Powerful Optimization Method Research Articles

Conventional and star-shaped auxetic materials for the creation of band gaps

A band gap region, or simply a band gap, is a range of frequencies where vibrations of certain frequency ranges are isolated. In the present paper, such ranges are sought through the study of different cases for the shape of the unit cells of a lattice, i.e., of an assembly of classical structural elements, such as beams and plates. A lattice with a specific, special designed microstructure is considered in the present investigation. Each particular cell of the examined lattice is studied as a classical composite material consisting of a matrix and the reinforcing core (e.g., matrix-fiber composite), and it is discretized by using two-dimensional plane stress finite elements. The form of the core of the unit cells can be of several shapes, e.g., quadratic, circular, and star. Some of these shapes provide the whole lattice with auxetic behavior, with negative Poisson’s ratio at the homogenized properties. The shape and the microstructure of the lattice is optimized in order to achieve isolation of the desired frequencies. A first attempt on the optimization of star-shaped microstructures is also presented. The optimization is carried out using powerful global optimization methods, such as the genetic algorithms. Results indicate that band gaps may appear in both conventional and auxetic microstructures. Moreover, the appearance and the size of the band gaps depend on the selected microstructure.

Integrated Reactive Power Optimization Method for Active Distribution Networks Based on a Quantum Krill Herd Algorithm

Aiming at the problem that the reactive power optimization of traditional distribution network can’t adapt to the active distribution network (ADN) of large-scale distributed power access, a comprehensive reactive power optimization method based on quantum krill herd algorithm for ADN is proposed. Firstly, a reactive power optimization model of ADN based on proportional coefficient is proposed by analyzing the characteristics of active control and active management. Secondly, the krill herd algorithm is easy to fall into local optimum when solving optimization problems. To overcome this shortcoming, a quantum krill swarm algorithm is proposed. The algorithm uses the probability amplitude of quantum bits to represent the information of particle position, uses quantum revolving gate to increase population diversity and generates new population through chaotic crossover, which improves the convergence accuracy of the algorithm. Finally, simulation experiments are carried out on the modified IEEE33 node and IEEE 69 node to verify the effectiveness of the proposed model and algorithm.

Vortex Search Algorithm for Optimal Power Flow Analysis in DC Resistive Networks With CPLs

This express brief addresses the optimal power flow (OPF) analysis in direct-current (DC) resistive networks with constant power loads (CPLs). The OPF employs the vortex search algorithm (VSA) in conjunction with a power flow method based on successive approximations by proposing a master-slave optimization methodology. The VSA is a powerful numerical optimization method that works with Gaussian distributions and variable radius for intensive exploration and exploitation in the solution space of the OPF problem. The power flow based on successive approximations allows evaluating the objective function by solving the non-convex equality constraints related to the power balance equations. Numerical implementations in two distribution DC feeders with 10 and 21 nodes show that the proposed approach attains the optimal solution reported by convex approximations, sequential quadratic models and nonlinear optimization methods. All the simulations have been conducted in MATLAB software.

Parameter identification of engineering problems using a differential shuffled complex evolution

An accurate mathematical model has a vital role in controlling and synchronization of different systems. But generally in real-world problems, parameters are mixed with mismatches and distortions. In this paper, an improved shuffled complex evolution (SCE) is proposed for parameter identification of engineering problems. The SCE by employing parallel search efficiently finds neighborhoods of the optimal point. So it carries out exploration in a proper way. But its drawback is due to exploitation stages. The SCE cannot converge accurately to an optimal point, in many cases. The current study focuses to overcome this drawback by inserting a shrinkage stage to an original version of SCE and presents a powerful global numerical optimization method, named the differential SCE. The efficacy of the proposed algorithm is first tested on some benchmark problems. After achieving satisfactory performance on the test problems, to demonstrate the applicability of the proposed algorithm, it is applied to ten identification problems includes parameter identification of ordinary differential equations and chaotic systems. Practical experiences show that the proposed algorithm is very effective and robust so that it produces similar and promising results over repeated runs. Also, a comparison against other evolutionary algorithms reported in the literature demonstrates a significantly better performance of our proposed algorithm.

Decomposition-Based Multiobjective Optimization with Invasive Weed Colonies

In order to solve the multiobjective optimization problems efficiently, this paper presents a hybrid multiobjective optimization algorithm which originates from invasive weed optimization (IWO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), a popular framework for multiobjective optimization. IWO is a simple but powerful numerical stochastic optimization method inspired from colonizing weeds; it is very robust and well adapted to changes in the environment. Based on the smart and distinct features of IWO and MOEA/D, we introduce multiobjective invasive weed optimization algorithm based on decomposition, abbreviated as MOEA/D-IWO, and try to combine their excellent features in this hybrid algorithm. The efficiency of the algorithm both in convergence speed and optimality of results are compared with MOEA/D and some other popular multiobjective optimization algorithms through a big set of experiments on benchmark functions. Experimental results show the competitive performance of MOEA/D-IWO in solving these complicated multiobjective optimization problems.

Iterated Local Search in Combinatorial Optimization Problem

Iterated local search (ILS) is a very powerful optimization method for continuous-valued numerical optimization. However, ILS has seldom been used to solve combinatorial integer-valued optimization problems. In this paper, the iterated local search (ILS) with random restarts algorithm is applied to solve combinatorial optimization problems, e.g., the classical weapon-target allocation (WTA) problem which arises from the military operations research. The mathematical model of the WTA problem is explained in detail. Then the idea of ILS with random restarts is explained. A comparison of the algorithm with several existing search approaches shows that the ILS outperforms its competitors on the tested WTA problem.

A novel reactive power optimization method for distributed power system using PSO

In order to solve the problem of coordinated optimization of capacitor banks and distributed power sources under load fluctuation conditions, a new dynamic reactive power optimization method for distribution networks is proposed. Firstly, a stochastic model of distributed power supply and load is established. The Monte Carlo simulation sampling is used to obtain the distributed power output curve and load curve of 24 h. And the random power flow analysis method based on semi-invariant method is studied. Secondly, the distributed power investment benefit and network active loss are the objective functions, and the multi-objective dynamic reactive power optimization model of active distribution network is established. Then, based on the adaptive particle swarm optimization (PSO) algorithm, the solution method and process of the model are proposed. At last the validity and practicability of the proposed method are verified.

Self-adapting control parameters in particle swarm optimization

Particle Swarm Optimization (PSO) is a powerful nature-inspired metaheuristic optimization method. Compared to other methods, PSO can determine the optimal solution in fewer evaluations and generally performs more efficiently and effectively. However, researches show that the PSO method suffers from premature convergence and a dependence on the initial control settings. Due to these shortcomings, the application of PSO may lead to failure in obtaining the global optimal solution. In this work, modifications were performed on the original PSO algorithm to adapt the control parameters to the circumstances of the particles at a specific moment. The proposed method is known as the Unique Adaptive Particle Swarm Optimization (UAPSO). In the developed approach, constraints were handled by forcing the particles to learn from their feasible solutions only. Therefore, the constraint handling technique worked in accord with the adapting scheme to ensure that the particles were adapting to the environment by directing itself to the feasible regions. The performance of UAPSO was verified by a comparative study involving eight benchmark constrained optimization problems and a real-world design problem. The numerical results showed the superiority of UAPSO compared to the selected state-of-the-art metaheuristic methods and PSO variants, its ability in avoiding premature convergence and its consistency and efficiency.

Hybrid optimization of preparative chromatography for a ternary monoclonal antibody mixture.

In the purification of monoclonal antibodies, ion-exchange chromatography is typically used among the polishing steps to reduce the amount of product-related impurities such as aggregates and fragments, whilst simultaneously reducing HCP, residual Protein A and potential toxins and viruses. When the product-related impurities are difficult to separate from the products, the optimization of these chromatographic steps can be complex and laborious. In this paper, we optimize the polishing chromatography of a monoclonal antibody from a challenging ternary feed mixture by introducing a hybrid approach of the simplex method and a form of local optimization. To maximize the productivity of this preparative bind-and-elute cation-exchange chromatography, wide ranges of the three critical operational parameters-column loading, the initial salt concentration, and gradient slope-had to be considered. The hybrid optimization approach is shown to be extremely effective in dealing with this complex separation that was subject to multiple constraints based on yield, purity, and product breakthrough. Furthermore, it enabled the generation of a large knowledge space that was subsequently used to study the sensitivity of the objective function. Increased design space understanding was gained through the application of Monte Carlo simulations. Hence, this work proposes a powerful hybrid optimization method, applied to an industrially relevant process development challenge. The properties of this approach and the results and insights gained, make it perfectly suited for the rapid development of biotechnological unit operations during early-stage bioprocess development.

Joint Design for Modulation and Constellation Labels in Multiuser Superposition Transmission

Hierarchical modulation (HM), a promising non-orthogonal multiple access scheme, is able to provide different levels of protection for data streams and achieve a larger rate region that cannot be realized by traditional orthogonal schemes. In this paper, we propose a novel HM system with non-uniform constellation inputs and analytically derive the bit-interleaved coded modulation (BICM)-capacity for each hierarchical layer. Different from the power optimization method for the superimposed uniform constellation, we directly optimize the constellation positions in conjunction with the optimal binary labels design for HM in additive white Gaussian noise channels. For maximizing the BICM-capacity, a fairness balanced criteria is developed for the HM system by considering both power constraints and QoS constraints. Multi-start interior-point algorithm is then proposed to carry out the constellation optimization problems, and the corresponding methods are also proposed to reduce optimization complexity. Numerical results verify the performance gains of optimized HM compared with the optimized quadrature amplitude modulation based HM and other orthogonal transmission methods.

Evaluation of the Impact of Technology Scaling on Delay Testing for Low-Cost AVS

With the continued down-scaling of IC technology and increase in manufacturing process variations, it is becoming ever more difficult to accurately estimate circuit performance of manufactured devices. This poses significant challenges on the effective application of adaptive voltage scaling (AVS) which is widely used as the most important power optimization method in modern devices. Process variations specifically limit the capabilities of Process Monitoring Boxes (PMBs), which represent the current industrial state-of-the-art AVS approach. To overcome this limitation, in this paper we propose an alternative solution using delay testing, which is able to eliminate the need for PMBs, while improving the accuracy of voltage estimation. The paper shows, using simulation of ISCAS’99 benchmarks with 28nm FD-SOI library, that using delay test patterns result in an error of 5.33% for transition fault testing (TF), error of 3.96% for small delay defect testing (SDD), and an error as low as 1.85% using path delay testing (PDLY). In addition, the paper also shows the impact of technology scaling on the accuracy of delay testing for performance estimation during production. The results show that the 65nm technology node exhibits the same trends identified for the 28nm technology node, namely that PDLY is the most accurate, while, TF is the least accurate performance estimator.

An Improved Artificial Bee Colony Algorithm Based on Elite Strategy and Dimension Learning

Artificial bee colony is a powerful optimization method, which has strong search abilities to solve many optimization problems. However, some studies proved that ABC has poor exploitation abilities in complex optimization problems. To overcome this issue, an improved ABC variant based on elite strategy and dimension learning (called ABC-ESDL) is proposed in this paper. The elite strategy selects better solutions to accelerate the search of ABC. The dimension learning uses the differences between two random dimensions to generate a large jump. In the experiments, a classical benchmark set and the 2013 IEEE Congress on Evolutionary (CEC 2013) benchmark set are tested. Computational results show the proposed ABC-ESDL achieves more accurate solutions than ABC and five other improved ABC variants.

Noise insensitive optimal harmonic estimator design with lightning search algorithm

Abstract Electrical harmonics directly affect the power quality of electrical systems. In order to improve power quality of electrical systems, accurate estimation of electrical harmonics is crucial and essential. This paper presents a novel estimator design approach for harmonic estimation problems. The proposed harmonic estimator is based on lightning search algorithm (LSA) that is a powerful metaheuristic optimization method inspired of natural phenomenon of lightning. The performance of designed estimator has been comprehensively analyzed by considering time varying power signals that contain two well-known estimation problems in the literature. In addition, each of these problems are examined for three different levels of noise. Furthermore, performance of the designed estimator is compared with the previously reported estimators. The achieved estimation results for different case studies clearly indicated that the proposed LSA based harmonic estimator provides better estimation results in all experiments than that of our previous method based on modified artificial bee colony (MABC) optimization and other existing methods available in the literature.

Analysis of Tie-line Power Optimization in Integrated Energy Distribution System

Abstract Recently, the concept of integrated energy distribution system (IEDS) has gained popularity both in academia and industry, it is characterized by the comprehensive integration of multi-energy carriers and flexible utilization of available resources including distribution generations (DGs). However, the stability problem caused by the intermittency of renewable energy and uncertainties of energy demands are among the main concerns that need to be regulated properly. To address this issue, a novel method of tie-line power optimization is proposed in this paper, where the synergies among various energy sectors are stressed. The model of regional energy station (RES) is studied to provide the model basis, which can be regarded as the coupling link of electric power system (EPS) and natural gas system (NGS). In this regard, the flexibility of energy conversion and dispatch can be utilized as a kind of controllable resources for the sake of tie-line power optimization. Therefore, the tie-line power can be regulated in a flexible way in response to the control signal from the dispatch center. Verified by numerical studies, the results show the efficiency and applicability of the proposed strategy.

A 2.5-W 40-MHz-Bandwidth Hybrid Supply Modulator With 91% Peak Efficiency, 3-V Output Swing, and 4-mV Output Ripple at 3.6-V Supply

We describe a supply modulator comprising a proposed delay-based hysteresis controller, proposed wideband class-AB amplifier, and class-D amplifier for an envelope tracking (ET) power amplifier (PA). We investigate the power dissipation and optimization method of the supply modulator for wideband applications taking into consideration the propagation delay, and show that a controller with zero threshold current not only improves the bandwidth of the supply modulator but also reduces its power dissipation. Based on our investigations, we propose a delay-based hysteresis controller embodying a novel high-speed current comparator whose reference current (threshold current) is zero, thereby achieving higher power-efficiency and simpler hardware. We further propose a class-AB amplifier, which features a high bandwidth and accurately controlled quiescent current. The proposed supply modulator features simultaneously the wide bandwidth (40 MHz), high output power (2.5 W), high peak efficiency (91%), high output voltage swing (3 V), and low output ripple noise (4 mVrms) at 3.6-V supply. The ET PA embodying our proposed supply modulator achieves a high power-efficiency of 41.4% at 28.5-dBm peak output power and 34.4% at 3-dB backoff—a significant >1.6× improvement over the radio frequency PA without the supply modulator.

Two-stage Reactive Power Optimization Strategy for Active Distribution Network Based on Extreme Scenarios

Uncertainties from distributed generations (DGs) make conventional reactive power optimization (RPO) methods difficult to be applied in the distribution network (DN). To settle this issue, a novel two-stage RPO strategy for active distribution network (ADN) based on extreme scenarios is proposed in this paper. Random variables are handled through extreme scenarios, then flexible and inflexible variables are decided in two different stages to adapt to DG’s randomness. Firstly, based on second order cone relaxation (SOCR), power flow equations are convexly relaxed to transform original model into mixed integer second order cone programming (MISOCP). Then, extreme scenario method (ESM) is employed to determine DG’s deployment. Finally, numerical tests are performed on a modified IEEE 33-bus DN to demonstrate the effectiveness and feasibility of the proposed strategy.

Detection of Power Quality Disturbance of the Best ASD Based on Dynamic PSO Search

This paper mainly studies the power quality disturbance feature detection and optimization method based on atomic sparse decomposition algorithm. In order to solve the problem of large amount of matching pursuit algorithm, this paper proposes a Particle Swarm Optimization dynamic search (PSO-DS) algorithm based on the PSO optimization algorithm, using the prior information provided by the fast Fourier transform and wavelet transform to search for parameters. The range and optimization of the search for the best atom are optimized. The simulation of the example shows that the PSO-DS algorithm can effectively extract the signal features with less decomposition times, avoiding the generation of unrelated and erroneous components, improving the detection accuracy of the disturbance signal and the simplicity and accuracy of the signal representation.

A coordinated high-order sliding mode control of DFIG wind turbine for power optimization and grid synchronization

This paper aims to propose a coordinated high-order sliding mode control (HOSMC) method for power optimization and grid synchronization of a doubly-fed induction generator (DFIG) based wind energy conversion system (WECS). The super-twisting algorithm is employed to maximize the captured wind energy with optimal rotor speed tracking, and regulate the voltage to fulfill the grid requirement. Subsequently, a grid synchronization scheme is proposed, where the wind turbine stator terminal voltage is controlled to synchronize with the grid voltage without employing the current control loop. Meanwhile, a novel reaching law is employed to accelerate the reaching speed. Simulations are carried out to verify the effectiveness of the novel approach, where the first order sliding mode control and the proportional-integral control are considered for comparison. A stepwise wind speed scenario, a variable wind speed condition, and the scenario under parameter perturbations are included in the comparative study. The simulation results fully reveal the robustness, chattering free property and high tracking precision of the proposed method.

Asymmetric Body Bias Control With Low-Power FD-SOI Technologies: Modeling and Power Optimization

Body bias control is a fundamental technique widely used to provide an efficient tradeoff between leakage power and performance in ultralow-power systems. Therefore, a lot of research about power optimization which provides optimal power supply and body bias voltages has been carried out. However, considering the actual voltage sources, the conventional approaches suffer from limited performance/power control granularity and may lead to degradation in terms of the energy efficiency. Therefore, in this paper, a power optimization method that improves the performance/power control granularity is proposed and evaluated with real processor chips. In the proposed optimization, the body biases for nMOSFET and pMOSFET are controlled independently, while the conventional methods control them uniformly. This increases the number of possible voltage combinations and allows finer target frequency selection leading to lower power consumption than the conventional methods at the cost of the optimization complexity. In order to ease this complexity, the proposed optimization is based on simple power and delay models. The model-based optimization does not require brute force search in the phase of real chip testing; thus, the testing time and cost can be significantly reduced. Since the coefficients of the models are extracted with real chip measurements, the error of the model can be suppressed to a few percent in average. The proposed approach is validated by real chips implemented with a 65-nm fully depleted silicon on insulator technology. The evaluation results show that the proposed optimization is an efficient mean of power reduction for a leakage current dominant chip. In fact, when compared with the conventional method, the proposed approach achieves 9.617% of average power reduction reaching up to 22.77% in the case of the V850 microcontroller.

Research on Dynamic Reactive Power Optimization of Receiving Terminal Network Based on Voltage Infeasible Node

A dynamic reactive power optimization method for receiving end power grid with voltage infeasible nodes is proposed. Firstly, the reactive power compensation nodes are classified, and the basic concept of the system reactive compensation node is proposed. Then, on the basis of whether a voltage constraint unfeasible node is presented in a certain period, all the days are divided into feasible period cluster and infeasible interval cluster. On this basis, the receiving terminal of an unfeasible node with voltage is constructed. Dynamic reactive power optimization model of power grid. The model includes two sub models of dynamic reactive power optimization, which are unfeasible period cluster and feasible time interval cluster. In the former, a two phase algorithm based on homotopy interior point method and parallel coevolution algorithm is proposed, and the latter is solved by immune genetic algorithm. Finally, the effectiveness of the proposed method is verified by simulation analysis of the actual 25 node 110 kV high voltage distribution network.