Efficient Optimization Algorithm Research Articles

Power Loss Minimization and Voltage Profile Improvement by System Reconfiguration, DG Sizing, and Placement

A number of algorithms that aim to reduce power system losses and improve voltage profiles by optimizing distributed generator (DG) location and size have already been proposed, but they are still subject to several limitations. Hence, new algorithms can be developed or existing ones can be improved so that this important issue can be addressed more appropriately and effectively. This study proposes a reconfiguration methodology based on a hybrid optimization algorithm, consisting of a combination of the genetic algorithm (GA) and the improved particle swam optimization (IPSO) algorithm for minimizing active power loss and maintaining the voltage magnitude at about 1 p.u. The buses at which DGs should be injected were identified based on optimal real power loss and reactive power limit. When applying the proposed optimization algorithm for DGs allocation in power system, the search space or number of iterations was reduced, increasing its convergence rate. The proposed reconfiguration methodology was test in an IEEE-30 bus electrical network system with DGs allocations and the simulations were conducted using MATLAB software compared to other optimization algorithms, such as GA, PSO, and IPSO, the combination of GA and IPSO or Hybrid GA & IPSO (HGAIPSO) method has a smaller number of iterations and is more effective in optimization problems. The effectiveness of the proposed HGAIPSO has been tested on IEEE-30 bus network system with DGs allocations, and the obtained test results have been compared to those from other methods (i.e., GA, PSO, and IPSO). The simulation results show that the proposed HGAIPSO can be an efficient and promising optimization algorithm for distribution network reconfiguration problems. The IEEE-30 bus test system with DGs integrated at various location revealed reductions in overall real power loss of 40.7040%, 36.2403%, and 42.9406% for type 1, type 2, and type 3 DGs allocation, respectively. The highest bus voltage profile goes to 1.01 pu in the IEEE-30 bus.

Optimal design of mooring systems for floating production units based on metamodel-assisted Differential Evolution

This work presents a new approach to the optimal design of mooring systems for oil & gas floating production units (FPU). The proposed optimization procedure integrates an efficient optimization algorithm, the ε-Constrained Differential Evolution, with Artificial Neural Networks (ANNs) as metamodels that evaluate the candidate solutions along the optimization loop, effectively replacing the computationally expensive Finite Element nonlinear time-domain simulations. The ANNs are trained with data from time-domain simulations of representative sample configurations. This integration of a metamodel and an optimization algorithm into a unified framework for the optimal design of FPU spread mooring systems comprises an innovative tool for such applications. Other innovative aspects include enhancements in the modeling of the optimization problem by defining design variables, objective function and constraints that are more simple, direct, and appropriate to actual taut leg and semi-taut leg configurations. A case study considering a real VLCC-sized spread-moored FPSO operating in a deep-water field is presented to illustrate the practical application of the optimization tool. The results indicate that, with virtually no human supervision, the tool can provide feasible solutions with noticeable overall cost reductions (in terms of the CAPEX of the system), and with drastic reductions of total computational requirement.

Simultaneous Robust Matching Pursuit for Multi-view Learning

Joint sparse representation (JSR) has attracted massive attention with many successful applications in pattern recognition recently. In this paper, we propose a novel robust multi-view JSR method referred to as Simultaneous Robust Matching Pursuit (SRMP) based on the outlier-resistant M-estimator originating from robust statistics. Because of the complexity of the objective function, we design an efficient optimization algorithm to implement SRMP based on the half-quadratic theory. In addition, we have also extended the proposed method for the problems of multi-view subspace clustering and multi-view pattern classification, respectively. The experimental results corroborate the efficacy and robustness of SRMP for multi-view data recovery, subspace clustering and classification.

Fast Cross-Modal Hashing With Global and Local Similarity Embedding.

Recently, supervised cross-modal hashing has attracted much attention and achieved promising performance. To learn hash functions and binary codes, most methods globally exploit the supervised information, for example, preserving an at-least-one pairwise similarity into hash codes or reconstructing the label matrix with binary codes. However, due to the hardness of the discrete optimization problem, they are usually time consuming on large-scale datasets. In addition, they neglect the class correlation in supervised information. From another point of view, they only explore the global similarity of data but overlook the local similarity hidden in the data distribution. To address these issues, we present an efficient supervised cross-modal hashing method, that is, fast cross-modal hashing (FCMH). It leverages not only global similarity information but also the local similarity in a group. Specifically, training samples are partitioned into groups; thereafter, the local similarity in each group is extracted. Moreover, the class correlation in labels is also exploited and embedded into the learning of binary codes. In addition, to solve the discrete optimization problem, we further propose an efficient discrete optimization algorithm with a well-designed group updating scheme, making its computational complexity linear to the size of the training set. In light of this, it is more efficient and scalable to large-scale datasets. Extensive experiments on three benchmark datasets demonstrate that FCMH outperforms some state-of-the-art cross-modal hashing approaches in terms of both retrieval accuracy and learning efficiency.

Physically Consistent Lie Group Mesh Models for Robot Design and Motion Co-Optimization

With recent advances in rapid prototyping and mechatronics, the problem of simultaneous design and motion optimization, or the co-design problem, is becoming more and more relevant in robotics. For reasons of computational tractability, all existing methods use simplified approximations of a robot’s kinodynamic model. We empirically confirm that any model approximations that are not physically consistent, i.e., the kinodynamic model parameters do not accurately reflect the actual link shapes, can sometimes lead to large errors in the optimization. We then propose a physically consistent, Lie group-based mesh element model for robot links that (i) parametrizes a wide variety of link shapes, and (ii) provides closed-form analytic gradients for the mesh deformations. The latter property is key to developing efficient co-design optimization algorithms that significantly improve numerical convergence and stability. Our co-design methodology is validated through extensive numerical and hardware experiments involving 3D-printed serial and parallel robots.

Sensored speed control of brushless DC motor based salp swarm algorithm

<p>This article uses one of the newest and efficient meta-heuristic optimization algorithms inspired from nature called salp swarm algorithm (SSA). It imitates the exploring and foraging behavior of salps in oceans. SSA is proposed for parameters tuning of speed controller in brushless DC (BLDC) motor to achieve the best performance. The suggested work modeling and control scheme is done using MATLAB/Simulink and coding environments. In this work, a 6-step inverter is feeding a BLDC motor with a Hall sensor effect. The proposed technique is compared with other nature-inspired techniques such as cuckoo search optimizer (CSO), honey bee optimization (HBO), and flower pollination algorithm (FPA) under the same operating conditions. This comparison aims to show the superiority features of the proposed tuning technique versus other optimization strategies. The proposed tuning technique shows superior optimization features versus other bio-inspired tuning methods that are used in this work. It improves the controller performance of BLDC motor. It refining the speed response features which results in decreasing the rising time, steady-state error, peak overshoot, and settling time.</p>

Fuzzy adaptive jellyfish search-optimized stacking machine learning for engineering planning and design

This paper presents a novel fuzzy adaptive jellyfish search-optimized stacking system (FAJS-SS) that integrates the jellyfish search (JS) optimizer, the fuzzy adaptive (FA) logic controller, and stacking ensemble machine learning. First, FA logic is incorporated into JS optimizer to construct an efficient metaheuristic algorithm for global optimization. The proposed algorithm is benchmarked against various well-known optimizers using mathematical functions. The FAJS optimizer is then used to optimize the hyperparameters of the stacking system (SS). Cases that involve construction productivity, the compressive strength of a masonry structure, the shear capacity of reinforced deep beams, the axial strength of steel tube-confined concrete, and the resilient modulus of subgrade soils were investigated. Results of analyses reveal that the FAJS-SS predicts more accurately than the other machine learning systems in the literature. Accordingly, the proposed fuzzy adaptive metaheuristic-optimized stacking system is effective for providing engineering informatics in the planning and design phase.

Optimal operation for marine hybrid power system: Novel optimization algorithm and comprehensive analysis for penalty functions

In order to optimally control the marine hybrid power system (HPS) under increasingly complex regulation constraints or hardware constraints, an efficient power-flow scheduling model and optimization algorithm are of great importance. This work focuses on the optimal power-flow scheduling of marine HPS, especially on the efficiency improvement of the penalty functions for satisfying complex constraints. To be specific, an optimal operation model of marine HPS is discussed, and the complex model constraints are described as various penalty functions. Secondly, a novel optimization algorithm, namely adaptive multi-context cooperatively coevolving differential evolution algorithm with random topology and mutated context vector (AMCCDE - rt - mcv) is developed to optimize the aforementioned model. In order to ensure the satisfaction of the complex model constraints, the detailed forms for penalty functions are researched and the optimal parameters for penalty functions are comprehensively compared, analysed and tested by a set of numerical experiments. Finally, the developed methodologies are tested by simulation experiments. Experimental results show that the damping factor, exponent parameter and punish strength constant effect the efficiency of penalty functions a lot, and the developed penalty functions can effectively satisfy all the model constraints with fast response speed. With the integration of penalty functions, the developed methodology can obtain promising performance on the optimal scheduling of the evaluated marine HPS.

Efficient Nonlinear Optimization Algorithm Based on Water Pollution Prevention System

Efficient Nonlinear Optimization Algorithm Based on Water Pollution Prevention System

Optimal Control of the Logistics Automation Transmission System Based on Partial Differential Equation

In this paper, the finite difference scheme of the spatiotemporal fractional convection-diffusion equation is established, and its stability and convergence are proved. Furthermore, this discrete technique is extended to solve nonlinear spatiotemporal fractional convection-diffusion equations. By using the Krylov subspace method to solve the discrete system, the numerical solution of the spatiotemporal fractional convection-diffusion equation can be obtained quickly. In this paper, an efficient optimal control algorithm is proposed to solve the free control problem of a class of nonlinear time-delay systems. We obtained the optimal control law of the system through the Bellman optimality principle, obtained the asymptotic stability criterion of the system in the form of LMI under the optimal control input by using the Lyapunov stability theory, and discussed the effect of the delay parameter on the system stability. Using the principle of intelligent neural network approximation function, the evaluation neural network and the execution neural network are used to approximate the optimal performance index function and optimal control input, respectively, the optimal control strategy of the system is obtained, and the convergence of the weight estimation error is proved to be optimal. On the basis of optimal state adjustment, the optimal tracking control problem is further solved. Numerical example results verify the effectiveness of the proposed method in terms of stability analysis, optimal state control, and optimal tracking control for the nonlinear time-delay system proposed in this paper. We calculate the parameters of the conveyor and select a reasonable transmission and sorting mechanism to realize the speed regulation of the driving motor of each mechanism. Through the work of each part, the design scheme of the automatic transmission system is formed, and the reliability, practicability, and economy of the system are guaranteed.

Thermal optimization of secondary cooling systems in the continuous steel casting process

A new process design methodology for the cooling system in the continuous steel casting process is introduced. It is based on a semi-empirical approach for choosing the primary cooling zone design parameters and using an optimization-based method to decide on the design parameters corresponding to the secondary cooling zone. The heat flux through the secondary cooling zone is the design parameter in the latter case. Unlike former studies that considered a misfit temperature function as the objective function, it is defined here as the square of the temperature field penalized with the Niyama’s criterion infeasibility to ensure the production of microporosity-free strands. This way, no information about the target temperature field is required in advance. The corresponding heat transfer problem is coupled to CALPHAD to consider commercial steel grades straightforwardly. An efficient gradient-based optimization algorithm is employed to find the optimal values of design parameters. Then, this algorithm is employed in an actual process design problem corresponding to the continuous slab casting of a commercial steel grade. A post-processing approach is suggested to convert the results of numerical optimization to industrially relevant machine parameters, and then they are exploited in practice in the foundry plant. While the optimization problem is only constrained to avoid solidification-induced defects, it produces sound and crack-free strands. Moreover, comparing numerical results to actual surface temperature measurements reveals more petite than a six percent discrepancy between the simulation and experiment. These observations illustrate the feasibility of the presented design methodology as the initial step in deciding on the cooling system parameters in this process.

Coordinating heterogeneous mobile sensing platforms for effectively monitoring a dispersed gas plume

In order to sufficiently protect active personnel and physical environment from hazardous leaks, recent industrial practices integrate innovative multi-modalities so as to maximize response efficiency. Since the early detection of such incidents portrays the most critical factor for providing efficient response measures, the continuous and reliable surveying of industrial spaces is of primary importance. Current study develops a surveying mechanism, utilizing a swarm of heterogeneous aerial mobile sensory platforms, for the continuous monitoring and detection of CH4 dispersed gas plumes. In order to timely represent the CH4 diffusion progression incident, the research concerns a simulated indoor, geometrically complex environment, where early detection and timely response are critical. The primary aim was to evaluate the efficiency of a novel multi-agent, closed-loop, algorithm responsible for the UAV path-planning of the swarm, in comparison with an efficient a state-of-the-art path-planning EGO methodology, acting as a benchmark. Abbreviated as Block Coordinate Descent Cognitive Adaptive Optimization (BCD-CAO) the novel algorithm outperformed the Efficient Global Optimization (EGO) algorithm, in seven simulation scenarios, demonstrating improved dynamic adaptation of the aerial UAV swarm towards its heterogeneous operational capabilities. The evaluation results presented herein, exhibit the efficiency of the proposed algorithm for continuously conforming the mobile sensing platforms’ formation towards maximizing the total measured density of the diffused volume plume.

Decision-making and optimal design of green energy system based on statistical methods and artificial neural network approaches

• A novel pathway to evaluate optimization algorithms utilizing statistics and ANN. • One-Way ANOVA, Tukey test, and ANN are used to choose the best procedure. • Statistically, the PSO algorithm surpasses all others, but the ANN is better. This study attempts to establish a novel statistical and ANN-based decision-making mechanism for appraising hybrid system optimization strategies. An operational strategy and optimization problem for a hybrid, off-grid PV-wind system based on Nickel Iron battery storage is established in this paper, and an objective function for the system under consideration is presented. Particle swarm optimization, hybrid firefly and harmony search algorithm (HFAHS), Cultural Algorithm, harmony search, and Simulated Annealing are all used to address the optimization issue. A new procedure for selecting the optimal efficient optimization algorithm based on the One-Way ANOVA, the Tukey test, and ANN has been suggested, which allows for effective comparative analysis of algorithms. In the proposed approach, there were just a few steps to determine which algorithm would be the most successful in addressing the specific problem. A comparison of meta-heuristic optimization techniques based on ANN for the optimality of the presented system has been done. Comparing statistical parameters and ANN (i.e., MAE, MAPE, RMSE, and R-squared) to other models verifies the proposed model's efficiency. PSO's R-squared was 99.7%, indicating more accurate predictions. Although the ANN is superior, the PSO algorithm surpasses all other statistically evaluated algorithms.

Can Deep Learning Solve Parametric Mathematical Programming? An Application to 0–1 Linear Programming Through Image Representation

Deep learning has been widely applied in many fields. Efficient optimization algorithms contribute a lot to the enhancement of deep learning. However, reverse studies on how deep learning can solve optimization problems, especially mathematical programming, are relatively scarce. In this work, we aim to initiate a discussion on using deep learning to solve parametric mathematical programming, which can be converted to find a mapping from parameter space to solution space. Given that deep convolutional neural networks (DCNNs) have been successfully applied in the field of computer vision, converting a mathematical programming problem into an image representation may provide the solution. This work takes the 0–1 knapsack problem (KP) as an example to build an original image representation method. After modifying the image details, the proposed method is extended to the general 0–1 linear programming (LP) problem, and a deep learning architecture is designed to solve the transferred computer vision problem. The efficiency of the proposed DCNNs method is validated on a large number of problems, and results show that this method can solve the 0–1 LP accurately and efficiently without iteration. The solution speed is 20 times faster than that of traditional optimization solvers.

Joint Offloading and Resource Allocation Using Deep Reinforcement Learning in Mobile Edge Computing

Mobile edge computation offloading (MECO) has recently emerged as a promising method to support computation-intensive and latency-sensitive applications, significantly saving the battery energy of smart mobile devices (SMDs). However, on the one hand, the energy consumption depends on both the SMD and the MEC server, which makes it necessary to consider these two entities to achieve energy sustainability jointly. On the other hand, for a real-time mobile edge computing (MEC) system, efficient optimization algorithms based on binary offloading have received significant attention, while efficient algorithms for partial offloading under time-varying channels are seldom investigated. In this paper, we propose an energy-efficient algorithm based on deep reinforcement learning to optimize the overall energy cost in a real-time multi-user MEC system. We decompose the energy minimization problem into two sub-problems, where a deep neural network learns the optimal mapping between wireless channels and offloading ratios, and a closed-form solution for the optimal local frequency and a convex optimization algorithm are used to solve the resource allocation sub-problem. Finally, the extensive experiments demonstrate the effectiveness of our proposed algorithm in reducing the total energy consumption of the MECO system against several offloading schemes and achieving low processing latency fit to the time-varying wireless channels.

Singular geometry perturbation based method for shape-topology optimization in unsteady Stokes flow

This paper concerns the topological derivatives and its applications for solving shape and topology optimization problems in fluid mechanics. The fluid flow is governed by the unsteady incompressible Stokes equations in the two dimensional case. We derive a topological sensitivity analysis for this parabolic-type operator. The proposed approach is based on a preliminary estimate describing the variation of the velocity field caused by the presence of a small obstacle inside the fluid flow. We obtain a topological asymptotic expansion for the unsteady Stokes operator valid for a large class of shape functions and an arbitrarily shaped geometric perturbation. Then, the topological gradient is exploited for building an efficient and accurate topology optimization algorithm. Finally, we present some numerical investigations showing the efficiency of the proposed approach.

Robust resource allocation for IRS‐assisted secure cognitive radio networks

In this paper, a robust resource allocation problem in an intelligent reflecting surface (IRS) assisted artificial noise (AN)-aided cognitive radio network is investigated. Considering that the channel state information (CSI) of the eavesdropper channel and interference channel cannot be perfectly known in practice, the aim is to minimise the power consumption of the secondary user transmitter (SU-TX) by jointly optimising the transmit beamforming and covariance matrix of AN at the SU-TX as well as the phase shifts of the IRS. The formulated problem is non-convex and challenging to tackle due to the coupling among multiple variables. An efficient alternative optimisation algorithm to solve it by exploiting the S-procedure and semi-definite relaxation. Simulation results demonstrate that the IRS helps in reducing the power consumption significantly even under imperfect CSI conditions.

Hybrid optimization and modelling of CI engine performance and emission characteristics of novel hybrid biodiesel blends

Different meta-heuristic optimization algorithms have been used in a variety of fields due to their intelligent behavior and fast convergence. However, use of these algorithms in the engine behavior optimization is very-limited. The development of so-called hybrid optimization technique when these algorithms are combined with experimental design technique is an upcoming method in the field of renewable energy. Hence in this research, meta-heuristic optimization algorithms and experimental design methods were combined to optimize the engine behavior. Additionally, artificial neural networks (ANN) were employed to forecast the performance and emission behaviors of a CI engine running on a novel hybrid biodiesel blend of Cucurbita pepo. L (pumpkin) and Prosopis juliflora, mixed with a novel Elaeocarpus ganitrus (Rudraksha) additive. To assess the success of the ANN, four statistical benchmarks (R2, and MSE) were used. Experiments were designed according to Design of Experiments (DOE) rules with performance and emission parameters as outputs. Response surface methodology (RSM) was employed to find the effect of interaction factors. Single objective and multi-objective optimization using highly efficient hybrid RSM-particle swarm optimization (RPSO) and dragon fly algorithm (RMODA) were employed to optimize the response of the obtained RSM equations. The outcomes demonstrated that RSM and ANN were excellent modelling techniques for these kinds of situations, with good accuracy. In addition, ANN's prediction performance (R2 = 0.978 for BTE) was somewhat better than RSM's (R2 = 0.960 for BTE). On the other hand, the PJB20 blend with 5 mL additive increased BTE by 52.8% and reduced BSFC by 34.9% at maximum load. The smoke opacity was lowered by 7.1% when compared to pure diesel, without any engine modifications. CO2 emission was seen to be shortened by 19.14%. Finally, it can be concluded that this novel biodiesel can be possibly utilized in CI engines with no modification and the engine characteristics can be controlled by optimization and prediction models.

An Efficient Multi-objective Optimization Algorithm Exploiting Gradient Enhanced Kriging with Optimally Selected Basis Functions for Electromagnetic Design

An Efficient Multi-objective Optimization Algorithm Exploiting Gradient Enhanced Kriging with Optimally Selected Basis Functions for Electromagnetic Design

A performance efficient joint clustering and routing approach for heterogeneous wireless sensor networks

AbstractWireless sensor networks have proven to be a promising paradigm due to their wide applicability in the real‐world scenarios. Because of the resource constraint, type‐variant and battery‐powered features of the sensor nodes, the key challenge is how to improve the energy usage and network life in the heterogeneous sensor networks. In this paper, the Mahalanobis distance based K‐Means algorithm is integrated with a novel evolutionary approach for clustering called calf search optimization algorithm (K‐CSOA) that creates energy efficient clusters. The routing in the network is performed using a power efficient ant colony optimization algorithm (ACO). The extensive and multiple simulations are performed to validate the effectiveness of the proposed method. It is observed that the running time of the proposed scheme is lowered due to the implementation of hybrid clustering. The results are validated for various performance metrics wherein the proposed method shows 41.6% and 4.3%–12% reduction in energy expenditure and end‐to‐end delay respectively and around 60% enhancement in the throughput of the network when compared with other similar state‐of‐the‐art methods. In addition to this, statistical analysis is also carried out to identify the performance of the proposed algorithm.