Economic dispatch is the optimal scheduling for generating units with technical constraints. Combined heat and power economic dispatch (CHPED) refers to minimization of the total energy cost for generating electricity...
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Economic dispatch is the optimal scheduling for generating units with technical constraints. Combined heat and power economic dispatch (CHPED) refers to minimization of the total energy cost for generating electricity and heat supply to load demand. This planning model integrates heat and power energy to balance energy supply and demand, mitigate climate change and improve energy efficiency of sustainable cities and green buildings. In this paper for the first time, self-regulating particle swarm optimization (SRPSO) algorithm is utilized for solving the CHPED problem by considering valve point effects and prohibited zones on fuel cost function of pure generation units and electrical power losses in transmission systems. The main advantage of SRPSO algorithm to PSO algorithm is the inertia weight flexibility with respect to search conditions. In this algorithm, unlike PSO algorithm that inertia weight reduces in each iteration, this value increases or reduces proportional to particles' positions, which will lead particles to achieve optimal value with higher speed. The capability and effectiveness of the proposed algorithm are evaluated on a large-scale energy system using MATLAB environment. The results obtained by SRPSO algorithm are outperformed by other optimization methods from the economic, sustainable energy and time consumption point of view.
To overcome the lack of flexibility in laser beam shaping in current industrial applications, a new improved artificial neural network algorithm for diffracted laser beam shaping is proposed. Aiming at the existing pr...
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To overcome the lack of flexibility in laser beam shaping in current industrial applications, a new improved artificial neural network algorithm for diffracted laser beam shaping is proposed. Aiming at the existing problems in laser beam shaping, the Unet neural network algorithm is improved from the label image and convolution operation. By clarifying its training and application steps, the improved neural network algorithm is pre-trained firstly and then formally trained (full training). The result shows that the UNet neural network algorithm can gradually realize the laser beam shaping with the spatial light modulator and find the mapping relationship between the input image (phase diagram) and the output image (laser contour diagram).
Nature inspired swarm based meta-heuristic optimization technique is getting considerable attention and established to be very competitive with evolution based and physical based algorithms. This paper proposes a nove...
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Nature inspired swarm based meta-heuristic optimization technique is getting considerable attention and established to be very competitive with evolution based and physical based algorithms. This paper proposes a novel Buyer Inspired Meta-heuristic optimization algorithm (BIMA) inspired form the social behaviour of human being in searching and bargaining for products. In BIMA, exploration and exploitation are achieved through shop to shop hoping and bargaining for products to be purchased based on cost, quality of the product, choice and distance to the shop. Comprehensive simulations are performed on 23 standard mathematical and CEC2017 benchmark functions and 3 engineering problems. An exhaustive comparative analysis with other algorithms is done by performing 30 independent runs and comparing the mean, standard deviation as well as by performing statistical test. The results showed significant improvement in terms of optimum value, convergence speed, and is also statistically more significant in comparison to most of the reported popular algorithms.
This article deals with the multi-mode multi-project inverse scheduling problem of the turbine assembly workshop in a Chinese electric power station equipment manufacturing firm considering some unexpected disturbance...
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This article deals with the multi-mode multi-project inverse scheduling problem of the turbine assembly workshop in a Chinese electric power station equipment manufacturing firm considering some unexpected disturbances in the assembly process, such as materials delay, equipment failure and parts rework, etc. The objective is to optimize the assembly cost in inverse scheduling under the constraints of due date, worker modes, cranes, etc. A modified integer and categorical particle swarm optimization algorithm combined with Tabu search (MICPSO-TS) is proposed. In the proposed MICPSO-TS, double-vector encoding is presented to show the execution modes of activities and overtime schedule of projects which are optimized by ICPSO and TS respectively. A hybrid heuristic decoding algorithm (HHDA) including project order selection rules, crane scheduling rules, resource reservation mechanism and overtime determination rules is proposed. Eventually, the feasibility and effectiveness of the proposed MICPSO-TS are verified by the experimental test data and a real-world engineering case.
Accurate and robust parameter identification method is helpful to the imitation, control and optimization of photovoltaic (PV) system. Therefore, it is necessary to create some more accurate and robust algorithms. A n...
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Accurate and robust parameter identification method is helpful to the imitation, control and optimization of photovoltaic (PV) system. Therefore, it is necessary to create some more accurate and robust algorithms. A new metaheuristic algorithm modified Rao-1(MRao-1) is proposed by combining Rao-1 with two-way updating strategy based on random individuals. In this strategy, the current or random individuals are chosen as updated starting point and the difference between random individuals is taken as updated direction. MRao-1 inherits the advantages of the original Rao-1 algorithm without additional special parameters and improves the global search ability of Rao-1 significantly without increasing the time complexity of Rao-1. MRao-1 is evaluated on benchmark functions and applied to parameter identification of PV models. The results show that the accuracy and robustness of MRao-1 algorithm are superior to the original algorithm and other recent excellent algorithms. Therefore, MRao-1 is a promising parameter identification algorithm.
The efforts of this study aimed to evaluate the feasibility of the nanotubular halloysites in weathered pegmatites (NaHWP) for removing heavy metals (i.e., Cd2+, Pb2+) from water. Furthermore, two novel intelligent mo...
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The efforts of this study aimed to evaluate the feasibility of the nanotubular halloysites in weathered pegmatites (NaHWP) for removing heavy metals (i.e., Cd2+, Pb2+) from water. Furthermore, two novel intelligent models, such as teaching-learning-based optimization (TLBO)-artificial neural network (ANN), and TLBO-support vector regression (SVR), named as TLBO-ANN and TLBO-SVR models, respectively, were proposed to predict the Cd2+ and Pb2+ absorption efficiencies from water using the NaHWP absorbent. Databases used, including 53 experiments for Pb2+ absorption and 56 experiments for Cd2+ absorption from water, under the catalysis of different conditions, such as initial concentration of Pb2+ and Cd2+, solution pH, adsorbent weight, and contact time. Subsequently, the TLBO-ANN and TLBO-SVR models were developed and applied to predict the efficiencies of Cd2+ and Pb2+ absorption from water, aiming to evaluate the role as well as the effects of different conditions on the absorption efficiencies using the NaHWP absorbent. The standalone ANN and SVM models were also taken into consideration and compared with the proposed hybrid models (i.e., TLBO-ANN and TLBO-SVR). The results showed that the NaHWP detected in a Kaolin mine (Vietnam) with 70% nanotubular halloysites is a potential adsorbent for water treatment to eliminate heavy metals from water. The two novel hybrid models proposed, i.e., TLBO-ANN and TLBO-SVR, also yielded the dominant performances and accuracies in predicting the Cd2+ and Pb2+ absorption efficiencies from water, i.e., RMSE = 1.190 and 1.102, R-2 = 0.951 and 0.957, VAF = 94.436 and 95.028 for the TLBO-ANN and TLBO-SVR models, respectively, in predicting the Pb2+ absorption efficiency from water;RMSE = 3.084 and 3.442, R-2 = 0.971 and 0.965, VAF = 96.499 and 96.415 for the TLBO-ANN and TLBOSVR models, respectively, in predicting the Cd2+ absorption efficiency from water. Furthermore, the validation results also demonstrated these findings in practic
Population-based optimization algorithms are one of the most widely used and popular methods in solving optimization problems. In this paper, a new population-based optimization algorithm called the Teamwork Optimizat...
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Population-based optimization algorithms are one of the most widely used and popular methods in solving optimization problems. In this paper, a new population-based optimization algorithm called the Teamwork optimization algorithm (TOA) is presented to solve various optimization problems. The main idea in designing the TOA is to simulate the teamwork behaviors of the members of a team in order to achieve their desired goal. The TOA is mathematically modeled for usability in solving optimization problems. The capability of the TOA in solving optimization problems is evaluated on a set of twenty-three standard objective functions. Additionally, the performance of the proposed TOA is compared with eight well-known optimization algorithms in providing a suitable quasi-optimal solution. The results of optimization of objective functions indicate the ability of the TOA to solve various optimization problems. Analysis and comparison of the simulation results of the optimization algorithms show that the proposed TOA is superior and far more competitive than the eight compared algorithms.
Traveling Salesman Problem (TSP) has been seen in diverse applications, which is proven to be NP-complete in most cases. Even though there are multiple heuristic techniques, the problem is still a complex combinatoria...
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Traveling Salesman Problem (TSP) has been seen in diverse applications, which is proven to be NP-complete in most cases. Even though there are multiple heuristic techniques, the problem is still a complex combinatorial optimization problem. The candidate solutions are chosen by considering only a set of high values of the objective function which may not lead to the best solutions. Hence, this paper develops a hybrid optimization algorithm, named Earthworm-based DHOA (EW-DHOA) to solve the TSP problem by finding an optimal solution. The proposed EW-DHOA is developed by integrating the two well-performing meta-heuristic algorithms, such as Deer Hunting optimization algorithm (DHOA) and Earthworm optimization algorithm (EWA). The EW-DHOA intends to optimize the constraint as the number of cities traveled by the salesman in terms of an optimal path. The main process for attaining this objective is to minimize the distance traveled by the salesman concerning the entire cities. The effectiveness of the proposed hybrid meta-heuristic algorithm is validated over the benchmark dataset. Finally, the experimental results show that the convergence of the proposed hybrid optimization will be better while solving TSP with less computational complexity, and improved significantly in attaining optimal results. (C) 2021 Elsevier B.V. All rights reserved.
In order to solve the problems, such as insufficient search ability and low search efficiency, of Whale optimization algorithm (WOA) in solving high-dimensional problems, a novel Hybrid WOA with Gathering strategies (...
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In order to solve the problems, such as insufficient search ability and low search efficiency, of Whale optimization algorithm (WOA) in solving high-dimensional problems, a novel Hybrid WOA with Gathering strategies (HWOAG) is proposed in this paper. Firstly, an individual-based updating way is used in HWOAG instead of the dimension-based updating one of WOA to reduce the computational complexity and to be more suitable for highdimensional problems. Secondly, a random opposition learning strategy is embedded into the individual-based WOA to form an opposition learning WOA (OWOA), and Grey Wolf Optimizer (GWO) is integrated into OWOA to form an OWOA with GWO (OWOAG) so as to improve the global search ability of WOA. Finally, two standalone OWOAGs are formulated to balance exploration and exploitation better. The two OWOAGs adopt strategies such as switching parameter tuning, random differential disturbance and global-best spiral operator to get stronger search ability. A lot of experimental results on high-dimensional (i.e. 1000-, 2000-, 4000- and 8000dimensional) benchmark functions and clustering datasets for Fuzzy C-Means (FCM) optimization show that HWOAG has stronger search ability and higher search efficiency than WOA and quite a few state-of-the-art algorithms and that all the strategies gathered to WOA are effective. The source codes of the proposed algorithm HWOAG are available at https://***/kangzhai/HWOAG.
A novel Monte Carlo Tree Search optimization algorithm that is trained using a Reinforcement Learning approach is developed for the application to geometric design tasks. It is capable of evaluating design parameters ...
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ISBN:
(数字)9781624105951
ISBN:
(纸本)9781624105951
A novel Monte Carlo Tree Search optimization algorithm that is trained using a Reinforcement Learning approach is developed for the application to geometric design tasks. It is capable of evaluating design parameters and demonstrates the successful application of reinforcement learning strategies on a physics informed design optimization task. The algorithm is intended to be used for the parametric design of the optimal geometry of a propeller for Fixed-Wing VTOL UAV but is also applied to an aircraft design problem with ease.
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