•A novel intelligent control approach AMDE-BP-FNN to control complex robot system.•There is layer upon layer of optimal structure for AMDE-BP-FNN.•Making the best of the advantages of AMDE algorithm ...and BP algorithm, to optimize FNN.•The superiority of AMDE-BP-FNN is highlighted by quantitative comparison.
This study established an adaptive memetic differential evolution-back propagation-fuzzy neural network (AMDE-BP-FNN) control method to achieve high-efficiency and precise control of robots with complex dynamic characteristics while reducing control costs. The adaptive differential evolution (ADE) method was applied to search the optimal parameters in the global scope and delimited the pseudo-global search scope. The memetic differential evolution (MDE) method was used to search for optimal parameters in the pseudo-global scope, and the probability factor was set to decide whether to use the back propagation (BP) algorithm for online optimization. Finally, simulations, experiments, and real-world applications were conducted. The results indicated the high efficiency, high precision, and viability of the proposed AMDE-BP-FNN method.
•Bernstein polynomials based parameter-free crossover.•A new universal/parameter-free Differential Evolution.•Real-valued numerical function optimization.•Evolutionary Image ...Vectorization.•Evolutionary Digital Terrain Model Simplification.
The standard Differential Evolution Algorithm (sDE) is a stochactic search method commonly used in evolutionary computing. The problem solving success of sDE is highly sensitive to the genetic operators used and the initial values of the parameters of these operators. Since a universal Differential Evolution Algorithm (uDE) is not sensitive to the structure and parameter values of the genetic operators used, it is parameter-free in practice and easier to control than sDE. uDE does not need a trial-and-error process when selecting the genetic operators and initial values of intrinsic parameter of related genetic operators to solve the problem, unlike the sDE. Therefore, the use and adaptation of a uDE to solve different types of numerical engineering problems is easy and time-consuming compared to sDE. In this paper, a new uDE, Bernstain-Search Differential Evolution Algorithm (BSD), is introduced. BSD is new and easily controllable, simple structured, non-recursive, highly efficient, fast and practically parameter-free uDE. BSD have a too feasible random crossover and mutation process and does not have a control-parameter setting process, contrary to sDE and its improved variants. In this paper, 30 benchmark problems of CEC’2014, 60 classic benchmark problems, image evolution problems for 12 test images and one Triangulated Irregular Network (TIN) refinement problem were used in the experiments performed to investigate the problem solving success of BSD, statistically. Four tested methods (i.e., ABC, JADE, CUCKOO, WDE) were used in the conducted experiments. Problem solving successes of BSD and tested methods were statistically compared by using Wilcoxon Signed Rank Test piecewisely. Results obtained from the performed tests showed that in general, problem solving success of BSD is statistically better than the tested methods that have been used in this paper.
In this article, a systematic design strategy of polarization-insensitive, broadband, and high-efficiency metasurface is reported, and the proposed metasurface has excellent performance in ...holographic imaging. First, we utilize a multiobjective differential evolution algorithm (MDEA) to rapidly design a high-performance meta-atom group, capable of achieving a reflected amplitude (efficiency) exceeding 0.91 (83%) within the bandwidth of 8.21-17.50 GHz. Subsequently, a wideband phase distribution method is proposed for calculating the imaging phase map. Guided by the phase map, the meta-array is designed for the generation of letter-shaped images under distinct polarization excitation across the entire bandwidth. Finally, calculations, simulations, and experiments collectively verify the effectiveness of the systematic design strategy utilized in holographic imaging. The metasurface sample has the capability to generate letter-shaped images at varying heights with an imaging efficiency over 62.8%. The proposed method can be employed to achieve the unrestricted distribution of electromagnetic (EM) wave energy in 3-D space, encompassing applications such as wireless charging, data storage, and microwave imaging.
•A Weighted-hybrid Support Vector Regression model is presented to forecast building energy consumption of an institutional building.•The parameter selection for the SVR models and optimization of ...the weights is done by the Differential Evolutionary algorithm.•The developed model can be used to forecast both half-hourly and daily energy consumption without manually changing any model parameter.•The model is compared with single SVR models in combination with other evolutionary algorithms like GA and PSO.•Time series cross-validation is adopted to prevent the model from overfitting.
Electricity load forecasting is crucial for effective operation and management of buildings. Support Vector Regression (SVR) have been successfully used in solving nonlinear regression and time series problems related to building energy consumption forecasting. As the performance of SVR heavily depends on the selection of its parameters, differential evolution (DE) algorithm is employed in this study to solve this problem. The forecasting model is developed using weighted SVR models with nu-SVR and epsilon-SVR. The DE algorithm is again used to determine the weights corresponding to each model. A case of time series energy consumption data from an institutional building in Singapore is used to elucidate the performance of the proposed model. The proposed model can be used to forecast both, half-hourly and daily electricity consumption time series data for the same building. The results show that for half-hourly data, the model exhibits higher weight for nu-SVR, whereas for daily data, a higher weight for epsilon-SVR is observed. The mean absolute percentage error (MAPE) for daily energy consumption data is 5.843 and that for half-hourly energy consumption is 3.767 respectively. A detailed comparison with other evolutionary algorithms show that the proposed model yields higher accuracy for building energy consumption forecasting.
Microgrid systems, such as solar photovoltaic (PV) power and wind energy, integrated with diesel generators are promising energy supplies and are economically feasible for current and future use in ...relation to increased demands for energy and depletion of conventional sources. It is thus important to optimize the size of hybrid microgrid system (HMS) components, including storage, to determine system cost and reliability. In this paper, optimal sizing of a PV/wind/diesel HMS with battery storage is conducted using the Multi-Objective Self-Adaptive Differential Evolution (MOSaDE) algorithm for the city of Yanbu, Saudi Arabia. Using the multi-objective optimization approach, the objectives are treated simultaneously and independently, thereby leading to a reduction in computational time. One of the main criteria to consider when designing and optimizing the HMS is the energy management strategy, which is required to coordinate the different units comprising the HMS. The multi-objective optimization approach is then used to analyze the Loss of Power Supply Probability (LPSP), the Cost of Electricity (COE), and the Renewable Factor (RF) in relation to HMS cost and reliability and is tested using three case studies involving differing house numbers. Results verify its application in optimizing the HMS and in its practical implementation. In addition, optimization results using the proposed approach provided a set of design solutions for the HMS, which will assist researchers and practitioners in selecting the optimal HMS configuration. Moreover, it is important to select optimally sized HMS components to ensure that all load demands are met at the minimum energy cost and high reliability.
•Optimal sizing of PV/wind/diesel hybrid system with battery storage was analyzed.•The analysis was done using MOSaDE algorithm for the city of Yanbu, Saudi Arabia.•The energy management strategy is the main criteria to design and optimize the HMS.•The multi-objective optimization approach was used to analyze the cost and reliability.•HMS components are selected to meet the minimum energy cost and high reliability.
Multimodal multi-objective optimization problem (MMOP) is a variant of the multi-objective problem (MOP), which has multiple optimal Pareto sets (PSs) mapping to the same or similar Pareto front (PF) ...from the decision space to the objective space. As algorithms must obtain multiple Pareto Sets, addressing MMOPs presents greater challenges for algorithms. To address the issue, the paper proposes a novel multimodal multi-objective differential evolution algorithm named MMODE_SDNR. Firstly, the algorithm sorts the population using the fast non-dominated sorting method and the Shortest Distance (SD) values. SD represents the shortest Euclidean distance between an individual and its nearest neighboring individual, calculated jointly in the decision space and the objective space. Secondly, a definition of the similarity between an individual and its neighbors in both spaces is provided, based on which a novel environmental selection strategy for the algorithm, called the nearest Neighbor-Repulsion (nNR) is devised. The strategy removes the individuals that have similarity with one another to maximize the retention of different individuals. Moreover, it keeps the diversity of PSs in the decision space and retains more solutions that exhibit a similar or same PF in the objective space. Finally, the proposed MMODE_SDNR has undergone several tests on the benchmark functions from CEC 2019 and CEC 2020 to assess its performance. The experimental results indicate that MMODE_SDNR excels in three metrics, outperforming other several state-of-the-art MMO algorithms. Its strong convergence and focus on decision space quality ensure competitiveness.
•Investigate stochastic resource leveling in projects with flexible structures.•Offer a stochastic programming-based algorithm and a differential evolution algorithm.•Devise a simulation-based ...evaluation algorithm to evaluate the scheduling policies.•Compare the proposed algorithms with state-of-the-art meta-heuristics.•Analyze the value of stochastic information after adopting the proposed algorithms.
In project management, efficient utilization of resources plays a key role in project success, and resource leveling is an effective technique to optimize resource usage. During project execution, there are often uncertainties that complicate resource leveling. Furthermore, existing research on resource leveling typically assumes a fixed project structure. However, this is not always the case in practice, because there may be a variety of optional technical solutions for some activities, leading to a flexible project structure. Therefore, considering both stochastic activity durations and flexible project structures, we propose and study the stochastic resource leveling problem with flexible project structures (SRLP-PS). The solution of the SRLP-PS is in the form of a scheduling policy. We design two algorithms for solving the NP-hard SRLP-PS: (a) an exact algorithm based on stochastic programming, in which we formulate a scenario-based non-linear stochastic programming model and linearize it into an equivalent deterministic mixed-integer linear programming model that can be directly solved by CPLEX; and (b) an improved differential evolution algorithm, which is equipped with several problem-specific components, such as two mutation operators balancing exploration and exploitation, initialization, and local improvement search. Extensive computational experiments on a large number of benchmark instances are performed to validate our algorithms, which are also compared with state-of-the-art meta-heuristics. The computational results reveal the effectiveness and competitiveness of our algorithms. We also analyze the value of stochastic information based on the exact algorithm and the meta-heuristics, respectively.
To reasonably arrange disassembly facilities and plan enterprise space, we propose a parallel disassembly line balancing problem (PW-PDLBP) with parallel workstations. Additionally, a mixed-integer ...non-linear programming (MINLP) model that minimizes the line length, number of workstations, idle time balancing index, and energy consumption is established based on the problem characteristics and is solved using the GUROBI optimizer. Furthermore, a multi-objective enhanced differential evolution algorithm (MEDE) is developed to obtain high-quality disassembly schemes for PW-PDLBP. The correctness of encoding and decoding and the solving performance of MEDE are verified by comparing with the MINLP model and four existing algorithms. Then, an instance consisting of two different types of end-of-life TVs is optimized. Finally, the effectiveness of PW-PDLBP in improving enterprise space utilization is validated by comparing it with the parallel line layout without parallel workstations.
Electric power, as an efficient and clean energy, has considerable importance in industries and human lives. Electricity price is becoming increasingly crucial for balancing electricity generation ...and consumption. In this study, long short-term memory (LSTM) with the differential evolution (DE) algorithm, denoted as DE–LSTM, is used for electricity price prediction. Several recent studies have adopted LSTM with considerable success in certain applications, such as text recognition and speech recognition. However, problems in the application of LSTM to solving nonlinear regression and time series problems have been encountered. DE, a novel evolutionary algorithm that effectively obtains optimal solutions, is designed to identify suitable hyperparameters for LSTM. Experiments are conducted to verify the performance of the DE–LSTM model under the electricity prices in New South Wales, Germany/Austria, and France. Results indicate that the proposed DE–LSTM model outperforms existing forecasting models in terms of forecasting accuracies.
•Effective Long short-term Memory (LSTM) is proposed for electricity price forecasting.•Differential evolution helps selecting suitable hyper-parameters of LSTM.•The proposed method named DE-LSTM is the best for three cases in terms of accuracy.
