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This paper presents an efficient optimization procedure to find the optimal shapes of double curvature  arch  dams  considering  fluid–structure  interaction  subject  to  earthquake  loading. The optimization is carried out using a combination of the magnetic charged system search, big bang-big crunch algorithm and artificial neural network methods. Performing the finite element  analysis  during  the  optimization  process  is  time  consuming.  Back  propagation neural  network  is  utilized  to  reduce  the  computational  burden.  A  real-world  arch  dam  is considered as a numerical example to demonstrate the efficiency of the proposed method. The numerical results reveal the computational advantages of the new method for optimal 
design of arch dams.

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For optimization of real-world arch dams, it is unavoidable to consider two or more conflicting objectives. This paper employs two multi-objective differential evolution algorithms (MoDE) in combination of a parallel working MATLAB-APDL code to obtain a set of Pareto solutions for optimal shape of arch dams. Full dam-reservoir interaction subjected to seismic loading is considered. A benchmark arch dam is then examined as the numerical example. The numerical results are compared to show the performance of the MoDE methods.

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Best Management Practices (BMPs) are implemented in a watershed to reduce the amount of non-point source pollutants transported to water bodies. However, an optimization algorithm is required to choose the efficient type, size, and location of BMPs for application in a watershed for improving the water quality. In this study, the Charged System Search, a well-known and powerful meta-heuristic optimization algorithm, as an optimization model and a semi-distributed hydrological model i.e. Soil and Water Assesment Tool (SWAT) were coupled to obtain cost-effective combination of different BMPs. To demonstrate the performance and applicability of the coupled model, it was utilized to Sofichai watershed upstream of the Alavian Reservoir in the northwestern part of Iran to compare four reduction levels of sediment, nitrate nitrogen and phosphate phosphorous loads at the watershed outlet.

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This paper utilizes the Colliding Bodies of Optimization (CBO), Enhanced Colliding Bodies of Optimization (ECBO) and Vibrating Particles System (VPS) algorithms to optimize the reservoir system operation. CBO is based on physics equations governing the one-dimensional collisions between bodies, with each agent solution being considered as an object or body with mass and ECBO utilizes memory to save some historically best solutions and uses a random procedure to escape from local optima. VPS is based on simulating free vibration of single degree of freedom systems with viscous damping. To evaluate the performance of these three recent population-based meta-heuristic algorithms, they are applied to one of the most complex and challenging issues related to water resource management, called reservoir operation optimization problems. Hypothetical 4 and 10-reservoir systems are studied to demonstrate the effectiveness and robustness of the algorithms. The aim is on discovering the optimum mix of releases, which will lead to maximum benefit generation throughout the system. Comparative results show the successful performance of the VPS algorithm in comparison to the CBO and its enhanced version.