
SMGKM : an efficient incremental algorithm for clustering document collections
- Authors: Bagirov, Adil , Seifollahi, Sattar , Piccardi, Massimo , Zare Borzeshi, Ehsan , Kruger, Bernie
- Date: 2023
- Type: Text , Conference paper
- Relation: 19th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2018, Hanoi, Vietnam, 18-24 March 2018, Computational Linguistics and Intelligent Text Processing Vol. 13397 LNCS, p. 314-328
- Full Text: false
- Reviewed:
- Description: Given a large unlabeled document collection, the aim of this paper is to develop an accurate and efficient algorithm for solving the clustering problem over this collection. Document collections typically contain tens or hundreds of thousands of documents, with thousands or tens of thousands of features (i.e., distinct words). Most existing clustering algorithms struggle to find accurate solutions on such large data sets. The proposed algorithm overcomes this difficulty by an incremental approach, incrementing the number of clusters progressively from an initial value of one to a set value. At each iteration, the new candidate cluster is initialized using a partitioning approach which is guaranteed to minimize the objective function. Experiments have been carried out over six, diverse datasets and with different evaluation criteria, showing that the proposed algorithm has outperformed comparable state-of-the-art clustering algorithms in all cases. © 2023, Springer Nature Switzerland AG.
Minimization of pumping costs in water distribution systems using explicit and implicit pump scheduling
- Authors: Barton, Andrew , Mala-Jetmarova, Helena , Nuamat, Alia Mari Al , Bagirov, Adil , Sultanova, Nargiz , Ahmed, Shams
- Date: 2012
- Type: Text , Conference paper
- Relation: 34th Hydrology and Water Resources Symposium, HWRS 2012; Sydney, Australia; 19th-22nd November 2012; p. 1298-1305
- Relation: http://purl.org/au-research/grants/arc/LP0990908
- Full Text: false
- Reviewed:
- Description: The operation of a water distribution system is a complex task which involves scheduling of pumps, regulating water levels of storages, and providing satisfactory water quality to customers at required flow and pressure. Pump scheduling is one of the most important tasks of the operation of a water distribution system as it represents the major part of its operating costs. In this paper, a novel approach for modeling of pump scheduling to minimize energy consumption by pumps is introduced which uses pump's start/end run times. We separate two types of pumps, one is operated based on the water level in a storage and another one is operated based on downstream pressure. For the first type of pumps both the explicit and implicit pump scheduling can be used, whereas the second type pumps can be optimized only using implicit pump scheduling. The problem is formulated as an optimization problem and an algorithm is developed for its solution. The performance of the algorithm is evaluated using a literature test problem applying the hydraulic simulation model EPANet.
Parallelization of the discrete gradient method of non-smooth optimization and its applications
- Authors: Beliakov, Gleb , Tobon, Monsalve , Bagirov, Adil
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at Computational Science ICCS 2003 Conference, Melbourne : 2nd June, 2003
- Full Text: false
- Reviewed:
- Description: We investigate parallelization and performance of the discrete gradient method of nonsmooth optimization. This derivative free method is shown to be an effective optimization tool, able to skip many shallow local minima of nonconvex nondifferentiable objective functions. Although this is a sequential iterative method, we were able to parallelize critical steps of the algorithm, and this lead to a significant improvement in performance on multiprocessor computer clusters. We applied this method to a difficult polyatomic clusters problem in computational chemistry, and found this method to outperform other algorithms.
- Description: E1
- Description: 2003000435
Improving risk grouping rules for prostate cancer patients with optimization
- Authors: Churilov, Leonid , Bagirov, Adil , Schwartz, Daniel , Smith, Kate , Dally, Michael
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at the 37th Annual Hawaii International Conference on System Sciences, Big Island, Hawaii : 5th January, 2004
- Full Text:
- Reviewed:
- Description: Data mining techniques provide a popular and powerful toolset to address both clinical and management issues in the area of health care. This paper describes the study of assigning prostate cancer patients into homogenous groups with the aim to support future clinical treatment decisions. The cluster analysis based model is suggested and an application of non-smooth non-convex optimization techniques to solve this model is discussed. It is demonstrated that using the optimization based approach to data mining of a prostate cancer patients database can lead to generation of a significant amount of new knowledge that can be effectively utilized to enhance clinical decision making.
- Description: E1
- Description: 2003000846
Multi-objective optimisation to manage trade-offs in water quality and quantity of complex water resource system
- Authors: Dey, Sayani , Barton, Andrew , Bagirov, Adil , Kandra, Harpreet , Wilson, Kym
- Date: 2021
- Type: Text , Conference paper
- Relation: Hydrology and Water Resources Symposium 2021, HWRS 2021: Digital Water: Hydrology and Water Resources Symposium 2021, Virtual online, 31 August-1 September 2021, HWRS 2021: Digital Water: Hydrology and Water Resources Symposium 2021 p. 465-480
- Full Text: false
- Reviewed:
- Description: Water of adequate quality and quantity is the key to health and integrity of the environment and fundamental to good water supply. Achieving water quality and quantity objectives can conflict and has become more complicated with challenges like, climate change, growing populations and changed land uses. Therefore, a multi-objective optimisation strategy is required for achieving optimal water quality and quantity outcomes from a water resources system. This study uses a multi-objective optimisation approach to illustrate the trade-offs occurring when water quantity and quality in a reservoir system are optimised. Taylors Lake, part of the Grampians Reservoir System in Western Victoria, Australia was chosen as the case study for this research as it is quite complex and includes many contemporary water resources challenges seen around the world, such as high turbidity and salinity. The objective functions are set in a way to maximise the water quantity available for supply, while minimising the deviation of quality parameters from the accepted limits. The water system is modelled using eWater Source® modelling platform, while optimisation is undertaken using NSGA-II optimisation technique. Daily time step data over a ten-year period was used in this work. Various optimisation runs were performed with different population sizes and generations to seek out the best trade-off curve. The optimisation results indicate trade-offs between salinity, turbidity, and quantity. Key findings for this case study show that through optimisation, stored water never exceeded 19,000 ML even though the storage capacity was 27,000 ML indicating a significant loss of water to improve quality, or alternatively, a potential asset re-design opportunity.
Application of optimisation-based data mining techniques to medical data sets: A comparative analysis
- Authors: Dzalilov, Zari , Bagirov, Adil , Mammadov, Musa
- Date: 2012
- Type: Text , Conference paper
- Relation: IMMM 2102: The Second International Conference on Advances in Information Mining and Management p. 41-46
- Full Text: false
- Reviewed:
- Description: Abstract - Computational methods have become an important tool in the analysis of medical data sets. In this paper, we apply three optimisation-based data mining methods to the following data sets: (i) a cystic fibrosis data set and (ii) a tobacco control data set. Three algorithms used in the analysis of these data sets include: the modified linear least square fit, an optimization based heuristic algorithm for feature selection and an optimization based clustering algorithm. All these methods explore the relationship between features and classes, with the aim of determining contribution of specific features to the class outcome. However, the three algorithms are based on completely different approaches. We apply these methods to solve feature selection and classification problems. We also present comparative analysis of the algorithms using computational results. Results obtained confirm that these algorithms may be effectively applied to the analysis of other (bio)medical data sets