. In this paper, an optimization model is introduced for hydraulic analysis of water distribution networks using a meta-heuristic method called Differential Evolution (DE) algorithm. In this methodology, there is no need to solve linear systems of equations...

Differential Evolution (DE) is one of the most successful and powerful evolutionary algorithms for global

optimization problem. The most important operator in this algorithm is mutation operator which parents are selected randomly...

separately. Furthermore, to improve sampling the search space, the algorithm uses a combination of Cauchy and Gaussian distributions. To show the algorithm’s viability, we use Differential Evolution (DE), CEAs and PSO on some well-known benchmark functions...

Differential Evolution (DE) proved to be one of

the most successful evolutionary algorithms for global

optimization purposes in continuous problems. The core

operator in DE is a mutation which can provide the algorithm...

Here, we give a two-phase algorithm based on integrating differential evolution (DE) algorithm with modi ed hybrid genetic algorithm (MHGA) for solving the associated nonlinear programming problem of a

nonlinear optimal control problem...

charge and discharge storage devices that provides the

opportunity to benefit from market price fluctuations. The

presented optimization framework is based on Multiobjective

Particle Swarm Optimization (MOPSO) approach which adopts

Differential...

exploit an alternative reformulation of differential evolution optimization method to compute the capacity for channels

with causal side information at the transmitter, and introduce an efficient algorithm to compute the capacity of these

channels. Also, we...

Often parameters in hydrologic models cannot be measured directly and can only be inferred by a

calibration process. This work addresses the application and comparison of three parameter uncertainty

methods ...