Multi-objective optimizing for image recovering in compressive sensing

Wind power ramp events increasingly affect the integration of wind power and cause more and more problems to the safety of power grid operation in recent years. Several forecasting techniques for wind power ramp events have been reported. In this paper, the statistical scenarios forecasting method is proposed for wind power ramp event probabilistic forecasting based on the probability generating model. Multi-objective fitness functions are established considering cumulative density functions and higher order moment autocorrelation functions with respect to the consistency of distribution and timing characteristics, respectively. Parameters of probability generating model are calculated by the iterative optimization using the modified genetic algorithm with multi-objective fitness functions. A number of statistical scenarios captured bands are generated accordingly. Eventually, ramp event probability characteristics are detected from scenarios captured bands to evaluate the ramp event forecasting method. A wind plant of Bonneville Power Administration with actual wind power data is selected for calculation and statistical analysis. It is shown that statistical results with multi-objective functions are more accurate than the results with single objective functions. Moreover, the statistical scenarios forecasting method can accurately estimate the characteristics of wind power ramp events. The results verify that the proposed method can guide the generation method of statistical scenarios and forecasting models for ramp events.

Heart disease is a leading cause of death worldwide, and the need for effective predictive systems is a major source of the need to treat affected patients. This study aimed to determine how to improve the accuracy of Random Forest in predicting and classifying heart disease. The experiments performed in this study were designed to select the most optimal parameters using an RF optimization technique using GA. The Genetic Algorithm (GA) is used to optimize RF parameters to predict and classify heart disease. Optimization of the Random Forest parameter using a genetic algorithm is carried out by using the Random Forest parameter as input for the initial population in the Genetic Algorithm. The Random Forest parameter undergoes a series of processes from the Genetic Algorithm: Selection, Crossover Rate, and Mutation Rate. The chromosome that has survived the evolution of the Genetic Algorithm is the best population or best parameter Random Forest. The best parameters are stored in the hall of fame module in the DEAP library and used for the classification process in Random Forest. The optimized RF parameters are max_depth, max_features, n_estimator, min_sample_leaf, and min_sample_leaf. The experimental process performed in RF uses the default parameters, random search, and grid search. Overall, the accuracy obtained for each experiment is the default parameter 82.5%, random search 82%, and grid search 83%. The RF+GA performance is 85.83%; this result is affected by the GA parameters are generations, population, crossover, and mutation. This shows that the Genetic Algorithm can be used to optimize the parameters of Random Forest.

In this project we have tested evolutionary process on concertina motion of snake robot. Concertina motion is a type of gait in snake in which it draws its body into sine curve then motion begins by extending its head and forward parts of the body by using frictional forces. This type of gait enable snake to move in narrow spaces. Here we have designed our snake robot in Bullet-Physics environment while Hill-Climber algorithm which is a genetic algorithm has been used to optimize the Artificial Neural Network for to give our robot the evolving capability. A multi-objective Fitness Function is designed in bullet-physics engine which reinforces learning of sinusoidal curve in snake and also reinforces forward motion in it. This multi-objective function seems to be promising as compare to single-objective as it considers the shape of the robot and takes into account the distance as well. Here in our experiment the locomotion controller quickly evolved and the desired behaviors have been obtained. Concertina motion with the proposed evolving capabilities make it a good solution for designing basic motion pattern for snake robots.

Slip plays a vital role in traction control when a mobile robot traverses over soft soils. To estimate slip parameters, accurate measurement of robot velocity is particularly required. Previous related work done by the authors has adopted a single downward-looking single camera system for velocity and slip estimation [1][2]; however, such a single camera system is prone to lose accuracy when the distance between the camera and terrain is time-varying, such as traversing over uneven terrains [1]. To cope with the problem, this paper presents a robust downward-looking camera based velocity estimation approach, which can particularly be capable of identifying height variation and compensating for velocity estimation. A downward-looking stereo camera instead of previously used single camera is adopted. The camera-terrain distance can be estimated by matching same features in left and right frames. Robot velocity measured with height compensation can be more accurate, compared to estimates without it. The proposed approach has been validated through comprehensive experimental study on a lab-based test rig; and test results show good performance of the proposed approach. With the proposed method, slip estimation techniques given by [2][3] can be promisingly extended to non-flat terrains.

We demonstrate an optical profilometer composed of an optical frequency comb source, a single pixel camera, and an optoelectronic interferometer in radio frequecny range. The system enables us to measure an object with a large depth much more than a light wavelength. The wide dynamic range is achieved by the high stability of the RF signal. The single pixel camera is used for two-dimensional imaging without a mechanical scanning, and the compressive sensing technique is used to reduce the number of measurements.

A new approach to regional land use and transportation planning, which uses a genetic algorithm as an integrated optimization tool, is presented. The approach is illustrated by applying it to the Wasatch Front Metropolitan Region, which consists of four counties in the state of Utah. This genetic algorithm–-based approach was applied earlier to the twin cities of Provo and Orem in Utah, but here it is adapted to regional planning. Three issues make regional planning particularly difficult: ( a) individual cities have significant planning autonomy, ( b) the search space of possible plans is immense, and ( c) preferences between competing objectives vary among stakeholders. The approach used here addresses the first issue by the way the problem is formulated. The second issue is addressed with a genetic algorithm. Such algorithms are particularly well suited to problems with large search spaces. The third issue is addressed by using a multiobjective fitness function in the genetic algorithm. It was found that a genetic algorithm could produce a set of nondominated future land use scenarios and street plans for a region, from which regional planners can make a selection. Execution of the algorithm to produce 100 plans per generation for 100 generations took about 4 days with a high-end personal computer. Interesting trends for reducing change and traffic congestion were discovered.

Optimally designed multi-material structures offer the potential for increased functionality. The present paper describes a modeling and optimization procedure based on the finite element method (FEM) combined with an evolutionary genetic algorithm (GA). The GA offers the possibility of finding the global optimum in a multi-modal design space. This advantage, however, is counterbalanced by the high computational expense of utilizing many FEM evaluations, as is often required in structural optimization. Furthermore, in the context of multi-material optimization, the large number of material possibilities for each finite element can render the conventional GA-based optimization prohibitively costly in computational time and inconclusive. In this paper, a stepwise mesh refinement technique is presented. Coupled to the GA using a multi-objective fitness function, the method is shown to have a significantly lower computational time and leads to satisfactory designs of heterogeneous objects of arbitrary shapes. Design issues related to the use of this method are discussed and exemplified with the design of a three-dimensional heterogeneous connector.

This research analyses and discusses the use of Multiobjective fitness function to evolve Finite State Automata. Such automata can describe system’s behavior mathematically in an efficient manner. However system’s behavior must highly depend on its input-output specifications. Genetic Programming is used, and the fitness function is built to guide the evolutionary process in two different cases. First case: Single point fitness function is used where the only focus is on the correctness of the evolved automata. Second case: multiobjective fitness function is used since every real-world problem involves simultaneous optimization of several incommensurable and often competing objectives. Multiobjective optimization is defined as a problem of finding a Finite State Automata which satisfies: parsimony, efficiency, and correctness. It has been presented that for large and complex problems it is necessary to divide them into sub problem(s) and simultaneously breed both sub-program(s) and a calling program.

We previously presented a technique calibrating a Multi-Aperture Array Projector (MAAP) for performing high-speed three-dimensional (3D) surface topology measurements with structured illumination. The MAAP achieves projection rates up to 3 kHz by projecting a sequence of aperiodic sinusoidal fringes through several distinct projecting aperture channels such that up to 330 3D measurements per second could be achieved. This rapid switching between several projecting channels overcomes the projection limit of conventional off-the-shelf single aperture Digital Light Processing (DLP) or Liquid Crystal on Silicon (LCoS) technologies that typically have projection rates on the order of 100 Hz. The proposed MAAP calibration technique re-enabled photogrammetric 3D measurements to be made using a single CCD or CMOS camera where a stereo-camera setup used to be required for triangulation. The focus to reduce a stereo-configuration to a single camera system opens possibilities to decrease cost as well as form factor of such a high-speed metrology system utilizing an MAAP. For such a photogrammetric optical metrology system, intrinsic camera calibration and extrinsic setup calibration are vital to obtain high measurement performance. With the pinhole camera model assumption, intrinsic calibration is typically carried out using the standard Zhang method. However, there is currently no such standard technique that can comprehensively perform the required extrinsic calibration. In this study, the main sources of measurement error arising from extrinsic calibration of the proposed technique are discussed and investigated. Several different extrinsic calibration techniques are proposed and the resulting measurement performance evaluated.

Ultrathin metasurfaces have shown the capability to influence all aspects of light propagation. This has made them promising options for replacing conventional bulky imaging optics while adding advantageous optical properties or functionalities. We demonstrate that such metasurfaces can also be applied for single-lens three-dimensional (3-D) imaging based on a specifically engineered point-spread function (PSF). Using Huygens’ metasurfaces with high transmission, we design and realize a phase mask that implements a rotating PSF for 3-D imaging. We experimentally characterize the properties of the realized double-helix PSF, finding that it can uniquely encode object distances within a wide range. Furthermore, we experimentally demonstrate wide-field depth retrieval within a 3-D scene, showing the suitability of metasurfaces to realize optics for 3-D imaging, using just a single camera and lens system.

In a single camera single source gaze tracking system, if we can accurately estimate the 3-D coordinates of the iris center and the center of corneal surface curvature, we can reconstruct 3-D gaze direction. A novel method is proposed in this paper, different from other gaze tracking systems, the user calibration in this paper is the calibration of the iris radius, corneal curvature radius and kappa angle. Through the simulation experiments, the feasibility of the algorithm proposed in this paper is analyzed, and the error of the algorithm is relatively small.

Load balancing techniques are a typical NP-hard problem. Currently, many researchers have solved load balancing problem by considering well-known metaheuristic techniques. However, these techniques suffer from one of these issues: premature convergence, poor convergence speed, initially selected random solutions, and stuck in local optima. To handle the issues associated with existing metaheuristic techniques, in this paper, a mutation-based particle swarm optimization based load balancing technique is proposed. The proposed technique has an ability to overcome several issues associated with existing techniques such as premature convergence, poor convergence speed, initially selected random solutions, and stuck in local optima issues. Also, multi-objective fitness function is designed as a minimization problem. Multi-objective fitness function considers energy consumption, makespan, and load imbalance rate parameters. The proposed technique outperforms existing load balancing techniques in terms of makespan, speedup, communication overheads, efficiency, utilization, mean gain time, load imbalance rate, and energy consumption.

This paper deals with the application of gray wolf optimiser (GWO) based on an artificial neural network (ANN) to optimise the coupling of the Kerman combined cycle power plant (KCCPP) with multi-effect distillation desalination (MED) unit. Thermodynamic simulation of the initial power plant has been performed in the GT Pro simulator, and an extracted steam from the steam turbine was used to supply the required energy for the desalination plant. A desalination economic evaluation program called DEEP has been applied to calculate the cost of power and freshwater production. To optimise the system, a multi-objective fitness function has been defined to reach the minimum production cost and maximum performance in the dual-purpose plant. The optimisation procedure has been performed by combining an artificial neural network along with gray wolf optimiser, and the multi-objective fitness function has been predicted by a neural network at each stage of optimisation. In this analysis, the best value of 2.286 has been found for the fitness function by the gray wolf optimiser based on an artificial neural network. This fitness value has been obtained from 89.69 US$/MWh for electric power cost, 40.78% for net electric efficiency, 1.22 US$/m3 for freshwater cost, and 14 for gain output ratio. Highlight Kerman combined cycle power plant with 1912 MWe nominal power has been modelled. A multi-effect distillation desalination plant has been coupled to the power plant to supply 50,000 m3/day of freshwater. To find the optimal design of the dual-purpose plant, an artificial neural network was connected to the gray wolf optimiser algorithm. Multi-objective optimisation has been applied to increase efficiency and reduce costs in the dual-purpose plant.

Many state-of-the-art approaches to power minimisation in Networks-on-Chip (NoC) are based on the reduction of the communication paths taken by packets over the interconnect. This is often done by optimising the packet routing, the allocation of tasks that produce and consume those packets, or both. In all cases, the optimisation affects the timeliness of the packets, because changes will occur in the way resources are shared at the platform cores (as tasks are reallocated) and NoC links (as packet routes are changed). In this paper, we propose an optimisation technique that is able to minimise power dissipation without sacrificing timing constraints, thus suitable to systems with hard real-time requirements. It is based on a Genetic Algorithm (GA) that evolves chromosomes representing the mapping of tasks to cores, guided by a multi-objective fitness function that combines power estimation macromodels and real-time schedulability analysis.

Most of the generated electricity is lost in power loss while transmitting and distributing it to the consumer end. The power losses occurring in the distribution network cause deviation in voltage and lower stability due to increased load demand. The integration of multiple Distributed Generation (DG) will enable the existing radial electrical distribution network efficient by minimizing the power losses and improving the voltage profile. Metaheuristic optimization techniques provide a favorable solution for optimal location and sizing of DG in the distribution network. A novel modern metaheuristic Transient Search Optimization (TSO) algorithm, inspired by the electrical network’s transient response of storage components implemented in the proposed work. The TSO formulated optimal DGs allocation to minimize total active power loss, voltage deviation and enhance voltage stability index as minimization optimization problem satisfying various equality and inequality constraints. The installation of multiple DG units at unity, fixed, and optimal power factors were examined. The TSO algorithm’s effectiveness was tested on standard IEEE 33-bus and 69-bus radial distribution networks, including various operating events developed in the form of single and multi-objective fitness functions. The active power loss reduced to 94.29 and 94.71% for IEEE 33 and 69 bus distribution systems. The obtained results trustworthiness is confirmed by comparison with well-known optimization methods like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), combined GA/PSO, Teaching Learning Based Algorithm (TLBO), Swine influenza model-based optimization with quarantine (SIMBO-Q), Multi-Objective Harris Hawks optimizer (MOHHO) and other provided in the literature. The presented numerical studies represent the usefulness and out-performance of the proposed TSO algorithm due to its exploration and exploitation optimization mechanisms for the DG allocation problem meticulously.