Calculation of optimal induction heater capacitance based on the smart bacterial foraging algorithm

The forward kinematic of the 3-RPR parallel manipulator is solved using a hybrid meta-heuristic technique where the simulated annealing algorithm replaces the mutation operator in a genetic algorithm. The results from the hybrid meta-heuristic approach is compared with the standard simulated annealing and genetic algorithm. The results show that the simulated annealing algorithm outperforms genetic algorithm in terms of computation time and overall accuracy of the solution. The hybrid meta-heuristic search algorithm shows better performance than the standard genetic algorithm.

Design Method of an Optimal Induction Heater Capacitance for Maximum Power Dissipation and Minimum Power Loss Caused by ESR

A variable-grouping based genetic algorithm for large-scale integer programming

This thesis focuses on the development and analysis of general methods for the design of optimal discrete PID control strategies for multivariable glass furnace processes, where standard genetic algorithms (SGAs) are applied to optimise specially formulated objective functions. Furthermore, a strong emphasis is given on the realistic model parameters identi cation method, which is illustrated to be applicable to a wide range of higher order model parameters identi cation problems. A complete, realistic and continuous excess oxygen model with nonlinearity effect was developed and the model parameters were identified. The developed excess oxygen model consisted of three sub-models to characterise the real plant response. The developed excess oxygen model was evaluated and compared with real plant dynamic response data, which illustrated the high degree of accuracy of the developed model. A new technique named predetermined time constant approximation was proposed to make an assumption on the initial value of a predetermined time constant, whose motive is to facilitate the SGAs to explore and exploit an optimal value for higher order of continuous model's parameters identi cation. Also, the proposed predetermined time constant approximation technique demonstrated that the population diversity is well sustained while exploring the feasible search region and exploiting to an optimal value. In general, the proposed method improves the SGAs convergence rate towards the global optimum and illustrated the effectiveness. An automatic tuning of decentralised discrete PID controllers for multivariable processes, based on SGAs, was proposed. The main improvement of the proposed technique is the ability to enhance the control robustness and to optimise discrete PID parameters by compensating the loop interaction of a multivariable process. This is attained by adding the individually optimised objective function of glass temperature and excess oxygen processes as one objective function, to include the total effect of the loop interaction by applying step inputs on both set points, temperature and excess oxygen, at two different time periods in one simulation. The effectiveness of the proposed tuning technique was supported by a number of simulation results using two other SGAs conventional tuning techniques with 1st and 2nd order control oriented models. It was illustrated that, in all cases, the resulting discrete PID control parameters completely satisfied all performance specifications. A new technique to minimise the fuel consumption for glass furnace processes while sustaining the glass temperature is proposed. This proposed technique is achieved by reducing the excess oxygen within the optimum thermal efficiency region within 1.7% to 3.2%, which is approximately equal to about 10% to 20% of excess air. Therefore, by reducing the excess oxygen set point within the optimum region, 2.45% to 2%, the fuel consumption is minimised from 0:002942kg/sec to 0:002868kg/sec while the thermal efficiency of the glass temperature is sustained at the desired set point (1550K). In addition, a reduction in excess oxygen within methane combustion guidelines will assure that undesirable emissions are in control throughout the combustion process. The efficiencies of the proposed technique were supported by a number of simulation results applying the three SGAs controller tuning techniques. It was illustrated that, in all cases, the fraction of excess oxygen reduction results in a great minimisation of fuel consumption over long plant operating periods.

Distributed generations (DGs) have been constantly incorporating into the distribution systems. The optimal planning (sizing and sitting) of the DGs by applying harmony search (HS)-based hybrid genetic algorithm integrated adaptive particle swarm optimization (GA-APSO) technique is exhibited in this work. The fitness evolution function has investigated as the multi-objective function (FMO), which depends on the five significant indexes such as active power loss, reactive power loss, total cost generation index, voltage deviation, and load balancing index. The ideal solution has attained by minimizing the multi-objective (MO) fitness function by deploying HS-based GA-PSO strategy. The projected work will be implemented on IEEE standard 33- and 69-bus radial distribution networks. The performance of the suggested algorithm has analyzed dependent on the five measures, such as active and reactive power loss, voltage profile improvement, reduction in voltage deviation, and cost of operation of generation. The legitimacy of the assessed outcomes has affirmed by comparing with some of the notable optimization algorithms.

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.

Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation

To overcome the disadvantages of traditional genetic algorithms, which easily fall to local optima, this paper proposes a hybrid genetic algorithm based on information entropy and game theory. First, a calculation of the species diversity of the initial population is conducted according to the information entropy by combining parallel genetic algorithms, including using the standard genetic algorithm (SGA), partial genetic algorithm (PGA) and syncretic hybrid genetic algorithm based on both SGA and PGA for evolutionary operations. Furthermore, with parallel nodes, complete-information game operations are implemented to achieve an optimum for the entire population based on the values of both the information entropy and the fitness of each subgroup population. Additionally, the Rosenbrock, Rastrigin and Schaffer functions are introduced to analyse the performance of different algorithms. The results show that compared with traditional genetic algorithms, the proposed algorithm performs better, with higher optimization ability, solution accuracy, and stability and a superior convergence rate.

Previous studies have shown that external loading increases the power output measured during stair climbing. However, it was noted in an earlier study that stairtreads form mechanical contraints which limit the extent to which a subject can be externally loaded, and, thereby, make it impossible to observe maximal power output for this type of activity. The purpose of this study was to compare the effects of external loading upon power output when running up stairs or a ramp. Since a ramp is free of the mechanical constraints of stairtreads, it was felt that higher power output values would be achieved using the ramp, and that it would be possible to observe an asymptote in power output which could not be obtained for stair climbing. Seven male subjects performed maximal ramp and stair climbing tests under five experimental loading conditions (no external load, 10.1, 19.2, 24.2, and 29.2 kg). For the ramp, it was possible to employ a sixth loading condition of 34.2 kg. For stair climbing, the mean (+/- SD) power output values under the five experimental conditions were 16.6 +/- 0.7, 17.3 +/- 1.3, 18.5 +/- 1.0, 18.6 +/- 1.5, and 18.9 +/- 1.7 W X kg-1, respectively. In contrast, the mean (+/- SD) power output values observed while running up the ramp were 18.8 +/- 1.4, 19.9 +/- 1.6, 20.5 +/- 1.6, 20.1 +/- 2.1, 20.3 +/- 2.1, and 19.8 +/- 1.9 W X kg-1, respectively. At each experimental condition, the differences between the ramp and stairs was significant (P less than 0.05).(ABSTRACT TRUNCATED AT 250 WORDS)

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.

Parameters of support vector machines (SVM) which is optimized by standard genetic algorithm is easy to trap into the local minimum, in order to get the optimal parameters of support vector machine, this paper proposed a parameters optimization method for support vector machines based on improved genetic algorithm, the simulation experiment is carried out on 5 benchmark datasets. The simulation show that the proposed method not only can assure the classification precision, but also can reduce training time markedly compared with standard genetic algorithm. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.3182

This paper presents the prediction of the output power of a solar power plant based on the nearest neighbor regression method. Data from a 50 MW solar power plant in China with solar radiation, ambient temperature, pressure, and humidity values and corresponding output power values from 01/01/2019 to 12/31/2020 with 15-minute intervals were used to create the output power forecast. Before using the data, they were cleaned of outliers using the standard interquartile range method, data points were divided into test and training groups, and feature scaling was applied using the standardization method to correctly calculate the Euclidean distance between data points. The application of clustering of weather parameters by the k-means method is proposed, which allows for individual selection of the number of neighbors for each cluster and to exclude the influence of points of one cluster on the number of neighbors of another cluster. The number of clusters is selected by determining the silhouette coefficient, the training group of weather parameters with their corresponding output power values is distributed among clusters based on the Euclidean distance to the centroid of the clusters. The test group of weather parameters is divided into clusters, after which forecasting by the method of nearest neighbors takes place within each cluster separately. The output power of the solar power plant is calculated as the weighted arithmetic average of the neighbors of each point of the test group. At the end of the algorithm, the sequence of points of weather parameters of the test group is restored and a time series of the output power forecast is created. The proposed algorithm made it possible to reduce the MSE, RMSE, MAPE, MAE forecast errors for 1 day by 0.5348, 0.2265, 0.38%, 0.1448, respectively, for 7 days, the errors became smaller by 0.1992, 0.0384, 0.1%, 0.0193, respectively. As a result, the relative error in forecasting for 24 hours is 4.22%.

Genetic algorithm is a search algorithm based on genetic mechanism and natural selection. It has been widely applied to research fields including image processing field. The paper improves standard genetic algorithm and improves the arithmetic speed of the algorithm, which achieves better image restoration effect. And the paper compares the image restoration quality of traditional algorithm, standard genetic algorithm and improved genetic algorithm to prove the feasibility of applying genetic algorithm to image restoration.

Guest Editorial

Distributed generation (DG) is integrated in a passive distribution system to reduce power loss, improve voltage profile, and increase power output. To reap the most benefits of the distribution system, the best location and appropriate DG size must be determined. This paper presents a hybrid Grey wolf Optimizer (GWO) and Particle swarm optimization (PSO) approach for determining the best placement and DG size while considering a multi-objective function that includes active and reactive power loss minimization as well as voltage profile enhancement. Dilla distribution system was used as a case study and the weighted technique was used to convert to a single objective function while taking into account multiple constraints such as bus voltage limit, DG output limit, and branch current limit. DG penetration is limited to up 60% of the total active load on the feeder and a forward–backward sweep load flow algorithm was used to generate the load flow solutions. The findings of the study show that combining three PV-DGs (Case 3) is the best way to improve voltage profile and minimize losses. In addition, the proposed hybrid GWO-PSO algorithm performed better compared to the other four algorithms (Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Particle swarm optimization (PSO), and sine cosine algorithm (SCA)) in terms of achieving the best multi-objective function (MOF) outcome.