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This paper aims to improve the adaptability and control performance of the controller, a proposed seeker optimization algorithm (SOA) is introduced to optimize the controller parameters of a robot manipulator.

In this paper, a traditional proportional integral derivative (PID) controller and a fuzzy logic controller are integrated to form a fuzzy PID (FPID) controller. The SOA, as a novel algorithm, is used for optimizing the controller parameters offline. There is a performance comparison in terms of FPID optimization about the SOA, the genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). The DC motor model and the experimental platform are used to test the performance of the optimized controller.

Compared with GA, PSO and ACO, this novel optimization algorithm can enhance the control accuracy of the system. The optimized parameters ensure a system with faster response speed and better robustness.

A simplified FPID controller structure is constructed and a novel SOA method for FPID controller is presented. In this paper, the SOA is applied on the controller of 5-DOF manipulator, and the validation of controllers is tested by experiments.

The purpose of this paper is to develop an optimal bidding strategy for a generation company (GenCo) in the network constrained electricity markets and to analyze the impact of network constraints and opponents bidding behavior on it.

A bi‐level programming (BLP) technique is formulated in which upper level problem represents an individual GenCo payoff maximization and the lower level represents the independent system operator's market clearing problem for minimizing customers' payments. The objective function of BLP problem used for bidding strategy by economic withholding is highly nonlinear, and there are complementarity terms to represent the market clearing. Fuzzy adaptive particle swarm optimization (FAPSO), which is a modern heuristic approach, is applied to obtain the global solution of the proposed BLP problem for single hourly and multi‐hourly market clearings. Opponents' bidding behavior is modeled with probabilistic estimation.

It is very difficult to obtain the global solution of this BLP problem using the deterministic approaches, even for a single hourly market clearing. However, the effectiveness of this new heuristic approach (FAPSO) has been established with four simulation cases on IEEE 30‐bus test system considering multi‐block bidding and multi‐hourly market clearings. The joint effect of network congestion and strategic bidding by opponents offer additional opportunities of increase in payoff of a GenCo.

FAPSO having dynamically adjusted particle swarm optimization inertia weight uses fuzzy evaluation to effectively follow the frequently changing conditions in the successive trading sessions of a real electricity market. This approach is applied to find the optimal bidding strategy of a GenCo competing with five GenCos in IEEE 30‐bus test system.

This paper is possibly the first attempt to evaluate an optimal bidding strategy for a GenCo through economic withholding in a network constrained electricity market using FAPSO.

The purpose of this paper is to develop three methods including artificial bee colony algorithm (ABC-ANN), regression and adaptive neural fuzzy inference system (ANFIS) to predict the environmental indicators for land leveling and to analysis the sensitivity of these parameters.

This paper develops three methods including artificial bee colony algorithm (ABC-ANN), regression and adaptive neural fuzzy inference system (ANFIS) to predict the environmental indicators for land leveling and to analysis the sensitivity of these parameters. So, several soil properties such as soil, cut/fill volume, soil compressibility factor, specific gravity, moisture content, slope, sand per cent and soil swelling index in energy consumption were investigated. A total of 90 samples were collected from three land areas with the selected grid size of (20 m × 20 m). Acquired data were used to develop accurate models for labor, energy (LE), fuel energy (FE), total machinery cost (TMC) and total machinery energy (TM).

By applying the three mentioned analyzing methods, the results of regression showed that, only three parameters of sand per cent, slope and soil, cut/fill volume had significant effects on energy consumption. All developed models (Regression, ANFIS and ABC-ANN) had satisfactory performance in predicting aforementioned parameters in various field conditions. The adaptive neural fuzzy inference system (ANFIS) has the most capability in prediction according to least RMSE and the highest R² value of 0.0143, 0.9990 for LE. The ABC-ANN has the most capability in prediction of the environmental and energy parameters with the least RMSE and the highest R² with the related values for TMC, FE and TME (0.0248, 0.9972), (0.0322, 0.9987) and (0.0161, 0.9994), respectively.

As land leveling with machines requires considerable amount of energy, optimizing energy consumption in land leveling operation is of a great importance. So, three approaches comprising: ABC-ANN, ANFIS as powerful and intensive methods and regression as a fast and simplex model have been tested and surveyed to predict the environmental indicators for land leveling and determine the best method. Hitherto, only a limited number of studies associated with energy consumption in land leveling have been done. In mentioned studies, energy was a function of the volume of excavation (cut/fill volume). Therefore, in this research, energy and cost of land leveling are functions of all the properties of the land including slope, coefficient of swelling, density of the soil, soil moisture, special weight and swelling index which will be thoroughly mentioned and discussed. In fact, predicting minimum cost of land leveling for field irrigation according to the field properties is the main goal of this research which is in direct relation with environment and weather pollution.

The purpose of this paper is to propose a novel design for determining the optimal hybrid fuzzy PID-controller of an active automobile suspension system, employing the gravitational search algorithm (GSA).

The hybrid fuzzy PID-controller structure is an improvement to fuzzy PID-controller by incorporating a fast learning PID-controller.

The GSA can adjust the parameters of the PID-controller to achieve the optimal performance.

The GSA may have the advantage of quick convergence, but the required computation may be intensive.

The simulation results demonstrate the effectiveness of the proposed approach on active automobile suspension system.

In order to demonstrate the theoretical guarantee of the proposed method, comparisons with particle swarm optimization or other methods has also been carried out.

Load forecasting is important to any electrical grid, but for the developing and third-world countries with power shortages, load forecasting is essential. When planed load shedding programs are implemented to face power shortage, a noticeable distortion to the load curves will happen, and this will make the load forecasting more difficult.

In this paper, a new load forecasting model is developed that can detect the effect of planned load shedding on the power consumption and estimate the load curve behavior without the shedding and with different shedding programs. A neuro-Fuzzy technique is used for the model, which is trained and tested with real data taken from one of the 11 KV feeders in Najaf city in Iraq to forecast the load for two days ahead for the four seasons. Load, temperature, time of the day and load shedding schedule for one month before are the input parameters for the training, and the load forecasting data for two days are estimated by the model.

To verify the model, the load is forecasted without shedding by the proposed model and compared to real data without shedding and the difference is acceptable.

The proposed model provides acceptable forecasting with the load shedding effect available and better than other models. The proposed model provides expected behavior of load with different shedding programs an issue helps to select the appropriate shedding program. The proposed model is useful to estimate the real demands by assuming load shedding hours to be zero and forecast the load. This is important in places suffer from grid problems and cannot supply full loads to calculate the peak demands as the case in Iraq.

This work aims to determine the best linear model using an artificial neural network (ANN) with the imperialist competitive algorithm (ICA-ANN) and ANN to predict the energy consumption for land leveling.

Using ANN, integrating artificial neural network and imperialist competitive algorithm (ICA-ANN) and sensitivity analysis (SA) can lead to a noticeable improvement in the environment. In this research, effects of various soil properties such as embankment volume, soil compressibility factor, specific gravity, moisture content, slope, sand per cent and soil swelling index on energy consumption were investigated.

According to the results, 10-8-3-1, 10-8-2-5-1, 10-5-8-10-1 and 10-6-4-1 multilayer perceptron network structures were chosen as the best arrangements and were trained using the Levenberg–Marquardt method as the network training function. Sensitivity analysis revealed that only three variables, namely, density, soil compressibility factor and cut-fill volume (V), had the highest sensitivity on the output parameters, including labor energy, fuel energy, total machinery cost and total machinery energy. Based on the results, ICA-ANN had a better performance in the prediction of output parameters in comparison with conventional methods such as ANN or particle swarm optimization (PSO)-ANN. Statistical factors of root mean square error (RMSE) and correlation coefficient (R²) illustrate the superiority of ICA-ANN over other methods by values of about 0.02 and 0.99, respectively.

A limited number of research studies related to energy consumption in land leveling have been done on energy as a function of volume of excavation and embankment. However, in this research, energy and cost of land leveling are shown to be functions of all the properties of the land, including the slope, coefficient of swelling, density of the soil, soil moisture and special weight dirt. Therefore, the authors believe that this paper contains new and significant information adequate for justifying publication in an international journal.

This study aims to present a novel optimization technique to solve the combined economic emission dispatch (CEED) problem considering transmission losses, valve-point loading effects, ramp rate limits and prohibited operating zones. This is one of the most complex optimization problems concerning power systems.

The proposed algorithm has been called advanced particle swarm optimization (APSO) and was created by applying several innovative modifications to the classic PSO algorithm. APSO performance was tested on four test systems having 14, 40, 54 and 120 generators.

The suggested modifications have improved the accuracy, convergence rate, robustness and effectiveness of the algorithm, which has produced high-quality solutions for the CEED problem.

The results obtained by APSO were compared with those of several other techniques, and the effectiveness and superiority of the proposed algorithm was demonstrated. Also, because of its superlative characteristics, APSO can be applied to many other engineering optimization problems. Moreover, the suggested modifications can be easily used in other population-based optimization algorithms to improve their performance.

The purpose of this paper is to track the maximal power of wind energy conversion system (WECS) and enhance the search capability for WECS maximum power point tracking (MPPT).

The hybrid technique is the combination of tunicate swarm algorithm (TSA) and radial basis function neural network.

TSA gets input parameters from the rectifier outputs such as rectifier direct current (DC) voltage, DC current and time. From the input parameters, it enhances the reduced fault power of rectifier and generates training data set based on the MPPT conditions. The training data set is used in radial basis function. During the execution time, it produces the rectifier reference DC side voltage that is converted to control pulses of inverter switches.

Finally, the proposed method is executed in MATLAB/Simulink site, and the performance is compared with different existing methods like particle swarm optimization algorithm and hill climb searching technique. Then the output illustrates the performance of the proposed method and confirms its capability to solve issues.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

The purpose of this paper is to address a fractional order fuzzy PID (FOFPID) control approach for solving the problem of enhancing high precision tracking performance and robustness against to different reference trajectories of a 6-DOF Stewart Platform (SP) in joint space.

For the optimal design of the proposed control approach, tuning of the controller parameters including membership functions and input-output scaling factors along with the fractional order rate of error and fractional order integral of control signal is tuned with off-line by using particle swarm optimization (PSO) algorithm. For achieving this off-line optimization in the simulation environment, very accurate dynamic model of SP which has more complicated dynamical characteristics is required. Therefore, the coupling dynamic model of multi-rigid-body system is developed by Lagrange-Euler approach. For completeness, the mathematical model of the actuators is established and integrated with the dynamic model of SP mechanical system to state electromechanical coupling dynamic model. To study the validness of the proposed FOFPID controller, using this accurate dynamic model of the SP, other published control approaches such as the PID control, FOPID control and fuzzy PID control are also optimized with PSO in simulation environment. To compare trajectory tracking performance and effectiveness of the tuned controllers, the real time validation trajectory tracking experiments are conducted using the experimental setup of the SP by applying the optimum parameters of the controllers. The credibility of the results obtained with the controllers tuned in simulation environment is examined using statistical analysis.

The experimental results clearly demonstrate that the proposed optimal FOFPID controller can improve the control performance and reduce reference trajectory tracking errors of the SP. Also, the proposed PSO optimized FOFPID control strategy outperforms other control schemes in terms of the different difficulty levels of the given trajectories.

To the best of the authors’ knowledge, such a motion controller incorporating the fractional order approach to the fuzzy is first time applied in trajectory tracking control of SP.