scispace - formally typeset
Search or ask a question
Journal ArticleDOI

Self-Adaptive PSO-GA Hybrid Model for Combinatorial Water Distribution Network Design

01 Feb 2013-Journal of Pipeline Systems Engineering and Practice (American Society of Civil Engineers)-Vol. 4, Iss: 1, pp 57-67
TL;DR: In this paper, a hybrid model PSO-GA is presented to effectively utilize local and global search capabilities of particle swarm optimization (PSO) for optimal pipe sizing in a water distribution network.
Abstract: In modern civilization, water distribution network has a substantial role in preserving the desired living standard. It has different components such as pipe, pump, and control valve to convey water from the supply source to the consumer withdrawal points. Among these elements, optimal sizing of pipes has great importance because more than 70% of the project cost is incurred on it. Unfortunately, optimal pipe sizing falls in the category of nonlinear polynomial time hard (NP-hard) problems. Hence, solid research activities march on because of two facts, namely, importance and complexity of the problem. The literature revealed that the stochastic optimization algorithms are successful in exploring the combination of least-cost pipe diameters from the commercially available discrete diameter set, but with the expense of significant computational effort. The hybrid model PSO-GA, presented in this paper aimed to effectively utilize local and global search capabilities of particle swarm optimization (...
Citations
More filters
Journal ArticleDOI
TL;DR: This survey presented a comprehensive investigation of PSO, including its modifications, extensions, and applications to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology.
Abstract: Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

836 citations


Cites background from "Self-Adaptive PSO-GA Hybrid Model f..."

  • ...Babu and Vijayalakshmi [292] presented a hybrid model PSO-GA, aimed at effectively utilizing local and global search capabilities of PSO and GA, respectively, to reduce the computational burden....

    [...]

  • ...Babu and Vijayalakshmi [292] presented a hybrid model PSO-GA, aimed at effectively utilizing local and global...

    [...]

  • ...[291], Babu and Vijayalakshmi [292], Mohan et al....

    [...]

Journal ArticleDOI
TL;DR: This study presents an effective algorithm called integrated particle swarm optimizer (iPSO) which combines favorable features of the standard PSO with an efficient concept of ‘weighted particle’ to improve its performance.
Abstract: The current investigation deals with the weight minimization of truss structures accomplishing the simultaneous shape, size, and topology optimization. In this regard, this study presents an effective algorithm called integrated particle swarm optimizer (iPSO) as an optimization tool. The iPSO combines favorable features of the standard PSO with an efficient concept of `weighted particle' to improve its performance. In addition, `improved fly-back' technique is introduced to handle the problem constraints. The proposed methodology is tested on a series of benchmark problems and the obtained results are compared with those available in the technical literature. The iPSO achieves the results which are capable of competitive with those obtained by other techniques used for simultaneous optimization of truss structures and reported in the literature. Furthermore, the relative simplicity of the formulation can be considered as one of the significant features of this method.

62 citations

Journal ArticleDOI
TL;DR: In this article, the integer discrete particle swarm algorithm is used as an optimization technique for the design of water distribution networks in order to minimize its total cost, and a new boundary condition called the billiard boundary condition is introduced.
Abstract: The integer discrete particle swarm algorithm is used as an optimization technique for the design of water distribution networks in order to minimize its total cost. Because the particle swarm is highly sensitive to its parameters and boundary conditions, the available restricted boundary conditions are applied. Also, a new boundary condition called the billiard boundary condition is introduced, which does not depend on the velocity clamping that mainly depends on human assumptions. The performance of the boundary conditions are tested under different populations, and a new initialization method by setting the initial position to one side of boundary solutions that is set to the maximum available diameter. The Newton-Raphson method is used as the hydraulic solver. The technique is applied to the optimal design of both the two-loop water distribution network, which is a well-known benchmark in the literature, and to a large-scale previously investigated two-source pipe network. The results show tha...

50 citations

Journal ArticleDOI
TL;DR: A comprehensive overview of the basic PSO algorithm search strategy and PSO’s applications and performance analysis in water resources engineering optimization problems is presented.
Abstract: Particle swarm optimization (PSO) is a stochastic population-based optimization algorithm inspired by the interactions of individuals in a social world. This algorithm is widely applied in different fields of water resources problems. This paper presents a comprehensive overview of the basic PSO algorithm search strategy and PSO’s applications and performance analysis in water resources engineering optimization problems. Our literature review revealed 22 different varieties of the PSO algorithm. The characteristics of each PSO variety together with their applications in different fields of water resources engineering (e.g., reservoir operation, rainfall–runoff modeling, water quality modeling, and groundwater modeling) are highlighted. The performances of different PSO variants were compared with other evolutionary algorithms (EAs) and mathematical optimization methods. The review evaluates the capability and comparative performance of PSO variants over conventional EAs (e.g., simulated annealing, differential evolution, genetic algorithm, and shark algorithm) and mathematical methods (e.g., support vector machine and differential dynamic programming) in terms of proper convergence to optimal Pareto fronts, faster convergence rate, and diversity of computed solutions.

43 citations

Journal ArticleDOI
TL;DR: Five models based on evolutionary algorithms (EAs) are introduced and compared for the optimization of the design and rehabilitation of water distribution networks.
Abstract: In this paper, five models based on evolutionary algorithms (EAs) are introduced and compared for the optimization of the design and rehabilitation of water distribution networks. These EAs...

36 citations

References
More filters
Journal ArticleDOI
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed The relationships between particle swarm optimization and both artificial life and genetic algorithms are described

18,439 citations

Book
01 Jan 2001
TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
Abstract: From the Publisher: Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run. · Comprehensive coverage of this growing area of research · Carefully introduces each algorithm with examples and in-depth discussion · Includes many applications to real-world problems, including engineering design and scheduling · Includes discussion of advanced topics and future research · Features exercises and solutions, enabling use as a course text or for self-study · Accessible to those with limited knowledge of classical multi-objective optimization and evolutionary algorithms The integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.

12,134 citations

Proceedings ArticleDOI
04 May 1998
TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.
Abstract: Evolutionary computation techniques, genetic algorithms, evolutionary strategies and genetic programming are motivated by the evolution of nature. A population of individuals, which encode the problem solutions are manipulated according to the rule of survival of the fittest through "genetic" operations, such as mutation, crossover and reproduction. A best solution is evolved through the generations. In contrast to evolutionary computation techniques, Eberhart and Kennedy developed a different algorithm through simulating social behavior (R.C. Eberhart et al., 1996; R.C. Eberhart and J. Kennedy, 1996; J. Kennedy and R.C. Eberhart, 1995; J. Kennedy, 1997). As in other algorithms, a population of individuals exists. This algorithm is called particle swarm optimization (PSO) since it resembles a school of flying birds. In a particle swarm optimizer, instead of using genetic operators, these individuals are "evolved" by cooperation and competition among the individuals themselves through generations. Each particle adjusts its flying according to its own flying experience and its companions' flying experience. We introduce a new parameter, called inertia weight, into the original particle swarm optimizer. Simulations have been done to illustrate the significant and effective impact of this new parameter on the particle swarm optimizer.

9,373 citations

Proceedings ArticleDOI
27 May 2001
TL;DR: Three kinds of dynamic systems are defined for the purposes of this paper and one of them is chosen for preliminary analysis using the particle swarm on the parabolic benchmark function.
Abstract: Using particle swarms to track and optimize dynamic systems is described. Issues related to tracking and optimizing dynamic systems are briefly reviewed. Three kinds of dynamic systems are defined for the purposes of this paper. One of them is chosen for preliminary analysis using the particle swarm on the parabolic benchmark function. Successful tracking of a 10-dimensional parabolic function with a severity of up to 1.0 is demonstrated. A number of issues related to tracking and optimizing dynamic systems with particle swarms are identified. Directions for future research and applications are suggested.

959 citations

Journal ArticleDOI
TL;DR: The development of a computer model GANET is described that involves the application of an area of evolutionary computing, better known as genetic algorithms, to the problem of least-cost design of water distribution networks, an efficient search method for nonlinear optimization problems.
Abstract: This paper describes the development of a computer model GANET that involves the application of an area of evolutionary computing, better known as genetic algorithms, to the problem of least-cost design of water distribution networks. Genetic algorithms represent an efficient search method for nonlinear optimization problems; this method is gaining acceptance among water resources managers/planners. These algorithms share the favorable attributes of Monte Carlo techniques over local optimization methods in that they do not require linearizing assumptions nor the calculation of partial derivatives, and they avoid numerical instabilities associated with matrix inversion. In addition, their sampling is global, rather than local, thus reducing the tendency to become entrapped in local minima and avoiding dependency on a starting point. Genetic algorithms are introduced in their original form followed by different improvements that were found to be necessary for their effective implementation in the optimization of water distribution networks. An example taken from the literature illustrates the approach used for the formulation of the problem. To illustrate the capability of GANET to efficiently identify good designs, three previously published problems have been solved. This led to the discovery of inconsistencies in predictions of network performance caused by different interpretations of the widely adopted Hazen-Williams pipe flow equation in the past studies. As well as being very efficient for network optimization, GANET is also easy to use, having almost the same input requirements as hydraulic simulation models. The only additional data requirements are a few genetic algorithm parameters that take values recommended in the literature. Two network examples, one of a new network design and one of parallel network expansion, illustrate the potential of GANET as a tool for water distribution network planning and management.

939 citations