Aiming at the diversity requirements of cognitive radar monitoring tasks, a joint optimization design criterion that comprehensively considers the mutual information (MI) and signal-to-interference-to-noise ratio (SIN...
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Aiming at the diversity requirements of cognitive radar monitoring tasks, a joint optimization design criterion that comprehensively considers the mutual information (MI) and signal-to-interference-to-noise ratio (SINR) between the target and the echo is proposed. In view of the challenges brought by the traditional water-filling algorithm, this paper further studies how to effectively solve the new optimization criteria to improve the overall performance of the system. Specifically, this paper proposes a PCMA-ES algorithm that combines an adaptive penalty function with the Covariance Matrix Adaptive evolutionary Strategy (CMA-ES) algorithm. The penalty function aims to prioritize feasible solutions by assigning them the highest fitness. For infeasible solutions with lower constraint violations, the fitness is slightly lower, allowing for better utilization of information from infeasible solutions. The simulation results show that the PCMA-ES algorithm has lower time complexity or better performance than the traditional water-filling algorithm, and can solve more complex transmission waveforms. In addition, the waveform designed with a joint optimization criterion outperforms that based on a single optimization criterion. The radar detection focus can be adjusted to meet the specific requirements of diverse detection tasks.
Load modeling is essential to distribution system analysis, planning, and control. Therefore, in this work, effect of non-linear load models has been considered for the optimal site and size of DG and SC allocation. A...
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Load modeling is essential to distribution system analysis, planning, and control. Therefore, in this work, effect of non-linear load models has been considered for the optimal site and size of DG and SC allocation. A new and efficient modified branch and bus ordering-based forward-backward load flow method has been applied to solve load flow problem in radial distribution system. Recently implemented several state-of-the-art evolutionaryalgorithms (EAs) are employed to solve optimal site and size of DG and SC allocation problems and it is shown that performance of multi-operator/multimethod is better than other algorithms that are based on a single operator and/or algorithm. Therefore, a new hybrid EA based on various state-of-the-art operators such as GA, DE, and PSO is designed and applied to solve optimal site and size of DG and SC allocation problems. Various technical objective functions (index of active and reactive power loss and voltage deviation index) are considered to show the impacts of non-linear load models. From the simulation results, it is shown that DG and SC allocation problem is multi-objective. Therefore, further weighted sum multi-objective technical, economic, and environmental functions are formulated to find the solution to DG and SC allocation problems. The gathered results demonstrate that the proposed methodology significantly minimizes the cost of energy supplied by grid, total operating cost, and active and reactive power losses. Consequently, it can be stated that the suggested methodology has considerable economic and technological benefits and may be used to address many optimization issues in various distribution networks.
While utilizing evolutionaryalgorithms (EAs) to solve constrained optimization problems (COPs), seeking feasible solutions that satisfy the constraints is the primary concern. In some cases, utilizing the knowledge o...
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While utilizing evolutionaryalgorithms (EAs) to solve constrained optimization problems (COPs), seeking feasible solutions that satisfy the constraints is the primary concern. In some cases, utilizing the knowledge of the objective may facilitate the exploration of feasible regions and the exploitation of global optima. To strike a balance between objective and constraints, we attempt to address two problems, namely whether transferring the knowledge of objective in the early evolutionary stage and how to dynamically transfer the knowledge of objective to constraints. First, whether transferring in the early stage is determined by the characteristics of the objective, which has been classified to simple and complex based on the variation of population distribution. Second, how to transfer is solved by designing suitable constraint handling techniques (CHTs). To deal with COPs with simple objective, an objective-oriented CHT is proposed, where an indicator called Knowledge Transfer Rate (KTR) representing the relationship between the objective and constraints is mined and used. For COPs with complex objective, a constraint-oriented CHT is proposed, which includes a constraint-driven strategy and a hybrid-driven strategy. The proposed method is executed on three benchmark test suites, namely IEEE CEC2006, CEC2010, and CEC2017, it achieves superior or at least competitive performance in comparison with other state-of-the-art methods. Furthermore, the proposed method is successfully implemented in a redundant robotic manipulator motion planning problem.
The expected penetration of renewable sources is driving the islanded microgrid towards uncertainties,which have highly influence the reliability and complexities of frequency *** alleviate the influence caused by loa...
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The expected penetration of renewable sources is driving the islanded microgrid towards uncertainties,which have highly influence the reliability and complexities of frequency *** alleviate the influence caused by load fluctuations and inherent variability of renewable sources,this article proposes an optimised robust proportional-integralderivation(PID)frequency control method by taking full advantage of a robust control strategy while simultaneously maintaining the basic characteristics of a PID *** the process of iterated optimisation,a weighted objective function is used to balance the tracking error performance,robust stability and disturbance attenuation ***,the robust PID frequency(RPIDF)controller is determined by an adaptive constrained population extremal optimisation algorithm based on self-adaptive penalty constraint-handling *** proposed control method is examined on a typical islanded microgrid,and the control performance is evaluated under various disturbances and parametric ***,the simulation results indicate that the fitness value of the proposed method is 1.7872,which is lower than 2.9585 and 3.0887 obtained by two other evolutionaryalgorithms-based RPIDF ***,the comprehensive simulation results fully demonstrate that the proposed method is superior to other comparison methods in terms of four performance indices on the most considered scenarios.
How to design an optimal mixed H-2/H-infinity robust PID controller for a complex control system is of great practical importance, but it is still an open issue. From the perspective of evolutionaryalgorithm, this pa...
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ISBN:
(纸本)9781538611685
How to design an optimal mixed H-2/H-infinity robust PID controller for a complex control system is of great practical importance, but it is still an open issue. From the perspective of evolutionaryalgorithm, this paper formulates this issue firstly as a typical constrained optimization problem by minimizing a weighted objective function consisting of the robust stability performance, disturbance attenuation performance and tracking error, and then presents a novel a novel optimal design method of mixed H-2/H-infinity. robust PID controllers based on a constrained evolutionary algorithm called CEO-DE by combining constrained extremal optimization and differential evolution. The simulation results on two typical multi-variable control systems have demonstrated the proposed CEO-DE based design method performs better than some reported popular methods such as intelligent genetic algorithm and multi-objective particle swarm optimization.
This paper presents two design optimization models for U-type electro-thermal microactuator. The result shows that the unequal arm model has better performance than the equal arm model. The models were solved by finit...
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This paper presents two design optimization models for U-type electro-thermal microactuator. The result shows that the unequal arm model has better performance than the equal arm model. The models were solved by finite element analysis that are verified and comparison to publish paper and then are utilized in optimum design. The feature of presenting work contains temperature constraints to avoid the harm of operating object and decrease stress concentration on flexure. For handling design constraints, a method named double chromosome genetic evolution(DCGE) is developed in gene based algorithm. The idea of the presenting constrained strategy was inspired from the gene evolved by double helix chromosome of expressing certain traits. The constrained evolutionary algorithm proposed in this work is efficient and reliable. It can be integrated into a broad field of engineering optimization.
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