The scarcity of water resources nowadays lays stress on researchers to develop strategies aiming at making the best benefit currently available resources. One these is ensuring that reliable and near-optimum designs distribution systems (WDSs) are achieved. Designing WDSs a discrete combinatorial NP-hard optimization problem, its complexity increases when more objectives added. Among many exist...

In this paper, the influence of -dominance on Multi-objective Particle Swarm Optimization (MOPSO) methods is studied. The most important role of dominance is to bound the number of non-dominated solutions stored in the archive (archive size), which has influences on computational time, convergence and diversity of solutions. Here, -dominance is compared with the existing clustering technique fo...

In the present study, multi-objective optimization of centrifugal pumps is performed at three steps. At the first step, η and NPSHr in a set of centrifugal pump are numerically investigated using commercial software. Two meta-models based on the evolved group method of data handling (GMDH) type neural networks are obtained, at the second step, for modeling of η and NPSHr with respect to geometr...

This paper proposed a optimal strategy for coordinated operation of electric vehicles (EVs) charging and discharging with wind-thermal system. By aggregating a large number of EVs, the huge total battery capacity is sufficient to stabilize the disturbance of the transmission grid. Hence, a dynamic environmental dispatch model which coordinates a cluster of charging and discharging controllable ...

The decision tree is a popular and widely-used classification model. The two main objectives in decision tree induction are accurate predictions for unseen instances and human comprehensibility. In this paper, we use multiobjective optimization for the evolution of decision tree classifiers that are efficient both with respect to classification accuracy and classifier complexity. Simpler decisi...

In this paper, a new dynamic self-adaptive multi-objective particle swarm optimization (DSAMOPSO) method is proposed to solve binary-state multi-objective reliability redundancy allocation problems (MORAPs). A combination of penalty function and modification strategies is used to handle the constraints in the MORAPs. A dynamic self-adaptive penalty function strategy is utilized to handle the co...

Utilising molten salt as coolant instead of carbon dioxide in traditional advanced gas-cooled reactors (AGRs) can potentially increase their core power density, simplify the safety case and shorten time needed for development fluoride-salt-cooled high-temperature reactor (FHR). However, change has a strong impact on system behaviour. Therefore, new type fuel assembly is required. design affecte...

This paper proposes an integrated approach to arrive at optimal build orientations, simultaneously minimizing surface roughness ’Ra’ and build time ’T ’, for object manufacturing in SLS process. The optimization task is carried out by two popularly known multi-objective evolutionary optimizers NSGA-II (non-dominated sorting genetic algorithm) and MOPSO (multi-objective particle swarm optimizer)...

in this paper, the gain in ld-celp speech coding algorithm is predicted using three neural models, that are equipped by genetic and particle swarm optimization (pso) algorithms to optimize the structure and parameters of neural networks. elman, multi-layer perceptron (mlp) and fuzzy artmap are the candidate neural models. the optimized number of nodes in the first and second hidden layers of el...

Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evalu...