Noncrossover Dither Creeping Mutation-Based Genetic Algorithm for Pipe Network Optimization

Ceylon [Sri Lanka]ColorVolume 9, Page 1

Dynamically expanding choice-table approach to genetic algorithm optimization of water distribution systems

This paper proposes a modified genetic algorithm (GA) for optimization of water distribution systems. A method of dynamically expanding the pipe-choice-table selections and reducing the number of decision variables is introduced, which occurs during a GA run. On the basis of the progressive selection, an initially reduced choice table for each decision variable is allowed to dynamically expand, and then the number of decision variables is gradually reduced. This process enables the GA search to concentrate on promising regions of the search space. The dynamically expanding choice-table genetic algorithm (GA DECT) has been applied to a benchmark case study, the New York Tunnels Problem. The results obtained show that the GA DECT yields a superior performance in terms of solution quality and computational efficiency. © 2011 American Society of Civil Engineers.Feifei Zheng, Angus R. Simpson, and Aaron C. Zecchi

Noncrossover dither creeping mutation-based genetic algorithm for pipe network optimization

A non-crossover dither creeping mutation-based genetic algorithm (CMBGA) for pipe network optimization has been developed and is analyzed. This CMBGA differs from the classic GA optimization in that it does not utilize the crossover operator, but instead it only uses selection and a proposed dither creeping mutation operator. The creeping mutation rate in the proposed dither creeping mutation operator is randomly generated in a range throughout a GA run rather than being set to a fixed value. In addition, the dither mutation rate is applied at an individual chromosome level rather than at the generation level. The dither creeping mutation probability is set to take values from a small range that is centered about 1/ND (where ND=number of decision variables of the optimization problem being considered). This is motivated by the fact that a mutation probability of approximately 1/ND has been previously demonstrated to be an effective value and is commonly used for the GA. Two case studies are used to investigate the effectiveness of the proposed CMBGA. An objective of this paper is to compare the performance of the proposed CMBGA with four other GA variants, and other published results. The results show that the proposed CMBGA exhibits considerable improvement over the considered GA variants, and comparable performance with respect to other previously published results. A big advantage of CMBGA is its simplicity and that it requires the tuning of fewer parameters compared with other GA variants.Feifei Zheng, Aaron C. Zecchin, Angus R. Simpson and Martin F. Lamber

Coupled binary linear programming–differential evolution algorithm approach for water distribution system optimization

A coupled binary linear programming-differential evolution (BLP-DE) approach is proposed in this paper to optimize the design of water distribution systems (WDSs). Three stages are involved in the proposed BLP-DE optimization method. In the first stage, the WDS that is being optimized is decomposed into trees and the core using a graph algorithm. Binary linear programming (BLP) is then used to optimize the design of the trees during the second stage. In the third stage, a differential evolution (DE) algorithm is utilized to deal with the core design while incorporating the optimal solutions for the trees obtained in the second stage, thereby yielding near-optimal solutions for the original whole WDS. The proposed method takes advantage of both BLP and DE algorithms: BLP is capable of providing global optimal solution for the trees (no loops involved) with great efficiency, while a DE is able to efficiently generate good quality solutions for the core (loops involved) with a reduced search space compared to the original full network. Two benchmark WDS case studies and one real-world case study (with multiple demand loading cases) with a number of decision variables ranging from 21 to 96 are used to verify the effectiveness of the proposed BLP-DE optimization approach. Results show that the proposed BLP-DE algorithm significantly outperforms other optimization algorithms in terms of both solution quality and efficiency.Feifei Zheng, Angus R. Simpson and Aaron C. Zecchi

Ant colony optimization for design of water distribution systems

During the last decade, evolutionary methods such as genetic algorithms have been used extensively for the optimal design and operation of water distribution systems. More recently, ant colony optimization algorithms ~ACOAs!, which are evolutionary methods based on the foraging behavior of ants, have been successfully applied to a number of benchmark combinatorial optimization problems. In this paper, a formulation is developed which enables ACOAs to be used for the optimal design of water distribution systems. This formulation is applied to two benchmark water distribution system optimization problems and the results are compared with those obtained using genetic algorithms ~GAs!. The findings of this study indicate that ACOAs are an attractive alternative to GAs for the optimal design of water distribution systems, as they outperformed GAs for the two case studies considered both in terms of computational efficiency and their ability to find near global optimal solutions.Holger R. Maier, Angus R. Simpson, Aaron C. Zecchin, Wai Kuan Foong, Kuang Yeow Phang, Hsin Yeow Seah and Chan Lim Ta

Determination of the linear frequency response of single pipelines using persistent transient excitation: a numerical investigation

The linear frequency response of a fluid-filled pipeline extracted by fluid transient waves can be used to detect leaks in pipelines. This research conducts numerical analysis on how to accurately determine the linear frequency response diagram (FRD) of single pipelines using persistent transient pressure signals. Two types of persistent signals, the maximum-length binary sequence and the inverse-repeat sequence (IRS), are compared in terms of the accuracy in estimating the linear response of a pipeline at resonant frequencies. The IRS is found to be more appropriate for identifying the linear portion of the FRD of a pipeline, since its antisymmetric property can supress part of the nonlinear response. Numerical simulations are conducted to verify the findings. © 2013 © 2013 International Association for Hydro-Environment Engineering and Research.Jinzhe Gong, Angus R. Simpson, Martin F. Lambert & Aaron C. Zecchi

Ant colony optimization applied to water distribution system design: Comparative study of five algorithms

Water distribution systems WDSs are costly infrastructure, and much attention has been given to the application of optimization methods to minimize design costs. In previous studies, ant colony optimization ACO has been found to perform extremely competitively for WDS optimization. In this paper, five ACO algorithms are tested: one basic algorithm ant system and four more advanced algorithms ant colony system, elitist ant system, elitist-rank ant system ASrank , and max-min ant system MMAS . Experiments are carried out to determine their performance on four WDS case studies, three of which have been considered widely in the literature. The findings of the study show that some ACO algorithms are very successful for WDS design, as two of the ACO algorithms MMAS and ASrank outperform all other algorithms applied to these case studies in the literature.Aaron C. Zecchin, Holger R. Maier, Angus R. Simpson, Michael Leonard, and John B. Nixo

Application of two ant colony optimisation algorithms to water distribution system optimisation

Water distribution systems (WDSs) are costly infrastructure in terms of materials, construction, maintenance, and energy requirements. Much attention has been given to the application of optimisation methods to minimise the costs associated with such infrastructure. Historically, traditional optimisation techniques have been used, such as linear and non-linear programming, but within the past decade the focus has shifted to the use of heuristics derived from nature (HDNs), for example Genetic Algorithms, Simulated Annealing and more recently Ant Colony Optimisation (ACO). ACO, as an optimisation process, is based on the analogy of the foraging behaviour of a colony of searching ants, and their ability to determine the shortest route between their nest and a food source. Many different formulations of ACO algorithms exist that are aimed at providing advancements on the original and most basic formulation, Ant System (AS). These advancements differ in their utilisation of information learned about a search-space to manage two conflicting aspects of an algorithm's searching behaviour. These aspects are termed 'exploration' and 'exploitation'. Exploration is an algorithm's ability to search broadly through the problem's search space and exploitation is an algorithm's ability to search locally around good solutions that have been found previously. One such advanced ACO algorithm, which is implemented within this paper, is the Max-Min Ant System (MMAS). This algorithm encourages local searching around the best solution found in each iteration, while implementing methods that slow convergence and facilitate exploration. In this paper, the performance of MMAS is compared to that of AS for two commonly used WDS case studies, the New York Tunnels Problem and the Hanoi Problem. The sophistication of MMAS is shown to be effective as it outperforms AS and performs better than any other HDN in the literature for both case studies considered. © 2005 Elsevier Ltd. All rights reserved.http://www.elsevier.com/wps/find/journaldescription.cws_home/623/description#descriptio

Inverse laplace transform for transient-state fluid line network simulation

Inverse Laplace transform methods have a long history in the development of time-domain fluid line models. This paper presents a study combining the new Laplace-domain input/output (I/O) model derived from the network admittance matrix with the Fourier series expansion numerical inverse Laplace transform (NILT) to serve as a time-domain simulation model. A series of theorems are presented demonstrating the stability of the I/O model, which is important for the construction of the NILT method. In the previous work by the first author, the Fourier series expansion algorithm was studied, where qualitative relationships between the parameters and numerical errors were analyzed, and reliable parameter heuristics were developed. These heuristics are used for a series of numerical examples dealing with networks of 11, 35, 51, and 94 pipes by using five different pipe models. The examples are used as the basis from which the accuracy and numerical efficiency of the proposed NILT are compared to the standard method of characteristics (MOCs) model for transient pipeline networks. Findings show that, for all case studies considered, the proposed NILT is numerically efficient for the pipe types involving convolution operations, and it is accurate for networks composed of both linear and nonlinear pipe types.Aaron C. Zecchin, Martin F. Lambert and Angus R. Simpso

Self-adaptive differential evolution algorithm applied to water distribution system optimization

Differential evolution (DE) is a relatively new technique that has recently been used to optimize the design for water distribution systems (WDSs). Several parameters need to be determined in the use of DE, including population size, N; mutation weighting factor, F; crossover rate, CR, and a particular mutation strategy. It has been demonstrated that the search behavior of DE is especially sensitive to the F and CR values. These parameters need to be fine-tuned for different optimization problems because they are generally problem-dependent. A self-adaptive differential evolution (SADE) algorithm is proposed to optimize the design of WDSs. Three new contributions are included in the proposed SADE algorithm: (1) instead of pre-specification, the control parameters of F and CR are encoded into the chromosome of the SADE algorithm, and hence are adapted by means of evolution; (2) F and CR values of the SADE algorithm apply at the individual level rather than the generational level normally used by the traditional DE algorithm; and (3) a new convergence criterion is proposed for the SADE algorithm as the termination condition, thereby avoiding pre-specifying a fixed number of generations or computational budget to terminate the evolution. Four WDS case studies have been used to demonstrate the effectiveness of the proposed SADE algorithm. The results show that the proposed algorithm exhibits good performance in terms of solution quality and efficiency. The advantage of the proposed SADE algorithm is that it reduces the effort required to fine-tune algorithm parameter values.Feifei Zheng, Aaron C. Zecchin and Angus R. Simpso