Power Distribution Optimization Based on Demand Respond with Improved Multi-Objective Algorithm in Power System Planning

In this article, a novel dynamic economic load dispatch with emission based on a multi-objective model (MODEED) considering demand side management (DSM) is presented. Moreover, the investigation and evaluation of impacts of DSM for the next day are considered. In other words, the aim of economical load dispatch is the suitable and optimized planning for all power units considering different linear and non-linear constrains for power system and generators. In this model, different constrains such as losses of transformation network, impacts of valve-point, ramp-up and ramp-down, the balance of production and demand, the prohibited areas, and the limitations of production are considered as an optimization problem. The proposed model is solved by a novel modified multi-objective artificial bee colony algorithm (MOABC). In order to analyze the effects of DSM on the supply side, the proposed MODEED is evaluated on different scenarios with or without DSM. Indeed, the proposed MOABC algorithm tries to find an optimal solution for the existence function by assistance of crowding distance and Pareto theory. Crowding distance is a suitable criterion to estimate Pareto solutions. The proposed model is carried out on a six-unit test system, and the obtained numerical analyses are compared with the obtained results of other optimization methods. The obtained results of simulations that have been provided in the last section demonstrate the higher efficiency of the proposed optimization algorithm based on Pareto criterion. The main benefits of this algorithm are its fast convergence and searching based on circle movement. In addition, it is obvious from the obtained results that the proposed MODEED with DSM can present benefits for all consumers and generation companies.

Directionally-Enhanced Binary Multi-Objective Particle Swarm Optimisation for Load Balancing in Software Defined Networks

Various aspects of task execution load balancing of Internet of Things (IoTs) networks can be optimised using intelligent algorithms provided by software-defined networking (SDN). These load balancing aspects include makespan, energy consumption, and execution cost. While past studies have evaluated load balancing from one or two aspects, none has explored the possibility of simultaneously optimising all aspects, namely, reliability, energy, cost, and execution time. For the purposes of load balancing, implementing multi-objective optimisation (MOO) based on meta-heuristic searching algorithms requires assurances that the solution space will be thoroughly explored. Optimising load balancing provides not only decision makers with optimised solutions but a rich set of candidate solutions to choose from. Therefore, the purposes of this study were (1) to propose a joint mathematical formulation to solve load balancing challenges in cloud computing and (2) to propose two multi-objective particle swarm optimisation (MP) models; distance angle multi-objective particle swarm optimization (DAMP) and angle multi-objective particle swarm optimization (AMP). Unlike existing models that only use crowding distance as a criterion for solution selection, our MP models probabilistically combine both crowding distance and crowding angle. More specifically, we only selected solutions that had more than a 0.5 probability of higher crowding distance and higher angular distribution. In addition, binary variants of the approaches were generated based on transfer function, and they were denoted by binary DAMP (BDAMP) and binary AMP (BAMP). After using MOO mathematical functions to compare our models, BDAMP and BAMP, with state of the standard models, BMP, BDMP and BPSO, they were tested using the proposed load balancing model. Both tests proved that our DAMP and AMP models were far superior to the state of the art standard models, MP, crowding distance multi-objective particle swarm optimisation (DMP), and PSO. Therefore, this study enables the incorporation of meta-heuristic in the management layer of cloud networks.

An enhanced Bouma model fits a hundred people’s visual crowding

ABSTRACTCrowding is the failure to recognize an object due to surrounding clutter. Its strength varies across the visual field and individuals. To characterize the statistics of crowding—ultimately to relate psychophysics of crowding to physiology—we measured radial crowding distance and acuity of 105 observers along the four cardinal meridians of the visual field. Fitting the well-known Bouma law — crowding distance depends linearly on radial eccentricity — explains 52% of the variance in log crowding distance, cross-validated. Our enhanced Bourma model, with factors for observer, meridian, and target kind, explains 72% of the variance, again cross-validated. The meridional factors confirm previously reported asymmetries. We find a 0.62 horizontal:vertical advantage, a 0.92 lower:upper advantage, and a 0.82 right:left advantage. Crowding distance and acuity have a correlation of 0.41 at the fovea, which drops to 0.23 at ±5 deg along the horizontal midline. Acuity and crowding represent the size and spacing limits of perception. Since they are dissociated in clinical populations (Song et al., 2014; Strappini et al., 2017) and shown here to be only moderately correlated in our sample of mostly university students, clinical testing to predict real-world performance should consider measuring both. In sum, enhancing the Bouma law with terms for meridian, observer, and target kind provides an excellent fit to our 105-person survey of crowding.