Powerful Optimization Method Research Articles

Optimal Quantitative Controller Design for Twin Rotor MIMO System

In this paper, the control approach is used for achieving the desired performance and stability of the twin-rotor MIMO system. This system is considered one of the complex multiple inputs of multiple-output systems. The complexity because of the high nonlinearity, significant cross-coupling and parameter uncertainty makes the control of such systems is a very challenging task. The dynamic of the Twin Rotor MIMO System (TRMS) is the same as that in helicopters in many aspects. The Quantitative Feedback Theory (QFT) controller is added to the control to enhance the control algorithm and to satisfy a more desirable performance. QFT is one of the frequency domain techniques that is used to achieve a desirable robust control in presence of system parameters variation. Therefore, a combination between control and QFT is presented in this paper to give a new efficient control algorithm. On the other hand, to obtain the optimal values of the controller parameters, Particle Swarm Optimization (PSO) which is one of the powerful optimization methods is used. The results show that the proposed quantitative control can achieve more desirable performance in comparison to control especially in attenuating the cross-coupling and eliminating the steady-state error.

Power Optimization in Device-to-Device Communications: A Deep Reinforcement Learning Approach With Dynamic Reward

Device-to-Device (D2D) communication can be used to improve system capacity and energy efficiency (EE) in cellular networks. One of the critical challenges in D2D communications is to extend network lifetime by efficient and effective resource management. Deep reinforcement learning (RL) provides a promising solution for resource management in wireless communication systems. This letter aims to maximise the EE while satisfying the system throughput constraints as well as the quality of service (QoS) requirements of D2D pairs and cellular users in an underlay D2D communication network. To achieve this, a deep RL based dynamic power optimization algorithm with dynamic rewards is proposed. Moreover, a novel algorithm with two parallel deep Q networks (DQNs) is designed to maximize the EE of the considered network. The proposed deep RL based power optimization method with dynamic rewards achieves higher EE while satisfying the system throughput requirements.

A new metaheuristic approach based on agent systems principles

Agent-based modeling is a relatively new approach to model complex systems composed of agents whose behavior is described using simple rules. As a consequence of the agent interactions emerges a complex global behavioral pattern not explicitly programmed. In the last decade, an increasing number of metaheuristic techniques have been reported in the literature where authors claim their novelty and their abilities to perform as powerful optimization methods. Although these schemes emulate very different processes or systems, the rules used to model individual behavior are very similar. The idea behind the design of many metaheuristic methods is to configure a recycled set of rules that has demonstrated to be successful in previous approaches for producing new optimization schemes. Such common rules have been designed without considering the final global result obtained by the individual interactions. On the other hand, agent-based systems provide a solid theory and a set of consistent models that allow characterizing global behavioral patterns produced by the collective interaction of the individuals from a set of simple rules. Under this perspective, several agent-based concepts and models that generate very complex global search behaviors can be used to produce or improve efficient optimization algorithms. In this paper, a new metaheuristic algorithm based on agent systems principles is presented. The proposed method is based on the agent-based model known as “Heroes and Cowards”. This model involves a small set of rules to produce two emergent global patterns that can be considered in terms of the metaheuristic literature as exploration and exploitation stages. To evaluate its performance, the proposed algorithm has been tested in a set of representative benchmark functions, including multimodal, unimodal, and hybrid benchmark formulations. The competitive results demonstrate the promising association between both paradigms.

Optimal Model Reference Control Scheme Design for Nonlinear Strict-Feedback Systems

In this paper, a new design of the model reference control scheme is proposed in a class of nonlinear strict-feedback system. First, the system is analyzed using Lyapunov stability analysis. Next, a model reference is used to improve system performance. Then, the Integral Square Error (ISE) is considered as a cost function to drive the error between the reference model and the system to zero. After that, a powerful metaheuristic optimization method is used to optimize the parameters of the proposed controller. Finally, the results show that the proposed controller can effectively compensate for the strictly-feedback nonlinear system with more desirable performance.

Heterogeneous Cooperative Bare-Bones Particle Swarm Optimization with Jump for High-Dimensional Problems

This paper proposes a novel Bare-Bones Particle Swarm Optimization (BBPSO) algorithm for solving high-dimensional problems. BBPSO is a variant of Particle Swarm Optimization (PSO) and is based on a Gaussian distribution. The BBPSO algorithm does not consider the selection of controllable parameters for PSO and is a simple but powerful optimization method. This algorithm, however, is vulnerable to high-dimensional problems, i.e., it easily becomes stuck at local optima and is subject to the “two steps forward, one step backward” phenomenon. This study improves its performance for high-dimensional problems by combining heterogeneous cooperation based on the exchange of information between particles to overcome the “two steps forward, one step backward” phenomenon and a jumping strategy to avoid local optima. The CEC 2010 Special Session on Large-Scale Global Optimization (LSGO) identified 20 benchmark problems that provide convenience and flexibility for comparing various optimization algorithms specifically designed for LSGO. Simulations are performed using these benchmark problems to verify the performance of the proposed optimizer by comparing the results of other variants of the PSO algorithm.

New Caputo-Fabrizio fractional order [formula omitted] model for COVID-19 epidemic transmission with genetic algorithm based control strategy

Fractional derivative has a memory and non-localization features that make it very useful in modelling epidemics’ transition. The kernel of Caputo-Fabrizio fractional derivative has many features such as non-singularity, non-locality and an exponential form. Therefore, it is preferred for modeling disease spreading systems. In this work, we suggest to formulate COVID-19 epidemic transmission via SEIASqEqHR paradigm using the Caputo-Fabrizio fractional derivation method. In the suggested fractional order COVID-19 SEIASqEqHR paradigm, the impact of changing quarantining and contact rates are examined. The stability of the proposed fractional order COVID-19 SEIASqEqHR paradigm is studied and a parametric rule for the fundamental reproduction number formula is given. The existence and uniqueness of stable solution of the proposed fractional order COVID-19 SEIASqEqHR paradigm are proved. Since the genetic algorithm is a common powerful optimization method, we propose an optimum control strategy based on the genetic algorithm. By this strategy, the peak values of the infected population classes are to be minimized. The results show that the proposed fractional model is epidemiologically well-posed and is a proper elect.

Joint channel power and amplifier gain optimization in coherent DWDM systems

In this paper, the channel power and amplifier gain of coherent dense wavelength division multiplexing (DWDM) fiber-optic communication systems are jointly optimized. The objective is to maximize the minimum margin of signal to noise ratio (SNR). The presented optimization problem is non-linear and non-convex, in which the intra- and inter-channel nonlinear interference (NLI) noise of fiber is estimated by using the so-called Gaussian noise (GN) model. Furthermore, the noise and other practical aspects of optical amplifiers such as output power saturation, maximum gain limitation, gain tilt and ripple, and noise figure non-flatness are modeled. Finally, we show that this problem can be reformulated as a convex optimization. Our results reveal that joint power and amplifier gain optimization in a 5-span fiber link with span length of 80 km (100 km) leads to 1 dB (0.47 dB) higher SNR than the conventional power optimization method in which amplifier gain is fixed and equals to span loss. By considering gain ripple and tilt in a 8-span fiber link with span length of 80 km, we observed that our algorithm has 0.72 dB and 1.86 dB higher SNR than the alternative power optimization methods in which amplifier gain is equalized and unequalized, respectively.

Optimal Nonlinear Controller Design for Different Classes of Nonlinear Systems Using Black Hole Optimization Method

In this paper, a new optimal nonlinear controller is proposed for different classes of nonlinear systems using black hole (BH) optimization method. The different classes presented by these nonlinear systems include, time-varying nonlinear system with external disturbance, non-affine nonlinear strict-feedback system with full state constraints and uncertain nonlinear system. First, the nonlinear systems are analyzed using Lyapunov quadratic function. Second, the nonlinear controller is designed to guarantee the stability of the system after using a model reference to achieve the required performance. Then, the Integral Square Error performance index is used as a cost function to minimize the error between the reference output and the actual output of the proposed systems to zero. Afterward, the BH optimization method is applied as a new powerful optimization method to find the optimal parameters for the proposed nonlinear controller. Finally, the simulation results that the asymptotic stability and optimal performance are achieved with a lowest possible control action. In addition, it shows the efficiency of the proposed nonlinear controller for compensating different nonlinear systems problems.

Joint Allocation Strategies of Power and Spreading Factors With Imperfect Orthogonality in LoRa Networks

The LoRa physical layer is one of the most promising Low Power Wide-Area Network (LPWAN) technologies for future Internet of Things (IoT) applications. It provides a flexible adaptation of coverage and data rate by allocating different Spreading Factors (SFs) and transmit powers to end-devices. We focus on improving throughput fairness while reducing energy consumption. Whereas most existing methods assume perfect SF orthogonality and ignore the harmful effects of inter-SF interferences, we formulate a joint SF and power allocation problem to maximize the minimum uplink throughput of end-devices, subject to co-SF and inter-SF interferences and power constraints. This results into a mixed-integer non-linear optimization, which, for tractability, is split into two sub-problems: firstly, the SF assignment for fixed transmit powers, and secondly, the power allocation given the previously obtained assignment solution. For the first sub-problem, we propose a low-complexity many-to-one matching algorithm between SFs and end-devices. For the second one, given its intractability, we transform it using two types of constraints’ approximation: a linearized and a quadratic version. Our performance evaluation demonstrates that the proposed SF allocation and power optimization methods enable to drastically enhance various performance objectives such as throughput, fairness and power consumption, and that they outperform baseline schemes.

Pixels of chemical structures correlate to chromatographic detector responses using genetic algorithm-adaptive neuro-fuzzy inference system as a novel nonlinear feature selection method

This paper introduces the genetic algorithm-adaptive neuro-fuzzy inference system (GA-ANFIS), as a novel nonlinear feature selection method. This hybrid technique combines genetic algorithms (GAs) as powerful optimization methods with ANFIS as a robust nonlinear statistical method. Multivariate image analysis whose descriptors achieved from bidimensional images coupled to principal component analysis (PCA) and the most significant principal components (PCs) were extracted. Eigen value ranking (EV), correlation ranking (CR), GA-partial least square (GA-PLS) as well the method proposed in this work were used to select the most relevant set of PCs as inputs for ANFIS to assess electron capture detector responses of 207 polychlorinated biphenyls. The results indicated that GA-ANFIS is superior over the others in both selecting the most relevant set of PCs and correlating the inputs (PCs) with the detector responses. The best model was statistically validated for its predictive power using cross-validation, applicability domain and Y-scrambling evaluation procedures. Moreover, the superiority of this model obtained from pixels of chemical structures over the nonlinear one obtained from original molecular descriptors in a previous work indicates that the image analysis is a powerful tool in quantitative structure-(chromatographic) property relationship (QSPR) studies.

Research on Reactive Power Optimization Method of DG Accessing Distribution Network Based on Genetic Algorithm

While developing distributed generation, a large number of distributed power sources will affect the voltage stability of the grid. Firstly, the combination of theoretical analysis and formula deduction is adopted to study the factors affecting the voltage of the distributed power supply grid. Then the IEEE 33-node system is used as the simulation model to optimize the reactive power distribution of the distributed power distribution network. Search for the optimal reactive power output of the single power supply and achieve the minimum system voltage fluctuation.

Power optimization method for wireless power transmission system based on improved differential evolution algorithm

In this paper, we proposes a power optimization algorithm in view of the Differential Evolution algorithm (DE) in order to reduce the transmission power due to the frequency split of the electromagnetic coupling resonant radio energy transmission system in the over-coupled state. Simulation experiments in MATLAB environment verify the effectiveness of the algorithm for power optimization of transmission system. Simulation experiments show that compared with the standard Differential Evolution algorithm and adaptive control parameter Differential Evolution algorithm (JDE), a new adaptive Differential Evolution algorithm (NDE) is improved based on the standard Differential Evolution algorithm. The new algorithm enables the system to find the optimal load power faster, which is conducive to increasing the output power of the system.

Sensitivity analysis of control parameters in particle swarm optimization

Particle Swarm Optimization (PSO) is a powerful nature-inspired metaheuristic optimization method that may determine optimal solutions of engineering problems in fewer evaluations compared to other optimization methods. However, the literature shows that PSO may suffer from converging prematurely to a local solution, and this occurs due to poor tuning of the control parameters in PSO. In this paper, an extensive parametric sensitivity analysis was conducted to understand the impact of the individual control parameters and their respective influence on the performance of PSO. A benchmark constrained optimization problem was considered for studying PSO by modifying each parameter one-at-a-time. Therefore, initially, a constraint handling technique was formulated to allow particles to update their best historical solutions according to the feasibility. Results of the sensitivity analysis revealed that PSO was most sensitive to the inertia weight, cognitive component, and social component. The optimal parameter set, determined from the sensitivity analysis, was verified by comparison with metaheuristic methods. The verification study shows that the proposed parameter setting outperformed the other methods in all but one case, where it performed competently.

Dynamic reactive power optimization method based on the transformer dummy node model

This paper proposes a dynamic reactive power optimization method based on the transformer dummy node model. Compared to the method based on the traditional equivalent n circuit, the proposed method adds the voltage at the dummy node as a variable, together with the voltage at the other side of the transformer, to express the voltage change on the two sides of the transformer. So the turn ratio of transformer is removed from variables, and the power flow equations of transformer branches do not contain turn ratio and its square, both the dimension of power flow equations and the number of non-zero elements in the Jacobian and Hessian matrix reduce, which decreases the calculative amount and accelerates the solution process. The test results of IEEE4, 14, 30 bus systems show that the proposed method obtains the same optimal solution with the traditional method but converges more quickly. With higher computational efficiency, the proposed method is suitable for the practical application of power systems.

On the Behavior of the Douglas--Rachford Algorithm for Minimizing a Convex Function Subject to a Linear Constraint

The Douglas--Rachford algorithm (DRA) is a powerful optimization method for minimizing the sum of two convex (not necessarily smooth) functions. The vast majority of previous research dealt with th...

Efficient optical parameter mapping based on time-domain radiative transfer equation combined with parallel programming.

A two-dimensional optical parameter mapping based on the time-domain radiative transfer equation (TD-RTE) is studied in this work. The finite element method with structured and unstructured grids is employed to solve TD-RTE and OpenMP parallel technology is employed to improve the computing efficiency. The sequential quadratic programming algorithm is used as a powerful optimization method to reconstruct absorption and scattering parameter fields and the maximum a posteriori estimation is employed by introducing the regularization term into the objective function to improve the ill-posed inverse problem. In addition, the effects of measurement errors on reconstruction accuracy are investigated thoroughly. All the simulation results demonstrate that the reconstructed scheme we developed is accurate and efficient in optical parameter mapping based on TD-RTE.

Research on Artificial Intelligence Algorithm for Reactive Power Optimization of Distribution Network

Reactive power optimization of power system is a complex problem with many variables. Its variable type from the continuous variable, namely, have discrete ‘variable again. In this paper, genetic algorithm is adopted as the reactive power optimization algorithm, and the minimum loss of active power network is selected as the objective function to discuss the reactive power optimization method of distribution network.

Markov chain block coordinate descent

The method of block coordinate gradient descent (BCD) has been a powerful method for large-scale optimization. This paper considers the BCD method that successively updates a series of blocks selected according to a Markov chain. This kind of block selection is neither i.i.d. random nor cyclic. On the other hand, it is a natural choice for some applications in distributed optimization and Markov decision process, where i.i.d. random and cyclic selections are either infeasible or very expensive. By applying mixing-time properties of a Markov chain, we prove convergence of Markov chain BCD for minimizing Lipschitz differentiable functions, which can be nonconvex. When the functions are convex and strongly convex, we establish both sublinear and linear convergence rates, respectively. We also present a method of Markov chain inertial BCD. Finally, we discuss potential applications.

Multiobjective great deluge algorithm with two-stage archive support

A multiobjective great deluge algorithm with a two-stage external memory support and associated search operators exploiting the experience accumulated in memory are introduced. The level based acceptance criterion of the great deluge algorithm is implemented based on the dominance of a new solution against its parent and archive elements. The novel two-stage memory architecture and the use of dominance-based level approach make it possible to exploit promising solutions that both lie on better Pareto fronts in objective space and that are diversely separated in variable space. In this respect, the first stage of the external memory is managed as a short-term archive that is updated frequently when a solution that dominates its parent or some individuals over the current Pareto front is extracted whereas the second stage is organized as a long-term memory that is updated only after a number of first stage insertions. The use of memory-based search supported by effective move operators and dominance-based implementation of level mechanism within the great deluge algorithm resulted in a powerful multiobjective optimization method. The success of the presented approach is illustrated using unconstrained (bound constrained) multiobjective test instances used in the CEC’09 contest of multiobjective optimization algorithms. Using the evaluation framework described in CEC’09 contest and in comparison to published results of well-known modern algorithms, it is observed that the presented approach performs better than majority of its competitors and is a powerful alternative for multiobjective optimization.

A comparative analysis of real-time power optimization for organic Rankine cycle waste heat recovery systems

Organic Rankine cycle waste heat recovery technology has been gaining more and more attention in recent years. Real-time power optimization is crucial to the system performance. Even though individual real-time optimization methods exist, literature rarely investigate comparison of different real-time power optimization methods. This paper first time compares three real-time implementable power optimization methods for an organic Rankine cycle waste heat recovery system. Three optimization methods include Proportional-Integral-Derivative rule-based method, nonlinear model predictive control and dynamic programming. In the Proportional-Integral-Derivative rule-based method, rule-based method defines the optimal working fluid vapor temperature trajectory and Proportional-Integral-Derivative controller manipulates the pump speed to follow that trajectory. In the nonlinear model predictive control method, reference vapor temperature is defined close to the saturation temperature and then the model predictive control minimizes the vapor temperature tracking error by controlling the pump speed. In the Dynamic Programming method, the net power produced by the organic Rankine cycle system is defined in the cost function, which is maximized by controlling the pump speed. A Random Forest machine learning model is utilized to extract the rules for Dynamic Programming and then implemented in real-time. All the three methods are implemented in the same experimentally validated plant model for the comparison analysis. The comparison results show that the Dynamic Programming Random Forest method has similar performance with nonlinear model predictive control method and outperforms the Proportional-Integral-Derivative rule-based method by 9.9% in net power production. Dynamic Programming Random Forest method can be an alternative to the nonlinear model predictive control for its low computation cost and high net power production.