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21Yearwood, John
20Ugon, Julien
14Rubinov, Alex
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11Mala-Jetmarova, Helena
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9Karmitsa, Napsu
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39Nonsmooth optimization
330103 Numerical and Computational Mathematics
280102 Applied Mathematics
190802 Computation Theory and Mathematics
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Constrained self organizing maps for data clusters visualization

- Mohebi, Ehsan, Bagirov, Adil

**Authors:**Mohebi, Ehsan , Bagirov, Adil**Date:**2016**Type:**Text , Journal article**Relation:**Neural Processing Letters Vol. 43, no. 3 (2016), p. 849-869**Full Text:**false**Reviewed:****Description:**High dimensional data visualization is one of the main tasks in the field of data mining and pattern recognition. The self organizing maps (SOM) is one of the topology visualizing tool that contains a set of neurons that gradually adapt to input data space by competitive learning and form clusters. The topology preservation of the SOM strongly depends on the learning process. Due to this limitation one cannot guarantee the convergence of the SOM in data sets with clusters of arbitrary shape. In this paper, we introduce Constrained SOM (CSOM), the new version of the SOM by modifying the learning algorithm. The idea is to introduce an adaptive constraint parameter to the learning process to improve the topology preservation and mapping quality of the basic SOM. The computational complexity of the CSOM is less than those with the SOM. The proposed algorithm is compared with similar topology preservation algorithms and the numerical results on eight small to large real-world data sets demonstrate the efficiency of the proposed algorithm. © 2015, Springer Science+Business Media New York.

Nonsmooth DC programming approach to the minimum sum-of-squares clustering problems

- Bagirov, Adil, Taheri, Sona, Ugon, Julien

**Authors:**Bagirov, Adil , Taheri, Sona , Ugon, Julien**Date:**2016**Type:**Text , Journal article**Relation:**Pattern Recognition Vol. 53, no. (2016), p. 12-24**Relation:**http://purl.org/au-research/grants/arc/DP140103213**Full Text:**false**Reviewed:****Description:**This paper introduces an algorithm for solving the minimum sum-of-squares clustering problems using their difference of convex representations. A non-smooth non-convex optimization formulation of the clustering problem is used to design the algorithm. Characterizations of critical points, stationary points in the sense of generalized gradients and inf-stationary points of the clustering problem are given. The proposed algorithm is tested and compared with other clustering algorithms using large real world data sets. © 2015 Elsevier Ltd. All rights reserved.

A heuristic algorithm for solving the minimum sum-of-squares clustering problems

**Authors:**Ordin, Burak , Bagirov, Adil**Date:**2015**Type:**Text , Journal article**Relation:**Journal of Global Optimization Vol. 61, no. 2 (2015), p. 341-361**Relation:**http://purl.org/au-research/grants/arc/DP140103213**Full Text:**false**Reviewed:****Description:**Clustering is an important task in data mining. It can be formulated as a global optimization problem which is challenging for existing global optimization techniques even in medium size data sets. Various heuristics were developed to solve the clustering problem. The global k-means and modified global k-means are among most efficient heuristics for solving the minimum sum-of-squares clustering problem. However, these algorithms are not always accurate in finding global or near global solutions to the clustering problem. In this paper, we introduce a new algorithm to improve the accuracy of the modified global k-means algorithm in finding global solutions. We use an auxiliary cluster problem to generate a set of initial points and apply the k-means algorithm starting from these points to find the global solution to the clustering problems. Numerical results on 16 real-world data sets clearly demonstrate the superiority of the proposed algorithm over the global and modified global k-means algorithms in finding global solutions to clustering problems.

A history of water distribution systems and their optimisation

- Mala-Jetmarova, Helena, Barton, Andrew, Bagirov, Adil

**Authors:**Mala-Jetmarova, Helena , Barton, Andrew , Bagirov, Adil**Date:**2015**Type:**Text , Journal article**Relation:**Water Science and Technology-Water Supply Vol. 15, no. 2 (2015), p. 224-235**Relation:**http://purl.org/au-research/grants/arc/LP0990908**Full Text:**false**Reviewed:****Description:**Water distribution systems have a very long and rich history dating back to the third millennium B.C. Advances in water supply and distribution were followed in parallel by discoveries and inventions in other related fields. Therefore, it is the aim of this paper to review both the history of water distribution systems and those related fields in order to present a coherent summary of the complex multi-stranded discipline of water engineering. Related fields reviewed in this paper include devices for raising water and water pumps, water quality and water treatment, hydraulics, network analysis, and optimisation of water distribution systems. The review is brief and concise and allows the reader to quickly gain an understanding of the history and advancements of water distribution systems and analysis. Furthermore, the paper gives details of other existing publications where more information can be found.

An incremental clustering algorithm based on hyperbolic smoothing

- Bagirov, Adil, Ordin, Burak, Ozturk, Gurkan, Xavier, Adilson

**Authors:**Bagirov, Adil , Ordin, Burak , Ozturk, Gurkan , Xavier, Adilson**Date:**2015**Type:**Text , Journal article**Relation:**Computational Optimization and Applications Vol. 61, no. 1 (2015), p. 219-241**Relation:**http://purl.org/au-research/grants/arc/DP140103213**Full Text:**false**Reviewed:****Description:**Clustering is an important problem in data mining. It can be formulated as a nonsmooth, nonconvex optimization problem. For the most global optimization techniques this problem is challenging even in medium size data sets. In this paper, we propose an approach that allows one to apply local methods of smooth optimization to solve the clustering problems. We apply an incremental approach to generate starting points for cluster centers which enables us to deal with nonconvexity of the problem. The hyperbolic smoothing technique is applied to handle nonsmoothness of the clustering problems and to make it possible application of smooth optimization algorithms to solve them. Results of numerical experiments with eleven real-world data sets and the comparison with state-of-the-art incremental clustering algorithms demonstrate that the smooth optimization algorithms in combination with the incremental approach are powerful alternative to existing clustering algorithms.

An incremental piecewise linear classifier based on polyhedral conic separation

- Ozturk, Gurkan, Bagirov, Adil, Kasimbeyli, Refail

**Authors:**Ozturk, Gurkan , Bagirov, Adil , Kasimbeyli, Refail**Date:**2015**Type:**Text , Journal article**Relation:**Machine Learning Vol. 101, no. 1-3 (2015), p. 397-413**Relation:**http://purl.org/au-research/grants/arc/DP140103213**Full Text:**false**Reviewed:****Description:**In this paper, a piecewise linear classifier based on polyhedral conic separation is developed. This classifier builds nonlinear boundaries between classes using polyhedral conic functions. Since the number of polyhedral conic functions separating classes is not known a priori, an incremental approach is proposed to build separating functions. These functions are found by minimizing an error function which is nonsmooth and nonconvex. A special procedure is proposed to generate starting points to minimize the error function and this procedure is based on the incremental approach. The discrete gradient method, which is a derivative-free method for nonsmooth optimization, is applied to minimize the error function starting from those points. The proposed classifier is applied to solve classification problems on 12 publicly available data sets and compared with some mainstream and piecewise linear classifiers. © 2014, The Author(s).

Diagnostic with incomplete nominal/discrete data

- Jelinek, Herbert, Yatsko, Andrew, Stranieri, Andrew, Venkatraman, Sitalakshmi, Bagirov, Adil

**Authors:**Jelinek, Herbert , Yatsko, Andrew , Stranieri, Andrew , Venkatraman, Sitalakshmi , Bagirov, Adil**Date:**2015**Type:**Text , Journal article**Relation:**Artificial Intelligence Research Vol. 4, no. 1 (2015), p. 22-35**Full Text:****Reviewed:****Description:**Missing values may be present in data without undermining its use for diagnostic / classification purposes but compromise application of readily available software. Surrogate entries can remedy the situation, although the outcome is generally unknown. Discretization of continuous attributes renders all data nominal and is helpful in dealing with missing values; particularly, no special handling is required for different attribute types. A number of classifiers exist or can be reformulated for this representation. Some classifiers can be reinvented as data completion methods. In this work the Decision Tree, Nearest Neighbour, and Naive Bayesian methods are demonstrated to have the required aptness. An approach is implemented whereby the entered missing values are not necessarily a close match of the true data; however, they intend to cause the least hindrance for classification. The proposed techniques find their application particularly in medical diagnostics. Where clinical data represents a number of related conditions, taking Cartesian product of class values of the underlying sub-problems allows narrowing down of the selection of missing value substitutes. Real-world data examples, some publically available, are enlisted for testing. The proposed and benchmark methods are compared by classifying the data before and after missing value imputation, indicating a significant improvement.

**Authors:**Jelinek, Herbert , Yatsko, Andrew , Stranieri, Andrew , Venkatraman, Sitalakshmi , Bagirov, Adil**Date:**2015**Type:**Text , Journal article**Relation:**Artificial Intelligence Research Vol. 4, no. 1 (2015), p. 22-35**Full Text:****Reviewed:****Description:**Missing values may be present in data without undermining its use for diagnostic / classification purposes but compromise application of readily available software. Surrogate entries can remedy the situation, although the outcome is generally unknown. Discretization of continuous attributes renders all data nominal and is helpful in dealing with missing values; particularly, no special handling is required for different attribute types. A number of classifiers exist or can be reformulated for this representation. Some classifiers can be reinvented as data completion methods. In this work the Decision Tree, Nearest Neighbour, and Naive Bayesian methods are demonstrated to have the required aptness. An approach is implemented whereby the entered missing values are not necessarily a close match of the true data; however, they intend to cause the least hindrance for classification. The proposed techniques find their application particularly in medical diagnostics. Where clinical data represents a number of related conditions, taking Cartesian product of class values of the underlying sub-problems allows narrowing down of the selection of missing value substitutes. Real-world data examples, some publically available, are enlisted for testing. The proposed and benchmark methods are compared by classifying the data before and after missing value imputation, indicating a significant improvement.

- Mala-Jetmarova, Helena, Barton, Andrew, Bagirov, Adil

**Authors:**Mala-Jetmarova, Helena , Barton, Andrew , Bagirov, Adil**Date:**2015**Type:**Text , Journal article**Relation:**Journal of Water Resources Planning and Management Vol. 141, no. 6 (2015), p. 1-16**Relation:**http://purl.org/au-research/grants/arc/LP0990908**Full Text:**false**Reviewed:****Description:**This paper explores the trade-offs between water quality and pumping costs objectives in optimization of operation of regional multiquality water distribution systems. The optimization model is designed to concurrently minimize each objective, where water quality is represented by the deviations of constituent concentrations from required values and pumping costs are represented by energy consumed by the pumps. The optimization problem is solved using an optimization software, incorporating the nondominated sorting genetic algorithm II (NSGA-II), linked with network analysis software. Two typical but purposefully different example networks are used. First, a network with multiple water sources of different qualities and second, a network with one water source only, which was converted to represent a regional nondrinking water distribution system. The trade-offs between water quality and pumping costs are explored using a total of 14 scenarios reflecting different water quality configurations of these networks. Those scenarios, into which time variability was introduced for both source water quality and customer water quality requirements, were systematically developed to represent real-life situations that could be found in practice. The results indicate that for the majority of the scenarios, there is a trade-off with a competing nature between water quality and pumping costs objectives. Additionally, it was discovered that multiobjective optimization problems with water quality (i.e., concentration deviations) and pumping costs objectives could be reduced in certain instances into a single-objective problem of minimizing pumping costs. In fact, a regional water distribution system in which water quality is represented by a single conservative constituent can produce either a trade-off or single-objective solution between those two objectives, and this outcome is dependent on both the water quality configuration of the system and system operational flexibility. Last, some particular conclusions are drawn for both a water distribution system with multiple water sources and a water distribution system with a single water source, which suggest how changes in source water qualities or customer water quality requirements may impact system operation. It is, therefore, demonstrated that water utilities which operate regional multiquality nondrinking water distribution systems could benefit from the exploration of trade-offs between water quality and pumping costs for the purpose of operational planning.

- Mala-Jetmarova, Helena, Barton, Andrew, Bagirov, Adil

**Authors:**Mala-Jetmarova, Helena , Barton, Andrew , Bagirov, Adil**Date:**2015**Type:**Text , Journal article**Relation:**Journal of Water Resources Planning and Management Vol. 141, no. 10 (2015), p.1-14**Relation:**http://purl.org/au-research/grants/arc/LP0990908**Full Text:**false**Reviewed:****Description:**The impact of water quality conditions in source reservoirs on the optimal operation of a regional multiquality water-distribution system is analyzed. The optimization model concurrently minimizes three operational objectives being pump energy costs, turbidity, and salinity deviations at customer demand nodes from allowed values. The optimization problem is solved using the optimization tool GANetXL incorporating the NSGA-II, linked with the network analysis software EPANet. The example network adapted from the literature captures some of the unique features of the Wimmera Mallee Pipeline in Australia. Six scenarios representing different water quality conditions in source reservoirs are analyzed. It was discovered that two types of trade-offs, competing and noncompeting, exist between the objectives and that the type of trade-off is not unique between a particular pair of objectives for all scenarios. These and other findings may be of particular use to system operators in their long-term operational planning and decision making. (C) 2015 American Society of Civil Engineers.

Modified self-organising maps with a new topology and initialisation algorithm

- Mohebi, Ehsan, Bagirov, Adil

**Authors:**Mohebi, Ehsan , Bagirov, Adil**Date:**2015**Type:**Text , Journal article**Relation:**Journal of Experimental and Theoretical Artificial Intelligence Vol. 27, no. 3 (2015), p. 351-372**Full Text:**false**Reviewed:****Description:**Mapping quality of the self-organising maps (SOMs) is sensitive to the map topology and initialisation of neurons. In this article, in order to improve the convergence of the SOM, an algorithm based on split and merge of clusters to initialise neurons is introduced. The initialisation algorithm speeds up the learning process in large high-dimensional data sets. We also develop a topology based on this initialisation to optimise the vector quantisation error and topology preservation of the SOMs. Such an approach allows to find more accurate data visualisation and consequently clustering problem. The numerical results on eight small-to-large real-world data sets are reported to demonstrate the performance of the proposed algorithm in the sense of vector quantisation, topology preservation and CPU time requirement. © 2014 Taylor & Francis.

Nonsmooth optimization algorithm for solving clusterwise linear regression problems

- Bagirov, Adil, Ugon, Julien, Mirzayeva, Hijran

**Authors:**Bagirov, Adil , Ugon, Julien , Mirzayeva, Hijran**Date:**2015**Type:**Text , Journal article**Relation:**Journal of Optimization Theory and Applications Vol. 164, no. 3 (2015), p. 755-780**Relation:**http://purl.org/au-research/grants/arc/DP140103213**Full Text:**false**Reviewed:****Description:**Clusterwise linear regression consists of finding a number of linear regression functions each approximating a subset of the data. In this paper, the clusterwise linear regression problem is formulated as a nonsmooth nonconvex optimization problem and an algorithm based on an incremental approach and on the discrete gradient method of nonsmooth optimization is designed to solve it. This algorithm incrementally divides the whole dataset into groups which can be easily approximated by one linear regression function. A special procedure is introduced to generate good starting points for solving global optimization problems at each iteration of the incremental algorithm. The algorithm is compared with the multi-start Spath and the incremental algorithms on several publicly available datasets for regression analysis.

Nonsmooth optimization based algorithms in cluster analysis

- Bagirov, Adil, Mohebi, Ehsan

**Authors:**Bagirov, Adil , Mohebi, Ehsan**Date:**2015**Type:**Text , Book chapter**Relation:**Partitional Clustering Algorithms p. 99-146**Full Text:**false**Reviewed:****Description:**Cluster analysis is an important task in data mining. It deals with the problem of organization of a collection of objects into clusters based on a similarity measure. Various distance functions can be used to define the similarity measure. Cluster analysis problems with the similarity measure defined by the squared Euclidean distance, which is also known as the minimum sum-of-squares clustering, has been studied extensively over the last five decades. L1 and L1 norms have attracted less attention. In this chapter, we consider a nonsmooth nonconvex optimization formulation of the cluster analysis problems. This formulation allows one to easily apply similarity measures defined using different distance functions. Moreover, an efficient incremental algorithm can be designed based on this formulation to solve the clustering problems. We develop incremental algorithms for solving clustering problems where the similarity measure is defined using the L1; L2 and L1 norms. We also consider different algorithms for solving nonsmooth nonconvex optimization problems in cluster analysis. The proposed algorithms are tested using several real world data sets and compared with other similar algorithms.**Description:**Cluster analysis is an important task in data mining. It deals with the problem of organization of a collection of objects into clusters based on a similarity measure. Various distance functions can be used to define the similarity measure. Cluster analysis problems with the similarity measure defined by the squared Euclidean distance, which is also known as the minimum sum-of-squares clustering, has been studied extensively over the last five decades. However, problems with the L

- Mala-Jetmarova, Helena, Barton, Andrew, Bagirov, Adil

**Authors:**Mala-Jetmarova, Helena , Barton, Andrew , Bagirov, Adil**Date:**2015**Type:**Text , Journal article**Relation:**Journal of Hydroinformatics Vol. 17, no. 6 (2015), p. 891-916**Relation:**http://purl.org/au-research/grants/arc/LP0990908**Full Text:**false**Reviewed:****Description:**This paper presents an extensive analysis of the sensitivity of multi-objective algorithm parameters and objective function scaling tested on a large number of parameter setting combinations for a water distribution system optimisation problem. The optimisation model comprises two operational objectives minimised concurrently, the pump energy costs and deviations of constituent concentrations as a water quality measure. This optimisation model is applied to a regional nondrinking water distribution system, and solved using the optimisation software GANetXL incorporating the NSGA-II linked with the network analysis software EPANet. The sensitivity analysis employs a set of performance metrics, which were designed to capture the overall quality of the computed Pareto fronts. The performance and sensitivity of NSGA-II parameters using those metrics is evaluated. The results demonstrate that NSGA-II is sensitive to different parameter settings, and unlike in the single-objective problems, a range of parameter setting combinations appears to be required to reach a Pareto front of optimal solutions. Additionally, inadequately scaled objective functions cause the NSGA-II bias towards the second objective. Lastly, the methodology for performance and sensitivity analysis may be used for calibration of algorithm parameters.

Solving DC programs using the cutting angle method

- Ferrer, Albert, Bagirov, Adil, Beliakov, Gleb

**Authors:**Ferrer, Albert , Bagirov, Adil , Beliakov, Gleb**Date:**2015**Type:**Text , Journal article**Relation:**Journal of Global Optimization Vol. 61, no. 1 (2015), p. 71-89**Relation:**http://purl.org/au-research/grants/arc/DP140103213**Full Text:**false**Reviewed:****Description:**In this paper, we propose a new algorithm for global minimization of functions represented as a difference of two convex functions. The proposed method is a derivative free method and it is designed by adapting the extended cutting angle method. We present preliminary results of numerical experiments using test problems with difference of convex objective functions and box-constraints. We also compare the proposed algorithm with a classical one that uses prismatical subdivisions.

A convolutional recursive modified Self Organizing Map for handwritten digits recognition

- Mohebi, Ehsan, Bagirov, Adil

**Authors:**Mohebi, Ehsan , Bagirov, Adil**Date:**2014**Type:**Text , Journal article**Relation:**Neural Networks Vol. 60, no. (2014), p. 104-118**Relation:**http://purl.org/au-research/grants/arc/DP140103213**Full Text:**false**Reviewed:****Description:**It is well known that the handwritten digits recognition is a challenging problem. Different classification algorithms have been applied to solve it. Among them, the Self Organizing Maps (SOM) produced promising results. In this paper, first we introduce a Modified SOM for the vector quantization problem with improved initialization process and topology preservation. Then we develop a Convolutional Recursive Modified SOM and apply it to the problem of handwritten digits recognition. The computational results obtained using the well known MNIST dataset demonstrate the superiority of the proposed algorithm over the existing SOM-based algorithms.

Aggregate codifferential method for nonsmooth DC optimization

- Tor, Ali, Bagirov, Adil, Karasozen, Bulent

**Authors:**Tor, Ali , Bagirov, Adil , Karasozen, Bulent**Date:**2014**Type:**Text , Journal article**Relation:**Journal of Computational and Applied Mathematics Vol. 259, no. Part B (2014), p. 851-867**Full Text:**false**Reviewed:****Description:**A new algorithm is developed based on the concept of codifferential for minimizing the difference of convex nonsmooth functions. Since the computation of the whole codifferential is not always possible, we use a fixed number of elements from the codifferential to compute the search directions. The convergence of the proposed algorithm is proved. The efficiency of the algorithm is demonstrated by comparing it with the subgradient, the truncated codifferential and the proximal bundle methods using nonsmooth optimization test problems.

An algorithm for clusterwise linear regression based on smoothing techniques

- Bagirov, Adil, Ugon, Julien, Mirzayeva, Hijran

**Authors:**Bagirov, Adil , Ugon, Julien , Mirzayeva, Hijran**Date:**2014**Type:**Text , Journal article**Relation:**Optimization Letters Vol. 9, no. 2 (2014), p. 375-390**Full Text:**false**Reviewed:****Description:**We propose an algorithm based on an incremental approach and smoothing techniques to solve clusterwise linear regression (CLR) problems. This algorithm incrementally divides the whole data set into groups which can be easily approximated by one linear regression function. A special procedure is introduced to generate an initial solution for solving global optimization problems at each iteration of the incremental algorithm. Such an approach allows one to find global or approximate global solutions to the CLR problems. The algorithm is tested using several data sets for regression analysis and compared with the multistart and incremental Spath algorithms.

CR-Modified SOM to the problem of handwritten digits recognition

- Mohebi, Ehsan, Bagirov, Adil

**Authors:**Mohebi, Ehsan , Bagirov, Adil**Date:**2014**Type:**Text , Conference proceedings**Relation:**34th SGAI International Conference on Innovative Techniques and Applications of Artcificial Intelligence; Cambridge, England; 9th-11th December 2014; published in Research and Development in Intelligent Systems XXXI (Incorporating Applications and Innovations in Intelligent Systems XXII) p. 225-238**Full Text:**false**Reviewed:****Description:**Recently, researchers show that the handwritten digit recognition is a challenging problem. In this paper first, we introduce a Modified Self Organizing Maps for vector quantization problem then we present a Convolutional Recursive ModifiedSOMto the problem of handwritten digit recognition. TheModifiedSOMis novel in the sense of initialization process and the topology preservation. The experimental result on the well known digit database of MNIST, denotes the superiority of the proposed algorithm over the existing SOM-based methods.

Introduction to Nonsmooth Optimization : Theory, practice and software

- Bagirov, Adil, Karmitsa, Napsu, Makela, Marko

**Authors:**Bagirov, Adil , Karmitsa, Napsu , Makela, Marko**Date:**2014**Type:**Text , Book**Full Text:**false**Reviewed:****Description:**This book is the first easy-to-read text on nonsmooth optimization (NSO, not necessarily differentiable optimization). Soving these kinds of problems plays a critical role in many industrial applications and real-world modeling systems, for example in the context of image denoising, optimal control, neural network training, data mining, ecomonics, and computational chemistry and physics. The book covers both the theory and the numerical methods used in NSO, and provides an overview of different problems arising in the field. It is organized into three parts: 1. convex and nonconvex analysis and the theory of NSO; 2. test problems and practical applications; 3. a guide to NSO software. The book is ideal for anyone teaching or attending NSO courses. As an accessible introduction to the field, it is also well suited as an independent learning guide for practitioners already familiar with the basics of optimization.

Optimal operation of a multi-quality water distribution system with changing turbidity and salinity levels in source reservoirs

- Mala-Jetmarova, Helena, Barton, Andrew, Bagirov, Adil

**Authors:**Mala-Jetmarova, Helena , Barton, Andrew , Bagirov, Adil**Date:**2014**Type:**Text , Conference proceedings**Relation:**http://purl.org/au-research/grants/arc/LP0990908**Relation:**16th International Conference on Water Distribution System Analysis, WDSA 2014; Bari, Italy; 14th-17th July 2014**Full Text:****Description:**Impact of water quality conditions in sources on the optimal operation of a regional multiquality water distribution system is analysed. Three operational objectives are concurrently minimised, being pump energy costs, turbidity and salinity deviations at customer nodes. The optimisation problem is solved using GANetXL (NSGA-II) linked with EPANet. The example network incorporates scenarios with different water quality in sources. It was discovered that two types of tradeoffs, competing and non-competing, exist between the objectives and that the type of tradeoff is not unique between a particular pair of objectives across scenarios. The findings may be used for system operational planning.

**Authors:**Mala-Jetmarova, Helena , Barton, Andrew , Bagirov, Adil**Date:**2014**Type:**Text , Conference proceedings**Relation:**http://purl.org/au-research/grants/arc/LP0990908**Relation:**16th International Conference on Water Distribution System Analysis, WDSA 2014; Bari, Italy; 14th-17th July 2014**Full Text:****Description:**Impact of water quality conditions in sources on the optimal operation of a regional multiquality water distribution system is analysed. Three operational objectives are concurrently minimised, being pump energy costs, turbidity and salinity deviations at customer nodes. The optimisation problem is solved using GANetXL (NSGA-II) linked with EPANet. The example network incorporates scenarios with different water quality in sources. It was discovered that two types of tradeoffs, competing and non-competing, exist between the objectives and that the type of tradeoff is not unique between a particular pair of objectives across scenarios. The findings may be used for system operational planning.

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