An improved multi-objective population-based extremal optimization algorithm with polynomial mutation

Zeng, Guo‐Qiang; Chen, Jie; Liu, Limin; Chen, Min-Rong; Wu, Lie; Dai, Yuxing; Chen, Zheng

doi:10.1016/j.ins.2015.10.010

Cited by 104 publications

(22 citation statements)

References 49 publications

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“…However, from the theoretical perspective, the optimal design issue of the weighting vectors, prediction horizon and control horizon in the proposed MPC method is still challenging. From the perspective of engineering practice, the proposed method will be further studied in depth by tuning the weighting vectors, prediction horizon and control horizon based on evolutionary algorithms, such as multi-objective optimization algorithms [46][47][48]. On the other hand, the extension of MPC to more complex power systems by taking into account the robust control performance indices [45] and real-time predictive power of renewable energy systems [49] is another significant subject of future investigation.…”

Section: Discussionmentioning

confidence: 99%

An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations

2017

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Abstract:As the penetration level of renewable distributed generations such as wind turbine generator and photovoltaic stations increases, the load frequency control issue of a multi-area interconnected power system becomes more challenging. This paper presents an adaptive model predictive load frequency control method for a multi-area interconnected power system with photovoltaic generation by considering some nonlinear features such as a dead band for governor and generation rate constraint for steam turbine. The dynamic characteristic of this system is formulated as a discrete-time state space model firstly. Then, the predictive dynamic model is obtained by introducing an expanded state vector, and rolling optimization of control signal is implemented based on a cost function by minimizing the weighted sum of square predicted errors and square future control values. The simulation results on a typical two-area power system consisting of photovoltaic and thermal generator have demonstrated the superiority of the proposed model predictive control method to these state-of-the-art control techniques such as firefly algorithm, genetic algorithm, and population extremal optimization-based proportional-integral control methods in cases of normal conditions, load disturbance and parameters uncertainty.

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Section: Discussionmentioning

confidence: 99%

An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations

2017

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“…There are many solutions are introduced to solve this problem. [45][46][47] The OBL and QOBL approaches are approved as effective solutions to cope with the trapping in a local optimum. The concept of OBL was applied in machine learning applications 48 and then to artificial intelligence techniques.…”

Section: The Proposed Modification Of Multitracker Optimization Algmentioning

confidence: 99%

Optimal design of nonlinear model predictive controller based on new modified multitracker optimization algorithm

Elsisi

2020

Int J Intell Syst

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The controller design for the robotic manipulator faces different challenges such as the system's nonlinearities and the uncertainties of the parameters. Furthermore, the tracking of different linear and nonlinear trajectories represents a vital role by the manipulator. This paper suggests an optimal design for the nonlinear model predictive control (NLMPC) based on a new improved intelligent technique and it is named modified multitracker optimization algorithm (MMTOA). The proposed modification of the MTOA is carried out based on opposition‐based learning (OBL) and quasi OBL approaches. This modification improves the exploration behavior of the MTOA to prevent it from becoming trapped in a local optimum. The proposed method is applied on the robotic manipulator to track different linear and nonlinear trajectories. The NLMPC parameters are tuned by the MMTOA rather than the trial and error method of the designer. The proposed NLMPC based on MMTOA is compared with the original MTOA, genetic algorithm, and cuckoo search algorithm in literature. The superiority and effectiveness of the proposed controller are confirmed to track different linear and nonlinear trajectories. Furthermore, the robustness of the proposed method is emphasized against the uncertainties of the parameters.

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“…In order to alleviate this problem, in this paper, a competitive evolutionary algorithm is employed to optimize the related parameters in FOPFC for improving the closed-loop performance. As an efficient evolutionary algorithm, population-based extremal optimization (PEO) [23] is extended from extremal optimization (EO) [24] and has shown great promising ability in a variety of fields, such as numerical optimization problems including single-objective and multiobjective problems [23,25], PID/FOPID controllers designing problems [26,27], and weighting optimization of ensemble learning [28]. To be more precise, in [25], an improved multiobjective PEO was presented for solving multiobjective problems.…”

Section: Introductionmentioning

confidence: 99%

Fractional-Order Predictive Functional Control of Industrial Processes with Partial Actuator Failures

Dai

et al. 2020

Complexity

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As the actuator faults in an industrial process cause damage or performance deterioration, the design issue of an optimal controller against these failures is of great importance. In this paper, a fractional-order predictive functional control method based on population extremal optimization is proposed to maintain the control performance against partial actuator failures. The proposed control strategy consists of two key ideas. The first one is the application of fractional-order calculus into the cost function of predictive functional control. Since the knowledge of analytical parameters including the prediction horizon, fractional-order parameter, and smoothing factor in fractional-order predictive functional control is not known, population extremal optimization is employed as the second key technique to search for these parameters. The effectiveness of the proposed controller is examined on two industrial processes, e.g., injection modeling batch process and process flow of coke furnace under constant faults, time-varying faults, and nonrepetitive unknown disturbance. The comprehensive simulation results demonstrate the performance of the proposed control method by comparing with a recently developed predictive functional control, genetic algorithm, and particle swarm optimization-based versions in terms of four performance indices.

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An improved multi-objective population-based extremal optimization algorithm with polynomial mutation

Cited by 104 publications

References 49 publications

An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations

An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations

Optimal design of nonlinear model predictive controller based on new modified multitracker optimization algorithm

Fractional-Order Predictive Functional Control of Industrial Processes with Partial Actuator Failures

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