The effectiveness of differentialevolution (DE) is significantly impacted by the selection of the mutation operator and the setup of control parameters. However, their unique selection might not ensure the optimized ...
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The effectiveness of differentialevolution (DE) is significantly impacted by the selection of the mutation operator and the setup of control parameters. However, their unique selection might not ensure the optimized search procedure that enables the algorithm to explore for a global optimal solution for the given optimization problem. Further, with this selection approach, the algorithm might suffer from the issues of getting stuck at local optima and premature convergence. To address these challenges, this paper proposes a new framework of the DE called fitness and collaborative information-driven DE (COLDE). In the COLDE, a novel mutation operator is proposed to strengthen the collaboration among elite and non-elite candidate solutions so that more promising offspring vectors can be generated. The scale factor parameters are adjusted according to the evolutionary state of candidate solutions engaged in the mutation operator, while the crossover operator is tuned based on the success rates of crossover parameters determined in the past evolutionary stage. Moreover, the population size is also reduced over the generations to discard the unfavorable candidate solutions. The validation of the proposed COLDE is conducted on the standard set of benchmark problems provided by the IEEE CEC2017 of real-parameter single-objective problems and eight real-world engineering optimization problems. A comparison of COLDE with other evolutionary algorithms using diverse performance metrics verifies its promising and competitive search efficiency against the compared algorithms.
With the increasing demand for location information, applications based on location information are also showing a diversified trend. Traditional indoor positioning technology often requires multiple base stations or ...
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With the increasing demand for location information, applications based on location information are also showing a diversified trend. Traditional indoor positioning technology often requires multiple base stations or relies on external devices to achieve positioning. To better avoid the problem of synchronization between sites in multi-site positioning systems, this study proposes a positioning method based on differential evolution algorithm in indoor non line of sight scenarios with known access point locations. By collecting multi-path fingerprint features from terminals and utilizing differential evolution algorithm for optimization, a single station positioning system based on differential evolution algorithm was established to achieve indoor single station positioning of basketball courts. The experiment shows that the positioning model can achieve 90% accuracy when the distance between R P is 0.8 m. In terms of positioning performance, a grid spacing of 0.5 meters is the best. Based on differential evolution algorithm, it can not only overcome the issue of error increase caused by multi-path effects but also improve positioning accuracy and stability. This study is essential for the design and optimization of indoor positioning systems.
Since passive elements such as resistors and capacitors used in active filter design change the gain and phase of the filter, their selection is an important issue. The ability of the filter to achieve a targeted qual...
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Since passive elements such as resistors and capacitors used in active filter design change the gain and phase of the filter, their selection is an important issue. The ability of the filter to achieve a targeted quality factor depends on the selection of these components. The filter sections are cascaded to get closer to the ideal filter response;in this case, as the number of components increases, the computational complexity in selecting the optimum values increases. Moreover, in practical applications, the values of the filter components should be fixed to the discrete component values in the industrial component series. Thus, the problem turns into a discrete optimization problem that becomes more difficult and time-consuming. The use of metaheuristic algorithms in solving the problem is an alternative approach. This work propounds a survey on the differentialevolution (DE) algorithm and its advanced variants utilized for the selection of optimal component values in filter design. Nine DE algorithms have been used to determine the optimal component values of the tenth-order Multi-Feedback topology Bessel filter. The performance of each algorithm has been evaluated in terms of convergence rate and solution accuracy. Statistical results show that success-history based adaptive DE with linear population size reduction (LSHADE) is superior to other DE variants and can reduce the filter quality factor error value to 4.64E-02 for E12 series, 6.45E-02 for E96 series and 2.23E-02 for E192 series. The obtained results show that LSHADE algorithm are an effective tool for the selection of optimal discrete component values in filter design, increasing computational speed and solution accuracy.
The traditional method for designing branch-line couplers relies on a time-consuming labor-intensive trial-And-error process with multiple EM simulation iterations. This paper introduces a novel framework combining ar...
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The pressure control of once-through steam generator (OTSG) is critical for the operation and safety of small modular reactors. However, the dynamics of the OTSG are quite nonlinear, time varying, and uncertain. Class...
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The pressure control of once-through steam generator (OTSG) is critical for the operation and safety of small modular reactors. However, the dynamics of the OTSG are quite nonlinear, time varying, and uncertain. Classical control methods face significant challenges in keeping the pressure within acceptable limits in an environment with frequent load variations and multiple sources of external *** this paper, a robust control strategy based on active disturbance rejection control (ADRC) optimized by the differentialevolution (DE) algorithm is proposed to satisfy the requirements of steam pressure control in an optimum and efficient way. First, a lumped parameter model for the OTSG is developed based on the notion of moving boundaries and linearized to introduce a transfer function model for control design purposes. Then a feedforward cascade control system based on an ADRC controller and a proportional integral differential (PID) controller is designed, which mainly consists of a pressure ADRC controller, a feedwater PID controller, and a feedforward *** improve the pressure control performance and parameter tuning efficiency of the OTSG control system, a DE algorithm is applied to optimize the ADRC parameters, and the frequency domain and time domain characteristics are compared with particle swarm optimization and the genetic algorithm. Transient simulation experiments were used to evaluate the control performance at 100%, 50%, and 25% power levels, respectively. Moreover, a performance robustness criterion is proposed to demonstrate the robust stability of the ADRC, and the robustness metric is compared with that of the PID control schemes. The simulation results show that DE-ADRC control strategy has better set point tracking, interference rejection, and robust stability than DE-PID control strategy.
This article proposes a novel differential evolution algorithm based on dynamic multi-population (DEDMP) for solving the multi-objective flexible job shop scheduling problem. In DEDMP, at each generation, the whole po...
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This article proposes a novel differential evolution algorithm based on dynamic multi-population (DEDMP) for solving the multi-objective flexible job shop scheduling problem. In DEDMP, at each generation, the whole population is divided into several subpopulations by the clustering partition and the size of the subpopulation is dynamically adjusted based on the last search experience. Furthermore, DEDMP is adaptive based on two search strategies, one with strong exploration ability and the other with strong exploitation ability. The selection probability of each search strategy is also dynamically adjusted according to the success rate. Furthermore, the proposed algorithm adopts newly designed mutation and crossover operators and it can directly generate feasible solutions in the search space. To evaluate the performance of DEDMP, DEDMP is compared with some state-of-the-art algorithms on benchmark instances. The experimental results show that DEDMP is better than or at least competitive with other outstanding algorithms.
In the present article, differential evolution algorithm is used to perform structural identification of mass and stiffness properties of civil structures from dynamic test results. Identification is performed initial...
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In the present article, differential evolution algorithm is used to perform structural identification of mass and stiffness properties of civil structures from dynamic test results. Identification is performed initially starting from exact values of modal parameters (frequencies and mode shapes). Robustness of the algorithm is then tested by adopting pseudo-experimental input data, obtained by adding to exact data some statistic scattering, representing experimental measurement error. Different objective functions are adopted in identification procedure, and results are compared with those obtained adopting classical gradient method. The method is used to identify masses, elastic moduli, and stiffnesses of external constraints of a RC frame structure and a steel-concrete bridge. Numerical results confirm that adopting both frequencies and mode shapes instead of frequencies only strongly increases sensitivity of objective function to identification parameters. Scattering of identified parameters is much smaller, with coefficient of variation of the same order of magnitude of that of pseudo-experimental data used as input values in dynamic identification procedure.
The ever-increasing demand for broadband Internet access has motivated the further development of the digital subscriber line to the *** standard in order to expand its operational band from 106 to 212 MHz. Convention...
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The ever-increasing demand for broadband Internet access has motivated the further development of the digital subscriber line to the *** standard in order to expand its operational band from 106 to 212 MHz. Conventional far-end crosstalk (FEXT)-based cancellers falter in the upstream transmission of this emerging *** system. In this paper, we propose a novel differential evolution algorithm (DEA)-aided turbo channel estimation (CE) and a multi-user detection (MUD) scheme for the *** upstream, including the frequency band up to 212 MHz, which is capable of approaching the optimal Cramer-Rao lower bound of the channel estimate, whilst approaching the optimal maximum likelihood MUD's performance associated with perfect channel state information and, yet, only imposing about 5% of its computational complexity. Explicitly, the turbo concept is exploited by iteratively exchanging information between the continuous value-based DEA-assisted channel estimator and the discrete value-based DEA MUD. Our extensive simulations show that 18-dB normalized mean square error gain is attained by the channel estimator and 10-dB signal-to-noise ratio gain can be achieved by the MUD upon exploiting this iteration gain. We also quantify the influence of the CE error, the copper length, and the impulse noise. This paper demonstrates that the proposed DEA-aided turbo CE and MUD scheme is capable of offering near-capacity performance at an affordable complexity for the emerging *** systems.
In this paper, a differentialevolution (DE) algorithm is developed to solve emission constrained economic power dispatch (ECEPD) problem. Traditionally electric power systems are operated in such a way that the total...
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In this paper, a differentialevolution (DE) algorithm is developed to solve emission constrained economic power dispatch (ECEPD) problem. Traditionally electric power systems are operated in such a way that the total fuel cost is minimized regardless of emissions produced. With increased requirements for environmental protection, alternative strategies are required. The proposed algorithm attempts to reduce the production of atmospheric emissions such as sulfur oxides and nitrogen oxides, caused by the operation of fossil-fueled thermal generation. Such reduction is achieved by including emissions as a constraint in the objective of the overall dispatching problem. A simple constraint approach to handle the system constraints is proposed. The performance of the proposed algorithm is tested on standard IEEE 30-bus system and is compared with conventional methods. The results obtained demonstrate the effectiveness of the proposed algorithm for solving the emission constrained economic power dispatch problem. Published by Elsevier B.V.
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