Multi-objective Grasshopper Optimization Algorithm Research Articles

An improved multi-objective learning automata and its application in VLSI circuit design

In this paper, an improved multi-objective optimization method, based on learning automata (called IMOLA), is proposed and its performance on the design of a variety of functional circuits is investigated. The most important feature of the proposed method is to provide a suitable schedule for effective compromise between exploration and exploitation during the search process. To evaluate the capability of the proposed method on multi-objective problems, digital and analog circuits have been selected. The results show the superiority in comparison with new and common algorithms called non-dominated sorting genetic algorithm III, multi-objective multi verse optimization, adaptive multi-objective black hole algorithm, multi-objective modified inclined planes system optimization, and multi-objective grasshopper optimization algorithm. Evaluation of the results was reported in terms of power-delay-product, power-area-product, success rate, Pareto-front, multi-objective criteria, circuit variables, design constraints, runtime, and performance analysis.

Reliability aware multi-objective predictive control for wind farm based on machine learning and heuristic optimizations

In this paper, a reliability aware multi-objective predictive control strategy for wind farm based on machine learning and heuristic optimizations is proposed. A wind farm model with wake interactions and the actuator health informed wind farm reliability model are constructed. The wind farm model is then represented by training a relevance vector machine (RVM), with lower computational cost and higher efficiency. Then, based on the RVM model, a reliability aware multi-objective predictive control approach for the wind farm is readily designed and implemented by using five typical state of the art meta-heuristic evolutionary algorithms including the third evolution step of generalized differential evolution (GDE3), the multi-objective evolutionary algorithm based on decomposition (MOEA/D), the multi-objective particle swarm optimization (MOPSO), the multi-objective grasshopper optimization algorithm (MOGOA), and the non-dominated sorting genetic algorithm III (NSGA-III). The computational experimental results using the FLOw Redirection and Induction in Steady-state (FLORIS) and under different inflow wind speeds and directions demonstrate that the relative accuracy of the RVM model is more than 97%, and that the proposed control algorithm can largely reduce thrust loads (by around 20% on average) and improve the wind farm reliability while maintaining similar level of power production in comparison with a conventional predictive control approach. In addition, the proposed control method allows a trade-off between these objectives and its computational load can be properly reduced.

Designing a short-term load forecasting model in the urban smart grid system

The transition of the energy system from fossil fuel towards renewable energy (RE) is rising sharply, which provides a cleaner energy source to the urban smart grid system. However, owing to the volatility and intermittency of RE, it is challenging to design an accurate and reliable short-term load forecasting model. Recently, machine learning (ML) based forecasting models have been applied for short-term load forecasting whereas most of them ignore the importance of characteristics mining, parameters fine-tuning, and forecasting stability. To dissolve the above issues, a short-term load forecasting model is proposed that incorporates thorough data mining and multi-step rolling forecasting. To alleviate the chaos of short-term load, a de-noising method based on decomposition and reconstruction is used. Then, a phase space reconstruction (PSR) method is employed to dynamically determine the train-test ratios and neurons settings of the artificial neural network (ANN). Further, a multi-objective grasshopper optimization algorithm (MOGOA) is applied to optimize the parameters of ANNs. Case studies are conducted in the urban smart grid systems of Victoria and New South Wales in Australia. Simulation results show that the proposed model can forecast short-term load well with various measurement metrics. Multiple criterion and statistical evaluation also show the good performance of the proposed forecasting model in terms of accuracy and stability. To conclude, the proposed model achieves high accuracy and robustness, which will provide references to RE transitions and smart grid optimization, and offer guidance to sustainable city development.

A novel hybrid air quality early-warning system based on phase-space reconstruction and multi-objective optimization: A case study in China

With the haze pollution occurred frequently in recent years, establishing an effective air quality early-warning system has become the top priority. Nowadays, researchers have provided numerous methods for air quality early-warning. However, the majority of studies ignores the significance of data preprocessing and air quality evaluation while designing an early-warning system, which leads to poor forecasting performance and insufficient information. A novel hybrid air quality early-warning system that consists of three modules: data preprocessing, forecasting and air quality evaluation module is designed in this paper. Aiming at extract chaotic characteristics of raw data, a new hybrid data preprocessing strategy is firstly developed to construct a more stable series of pollutant data for forecasting. Then a multi-objective grasshopper optimization algorithm is adopted in order to enhance the forecasting capability of accuracy and stability in the forecasting module. Moreover, a fuzzy evaluation module of air quality is proposed to provide comprehensive results for the system. Through the eighteen data sets’ experimental process, the results and discussions indicate that not only the forecasting method achieves higher accuracy and stronger stability than other comparison models, but also the evaluation module provides sufficient air quality information, which forms a scientific guidance to decision-makers against air pollution.

Optimal robust disturbance observer based sliding mode controller using multi-objective grasshopper optimization algorithm to enhance power system stability

Power system stabilizer (PSS) has operated as an auxiliary controller for the synchronous generator excitation system to suppress the power system low frequency oscillations. This paper aims to design an optimal robust disturbance observer based sliding mode controller (RDO-SMC) acting as PSS to enhance the power system dynamic stability through the nonlinear control of excitation system. Indeed, the sliding disturbance observer has expeditiously estimated the accumulated consequence of unknown nonlinearities, parameter uncertainties, non-ideal dynamic models and exterior time-varying interferences in support of sliding mode controller. The sliding surface trajectory has been mathematically derived using Lyapunov theory. RDO-SMC has investigated the low frequency suppression of both the terminal voltage and angular speed. Due to multi-objective nature of the designed control problem, multi-objective grasshopper optimization algorithm (MOGOA) has been implemented to optimally tune the parameters of RDO-SMC and other understudy controllers. To verify and validate the performance of proposed controller, the analyses have been performed in single-machine infinite-bus and multi-machine power systems under different disturbance conditions. At last, the comprehensive simulation results have certainly confirmed the dynamic capability of the optimal RPO-SMC.

Fuzzy adaptive robust proportional–integral–derivative control optimized by the multi-objective grasshopper optimization algorithm for a nonlinear quadrotor

Proportional–integral–derivative is one of the most applicable control methods in industry. Although it is simple and effective in most cases, it does not provide robustness against disturbances and may not perform well in cases with uncertainties and nonlinearities. In this study, a fuzzy adaptive robust proportional–integral–derivative controller is used to control a nonlinear 4 degree-of-freedom quadrotor. An adaptation mechanism is submitted to the proportional–integral–derivative controller for updating the proportional, derivative, and integral gains of proportional–integral–derivative control. Furthermore, a sliding surface is generated and submitted to the adaptation mechanism for better regulation of proportional–integral–derivative gains. Afterward, a fuzzy engine is applied to regulate the sliding surface for better performance of the adaptive proportional–integral–derivative when there are disturbance and uncertainties. The multi-objective grasshopper optimization algorithm is implemented on the control system for the regulation of the control system parameters to minimize the error and control effort of the proposed hybrid control system. Finally, the obtained results are presented for a nonlinear 4 degree-of-freedom multi-purpose (for marine, ground, and aerial maneuvers) quadrotor system designed and built in Sirjan University of Technology, Sirjan, Iran, to assure the effectiveness of this technique.

Modelling of carbon price in two real carbon trading markets

Abstract Carbon price forecasting plays a vital role in establishing a reasonable and stable carbon market. A number of carbon price forecasting models have been developed to improve the effectiveness of the predictions. However, most of the previous studies failed to focus on the role of choosing the appropriate input features and only aimed to improve the forecasting accuracy. In this paper, a novel hybrid model based on feature selection and a multi-objective optimization algorithm is proposed for carbon price forecasting. More specifically, the main novel contributions of this study are as follows. A two-stage feature selection method is developed to obtain the appropriate input variables to enhance the forecasting ability. In addition, the weighted regularized extreme learning machine is optimized using a multi-objective optimization algorithm, named the multi-objective grasshopper optimization algorithm, which can obtain better forecasting results. To demonstrate the effectiveness of the developed carbon price forecasting model, two daily carbon price datasets that were collected from the China and European Union Emissions Trading Scheme, are used in this study. The results revealed that the mean absolute percentage errors of the proposed model utilizing data from the China and European Union Emissions Trading Scheme are 2.4923% and 0.8418%, respectively, which are lower than those of other compared models. In addition, the variances of the forecasting errors of the developed model are 1.1419 and 0.0038 for the data from the China and European Union Emissions Trading Scheme, respectively. These results reflect the superior forecasting ability of this method compared to other methods. Therefore, the proposed method is more effective than other models in carbon price forecasting.

An innovative optimal RPO-FOSMC based on multi-objective grasshopper optimization algorithm for DFIG-based wind turbine to augment MPPT and FRT capabilities

Abstract Doubly Fed Induction Generator (DFIG) with consideration of its exceptional capabilities, i.e.: variable speed operation, low mechanical stresses, and excellent power quality in limited-range speed applications is a famous kind of wind turbines. Even so, there is a challenge due to its nonlinear dynamic features and several uncertainties like unknown non-linear disturbances and parameter uncertainties. Hence, this paper proposes a novel Robust Perturbation Observer based Fractional Order Sliding Mode Controller (RPO-FOSMC) for DFIG to extract the maximum power and improve the Fault Ride-Through (FRT) capability. The strong nonlinear aerodynamics of wind turbine, the uncertain dynamic parameters of induction generator and the stochastic characteristics of wind waves are constructed in perturbation term which is estimated using the proposed RPO-FOSMC. Accordingly, the perturbation compensator provides an appropriate robustness concerning different uncertain models and attains an exceptional control capability during stochastic wind waves. Considering the inherent multi-objective nature of the nonlinear control design problem, Multi-Objective Grasshopper Optimization Algorithm (MOGOA) has been implemented to augment the robustness and dynamic performance of RPO-FOSMC. Three distinct conditions are considered to compare and analyse the fast and robust dynamic performance of optimal RPO-FOSMC against other conventional approaches. Eventually, the comprehensive simulation results have revealed and validated the exceptional dynamic capability of the suggested control strategy.

Variable weights combined model based on multi-objective optimization for short-term wind speed forecasting

Accurate and steady wind speed prediction is essential for the efficient management of wind power factories and energy systems. However, it is difficult to obtain satisfactory forecasting performance because of the characteristics of random nonlinear fluctuations inherent in wind speed variation. Considering the drawbacks of statistical models in forecasting nonlinear time series and the problem of artificial intelligence models easily falling into a local optimum, in this study, we successfully integrate the variable weighted combination theory into a new combined forecasting model that simultaneously consists of three disparate hybrid models based on the decomposition technology. Moreover, the extreme learning machine optimized by the multi-objective grasshopper optimization algorithm is adopted to integrate all the forecasting results derived from each hybrid model to further enhance the forecasting accuracy. In this study, we consider a case study that employs several authentic wind speed data aggregates of Shandong wind farms for an evaluation of the forecasting performance of the proposed combined model. The experimental results reveal that this proposed model surpasses the contrasted benchmark models and is satisfactory for intellective grid programs.

Maximizing lifetime of large-scale wireless sensor networks using multi-objective whale optimization algorithm

The sink nodes in large-scale wireless sensor networks (LSWSNs) are responsible for receiving and processing the collected data from sensor nodes. Identifying the locations of sink nodes in LSWSNs play a vital role in term of saving energy. Furthermore, sink nodes have extremely extra resources such as large memory, powerful batteries, long-range antenna, etc. This paper proposes a multi-objective whale optimization algorithm (MOWOA) to determine the lowest number of sink nodes that cover the whole network. The major aim of MOWOA is to reduce the energy consumption and prolongs the lifetime of LSWSNs. To achieve these objectives, a fitness function has been formulated to decrease energy consumption and maximize the network’s lifetime. The experimental results revealed that the proposed MOWOA achieved a better efficiency in reducing the total power consumption by 26% compared with four well-known optimization algorithms: multi-objective grasshopper optimization algorithm, multi-objective salp swarm algorithm, multi-objective gray wolf optimization, multi-objective particle swarm optimization over all networks sizes.

A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting

Short-term wind speed forecasting plays an important role in wind power generation and considerably contributes to decisions regarding control and operations. In order to improve the accuracy of wind speed forecasting, a large number of prediction methods have been proposed. However, existing prediction models ignore the role of data preprocessing and are susceptible to various limitations of the single individual model that can lead to low prediction accuracy. In this study, a developed combined model is proposed, including complete ensemble empirical mode decomposition with adaptive noise—a multi-objective grasshopper optimization algorithm based on a no-negative constraint theory—and several single models, including four neural network models and a linear model, to achieve accurate prediction results. The novel combined model considers the linear and nonlinear characteristics of the sequence, successfully overcomes the limitations of the single model, and obtains accurate and stable prediction results. In order to test the performance of combined model, the wind speed sequence of a wind farm from China is used for experiments and discussions. The results of the experiments and discussions show that the novel combined model has better forecasting performance than traditional prediction models.

Harmonic analysis of hybrid renewable microgrids comprising optimal design of passive filters and uncertainties

In this paper, the applications of passive filters in grid-connected and isolated hybrid renewable microgrid are addressed. The single-tuned filters are engaged to reduce the total harmonic distortion (THD) at the point of common connection with keeping total demand distortions below the maximum allowable limits. The total cost of proposed passive filters along with THD define the adapted bi-objectives to be minimized simultaneously provided that set of operating inequality constraints are satisfied. A multi-objective grasshopper optimization algorithm (MOGOA) is applied in this regard. The best answers are picked up carefully among the Pareto solutions by technique for order of preference by similarity to ideal solution (TOPSIS) procedures. Harmonic analysis is performed to examine the critical frequencies in many instants before and after installing passive filters. The effects of harmonics on the motor and wind generator torques are analysed and discussed. In addition, the system power factor is improved. Various scenarios regarding the operations of the grid under study are investigated comprising uncertainties of renewable power sources. The reductions in torque ripples of induction motor and wind turbine generator are indicated. It can be emphasized that the cropped results of filter’s cost along with THD generated by MOGOA are very competitive and convincing when they are compared to the results of the well-structure multi-objective genetic algorithm.

Transmission Congestion Management in Deregulated Electricity Market using Multi-Objective Grasshopper Optimization Algorithm

In this paper, a study of multi-objective based congestion management is carried out by generator rescheduling with considering generator fuels cost. Due to the rapid growth of electrical load, the pressure in the transmission sector increases to provide transmiss

MOGOA algorithm for constrained and unconstrained multi-objective optimization problems

Grasshopper Optimization Algorithm (GOA) was modified in this paper, to optimize multi-objective problems, and the modified version is called Multi-Objective Grasshopper Optimization Algorithm (MOGOA). An external archive is integrated with the GOA for saving the Pareto optimal solutions. The archive is then employed for defining the social behavior of the GOA in the multi-objective search space. To evaluate and verify the effectiveness of the MOGOA, a set of standard unconstrained and constrained test functions are used. Moreover, the proposed algorithm was compared with three well-known optimization algorithms: Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Ant Lion Optimizer (MOALO), and Non-dominated Sorting Genetic Algorithm version 2 (NSGA-II); and the obtained results show that the MOGOA algorithm is able to provide competitive results and outperform other algorithms.