Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
A hybrid genetic algorithm for the design of water distribution networks
Engineering Applications of Artificial Intelligence
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Application of two ant colony optimisation algorithms to water distribution system optimisation
Mathematical and Computer Modelling: An International Journal
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Water distribution system design belongs to a class of large combinatorial non-linear optimization problems, involving complex implicit constraints, such as conservation of mass and energy equations, which are commonly satisfied through the use of hydraulic simulation solvers. Recently, many researchers have shifted the focus from traditional optimization methods to the use of meta-heuristic approaches for handling this complexity. This paper proposes a hybrid particle swarm optimization (PSO) and differential evolution (DE) method, linked to the hydraulic simulator, EPANET, for minimizing the cost design of water distribution systems. The performance of the proposed PSO-DE algorithm is demonstrated using three well-known benchmark water distribution system problems, the two-loop network, the Hanoi network and the New York Tunnels network. The results are compared to that of standard PSO and previously applied optimization methods. It is found that PSO-DE is a promising method for solving water distribution system design problems as it outperforms standard PSO and other algorithms previously presented in the literature for the three case studies considered.