Random Optimization Algorithm Research Articles

Optimization based design of a wideband near zero refractive index metasurface for gain improvement of planar antennas in the terahertz band

In this paper, a broadband near-zero refractive index metasurface is designed in the terahertz band. The Random Hill Climbing optimization algorithm is utilized to propose the corresponding unit cell. The designed metasurface is employed as the superstrate of two planar microstrip wideband antennas resulting to increase the gains of antennas. The impedance and 3-dB gain bandwidths of loaded patch antenna are 46% and 36%, respectively while these parameters are 17% and 28.5% for printed dipole antenna in the presence of designed metasurface. Moreover, the maximum gain of loaded patch and printed dipole antennas are 9.1 dB and 11.74 dB, consequently. The provided results confirm that the proposed structures achieve higher maximum gain and wide 3-dB gain and impedance bandwidths with respect to similar reported antennas. In addition, at least 3 dB gain improvement is achieved in the presence of designed metasurface on the top of antennas.

Investigating quantum approximate optimization algorithms under bang-bang protocols

The quantum approximate optimization algorithm (QAOA) is widely seen as a possible usage of noisy intermediate-scale quantum (NISQ) devices. We analyze the algorithm as a bang-bang protocol with fixed total time and a randomized greedy optimization scheme. We investigate the performance of bang-bang QAOA on MAX-2-SAT, finding the appearance of phase transitions with respect to the total time. As the total time increases, the optimal bang-bang protocol experiences a number of jumps and plateaus in performance, which match up with an increasing number of switches in the standard QAOA formulation. At large times, it becomes more difficult to find a globally optimal bang-bang protocol and performances suffer. We also investigate the effects of changing the initial conditions of the randomized optimization algorithm and see that better local optima can be found by using an adiabatic initialization.

Pattern Synthesis of Linear Antenna Array Using Improved Differential Evolution Algorithm with SPS Framework

In this paper, an improved differential evolution (DE) algorithm with the successful-parent-selecting (SPS) framework, named SPS-JADE, is applied to the pattern synthesis of linear antenna arrays. Here, the pattern synthesis of the linear antenna arrays is viewed as an optimization problem with excitation amplitudes being the optimization variables and attaining sidelobe suppression and null depth being the optimization objectives. For this optimization problem, an improved DE algorithm named JADE is introduced, and the SPS framework is used to solve the stagnation problem of the DE algorithm, which further improves the DE algorithm’s performance. Finally, the combined SPS-JADE algorithm is verified in simulation experiments of the pattern synthesis of an antenna array, and the results are compared with those obtained by other state-of-the-art random optimization algorithms. The results demonstrate that the proposed SPS-JADE algorithm is superior to other algorithms in the pattern synthesis performance with a lower sidelobe level and a more satisfactory null depth under the constraint of beamwidth requirement.

Hyperspectral remote sensing image classification based on dense residual three-dimensional convolutional neural network

The invention discloses a hyperspectral remote sensing image classification method based on a dense residual three-dimensional convolutional neural network. According to the method, original hyperspectral data are used as network input, three-dimensional spatial-spectral features of a hyperspectral remote sensing image are extracted through three-dimensional convolution, the hyperspectral image can be directly processed through three-dimensional convolution, preprocessing operations such as dimension reduction are not needed, and the spatial-spectral features of the hyperspectral image are extracted more sufficiently. The dense residual network is used to deepen the number of network layers and learn deeper spectral and spatial features, the residual network can effectively reduce the problem of gradient disappearance along with the increase of the network depth, and the structure can more effectively utilize the features and enhance the feature transfer between convolutional layers. The training time is shortened through an early stop method, classification prediction is carried out through a Soft-max classifier, and an initial classification result is obtained; and proposing a multi-label conditional random field optimization algorithm, and optimizing a classification result. The method improves the operation efficiency, and improves the classification accuracy of the remotesensing images.

Integrated continuous berth allocation and quay crane assignment and scheduling problem with time-dependent physical constraints in container terminals

This paper presents an integer programming model for integrated continuous berth allocation, quay crane assignment and quay crane scheduling problem (BACASP) in container terminals where berthing possibility depends on water depth and tide conditions. The proposed model also considers the safe distance between quay cranes and the fact that they cannot cross each other on the rails. Given the NP-Hard complexity of the proposed model, a particle swarm optimization (PSO) based meta-heuristic called the random topology particle swarm optimization algorithm (RTPSO) is developed for solving its large-size instances. To evaluate the performance of the developed RTPSO, its results are compared with the results of the exact solution and the basic PSO. The results illustrate the better performance of the proposed random topology particle swarm optimization algorithm in terms of accuracy and computational time.

Prediction of tool wear width size and optimization of cutting parameters in milling process using novel ANFIS-PSO method

In the process of intelligent manufacturing, appropriate learning algorithm and intelligent model are necessary. In this work, a novel learning algorithm named random vibration and cross particle swarm optimization algorithm was proposed. The proposed algorithm is used for the prediction and optimization of machining process. Tool wear is an important factor that affects the machined surface quality during machining process, so it is necessary to find qualified tool wear prediction model and obtain the best combination of machining parameters to prolong tool life. In this study, the adaptive network–based fuzzy inference system was established to predict the tool wear width size. The random vibration and cross particle swarm optimization algorithm was tested using benchmark functions, and the results showed that random vibration and cross particle swarm optimization algorithm is able to find global optimum. Compared with the adaptive network–based fuzzy inference system trained by particle swarm optimization algorithm and adaptive network–based fuzzy inference system trained by differential evolution models, the results showed that the adaptive network–based fuzzy inference system trained by random vibration and cross particle swarm optimization algorithm can give a more accurate predicted value for offline prediction of the tool wear width size. In order to obtain the best combinations of cutting parameters under different removal area, the multi-objective optimization based on random vibration and cross particle swarm optimization algorithm was established. The optimized cutting parameters were verified and could be accepted to prolong tool life and improve machining efficiency.

Randomized optimal consensus of multiagent systems based on a novel intermittent projected subgradient algorithm

SummaryIn this article, a novel intermittent projected subgradient algorithm is presented to solve the randomized optimal consensus problem for heterogeneous multiagent systems with time‐varying communication topologies. The multiagent systems achieve the consensus meanwhile minimizing the global objective function via the proposed algorithm, where fi(x) is the convex objective function of agent i itself. Due to the common Bernoulli distribution adopted in the existing random optimization algorithm without considering the different computing capability of each agent. An individual projection probability is assigned for each agent based on computing capabilities so that either making projection or taking average is chosen according to the above probability which can effectively avoid overload for some agents with lower computing capabilities and improve the reliability of the overall systems. A new sufficient step‐size condition is given to ensure all agents converge to the optimal solution with probability one. Finally, a numerical example is also given to validate the proposed method.

Diversity-guided Lamarckian random drift particle swarm optimization for flexible ligand docking

BackgroundProtein-ligand docking has emerged as a particularly important tool in drug design and development, and flexible ligand docking is a widely used method for docking simulations. Many docking software packages can simulate flexible ligand docking, and among them, Autodock is widely used. Focusing on the search algorithm used in Autodock, many new optimization approaches have been proposed over the last few decades. However, despite the large number of alternatives, we are still lacking a search method with high robustness and high performance.ResultsIn this paper, in conjunction with the popular Autodock software, a novel hybrid version of the random drift particle swarm optimization (RDPSO) algorithm, called diversity-guided Lamarckian RDPSO (DGLRDPSO), is proposed to further enhance the performance and robustness of flexible ligand docking. In this algorithm, a novel two-phase diversity control (2PDC) strategy and an efficient local search strategy are used to improve the search ability and robustness of the RDPSO algorithm. By using the PDBbind coreset v.2016 and 24 complexes with apo-structures, the DGLRDPSO algorithm is compared with the Lamarckian genetic algorithm (LGA), Lamarckian particle swarm optimization (LPSO) and Lamarckian random drift particle swarm optimization (LRDPSO). The experimental results show that the 2PDC strategy is able to enhance the robustness and search performance of the proposed algorithm; for test cases with different numbers of torsions, the DGLRDPSO outperforms the LGA and LPSO in finding both low-energy and small-RMSD docking conformations with high robustness in most cases.ConclusionThe DGLRDPSO algorithm has good search performance and a high possibility of finding a conformation with both a low binding free energy and a small RMSD. Among all the tested algorithms, DGLRDPSO has the best robustness in solving both holo- and apo-structure docking problems with different numbers of torsions, which indicates that the proposed algorithm is a reliable choice for the flexible ligand docking in Autodock software.

DIAGNOSIS OF CORONARY BLOCKAGE OF ARTERY USING MRI/CTA IMAGES THROUGH ADAPTIVERANDOM FOREST OPTIMIZATION

Heart attacks and diseases are normally observed due lack of Real time-Fast diagnosis. In that the fundamental and main cause is troubles in coronary blockage of artery (CBA).There are many ways to diagnosis this issue, but it takes more time and complexity function, the main purpose of this work is to give an easy technique for Heart surgery clinical purpose with accurate results. For this consider the open heart surgery CTA images are selected as experimental elements. Here for Accurate diagnosis and operation, clear MRI/CTA images of heart are required i.e., coronary tomography angiography (CTA) images are collected from real time clinical centers. Fast and accurate detections are possible only with the help of image processing techniques (IMT) through artificial intelligence algorithms (AIA). Random forest optimization algorithm is used for taking decisions via feature extraction and classification. Total work consists of 3 steps i.e., first step is loading the image through segmentation, 2nd step is preprocessing, Here the selected particular MRI/CTA heart images are feature extracted and in 3rd step is taking decision by using Adaptive Random Forest algorithm (ARF). Therefore this application achieves accuracy is 98.74%, Precision 97.81 ,Recall 98.14 , F1 score 93.87 PSNR 57.26 CC 0.0987 Sensitivity 98.45, so this work is compete with present technologies and very useful at fast & accurate diagnosis center for Heart patients.

RETRACTED ARTICLE: Research on English teaching system based on artificial intelligence and WBIETS wireless network system

The English teaching network system is a distant teaching based on the Web. This teaching method can stimulate students’ interests so that students can acquire knowledge voluntarily, and automatic test paper generation is one of the most important modules in the English teaching network system. This article first briefly introduces the architecture of the wireless sensor network and then gives a wireless sensor network teaching experiment system based on a genetic algorithm. The multiple sensor nodes in the system can form a variety of different topologies, the collected data can be sent to the user terminal through the GSM network, and the user can also control the remote sensor node through the GSM network. This paper first describes the automatic test problems, a constrained multi-objective problem, then the design of the genetic algorithm to improve the test paper and puts forward questions based on an encoding method and based on the difficulty and test points of F fitness function for dynamic adjustment of the parameters in the iterative process. Finally, it is verified by experiments that the test paper made by this method satisfies users’ requests for questions, contents, and scores, and at the same time, it also improves the running efficiency of a random optimization algorithm by 7–17 times.

An Ensemble Technique for Named Entity Recognition using Conditional Random Fields and L-BFGS Optimization Algorithm

An Ensemble Technique for Named Entity Recognition using Conditional Random Fields and L-BFGS Optimization Algorithm

Optimal Siting and Sizing of Battery Energy Storage System for Distribution Loss Reduction Based on Meta-heuristics

This paper presents an optimal sitting and sizing model of a lithium-ion battery energy storage system for distribution network employing for the scheduling plan. The main objective is to minimize the total power losses in the distribution network. To minimize the system, a newly developed version of cayote optimization algorithm has been introduced and validated based on different test functions and verified by some well-known meta-heuristics. The proposed algorithm is then applied to four different case studies in the presence of two photo voltaic (PVs) and without them. The optimization of the problems is applied based on two different trends including simultaneous and step-by-step optimization. Final results are compared with some meta-heuristics including cuckoo optimization algorithm, lion optimization algorithm, and cooperative random particle swarm optimization algorithm to show the algorithm higher efficiency. The results also showed that the presence of PVs reduces the total power losses and applying the simultaneous optimization gives higher efficiency with less total power losses.

Prediction of soil humidity based on random weight Particle Swarm Optimized Extreme Learning Machine

The prediction of high quality soil moisture is of great significance to agricultural production and scientific research. In order to solve the problem that the prediction results of (ELM) regression model of limit learning machine are affected by input parameters, the stochastic weight particle swarm optimization algorithm (RandWPSO) is applied to ELM regression model. In this paper, a soil moisture prediction method of particle swarm optimization limit learning machine based on random inertia weight is proposed. In this method, the data of soil temperature and light intensity measured by sensor are used to preprocess the data, the training sample set is constructed, and the ELM regression model is established. The input weight and threshold in ELM are optimized by using random weight particle swarm optimization algorithm to avoid falling into local optimization, thus the prediction model of soil moisture based on RandWPSO-ELM is established. The soil moisture of sugar beet in Hulan area was studied. The experimental results show that the method has high accuracy and stability, and can provide an effective reference for the growth of sugar beet in greenhouse.

RETRACTED ARTICLE: RWFOA: a random walk-based fruit fly optimization algorithm

As one kind of the classic swarm intelligence (SI) algorithms, fruit fly optimization algorithm has been widely used in many aspects, such as multiple objective optimization and service computing. However, due to the limitation that introduced by the initial population location, its global optimization ability is limited. Therefore, we propose a random walk-based fruit fly optimization algorithm, namely RWFOA, to enhance its global optimization ability. RWFOA employs the random walk mechanism to dynamically adjust the position of the fruit fly population, which can reduce the impact of the initial population location and thus enhance the global optimization ability. We conduct a comprehensive experimental evaluation of RWFOA by comparing it with three representative algorithms, i.e., the OFOA, CFOA and IFFO, over 29 widely used benchmark functions published in CEC 2015. Experimental results demonstrate that RWFOA can find better solutions in most of the selected benchmark functions.

Enhanced fuzzy C‐means clustering based cooperative spectrum sensing combined with multi‐objective resource allocation approach for delay‐aware CRNs

In cognitive radio networks (CRNs), the resource allocation is viewed as a multi-objective optimisation problem in terms of limitation of quality of service, the capacity of a channel and the transmitted power. To overcome these individual issues many researchers have been undertaken, but it does not solve the multi-objective problems. In this study, the authors propose a multi-objective random walk grey wolf optimisation (MORWGWO) algorithm to enhance the resource allocation in the CRNs. Here, they combined the spectrum sensing process with the resource allocation process. Initially, an enhanced fuzzy C-means algorithm based cluster formation is proposed for spectrum sensing and then they model the resource allocation process and propose a MORWGWO algorithm for CRNs which generates the Pareto front inbetween of different objectives in a time-efficient manner. The simulation result shows that the proposed method significantly enhances the network performance in terms of delay, delivery ratio, throughput, network lifetime, energy consumption, and fairness index. Result shows that the proposed method has a better throughput of 23.22, 15.09, 6.13% for varying nodes and 27.25, 10.62, 7.09% for varying data transfer rates while comparing with the multiple objective particle swarm optimisation, non-dominated sorting genetic algorithm, and multi-objective evolutionary algorithm.

An improved dynamic cooperative random drift particle swarm optimization algorithm based on search history decision

A novel dynamic cooperative random drift particle swarm optimization algorithm based on entire search history decision (CRDPSO) is reported. At each iteration, the positions and the fitness values of the evaluated solutions in the algorithm are stored by a binary space partitioning tree structure archive, which leads to a fast fitness function approximation. The mutation is adaptive and parameter-less because of the fitness function approximation enhancing the mutation strategy. The dynamic cooperation between the particles by using the context vector increases the population diversity helps to improve the search ability of the swarm and cooperatively searches for the global optimum. The performance of CRDPSO is tested on standard benchmark problems including multimodal and unimodal functions. The empirical results show that CRDPSO outperforms other well-known stochastic optimization methods.

Hdconfigor: Automatically Tuning High Dimensional Configuration Parameters for Log Search Engines

Search engines are nowadays widely applied to store and analyze logs generated by large-scale distributed systems. To adapt to various workload scenarios, log search engines such as Elasticsearch usually expose a large number of performance-related configuration parameters. As manual configuring is time consuming and labor intensive, automatically tuning configuration parameters to optimize performance has been an urgent need. However, it is challenging because: 1) Due to the complex implementation, the relationship between performance and configuration parameters is difficult to model and thus the objective function is actually a black box; 2) In addition to application parameters, JVM and kernel parameters are also closely related to the performance and together they construct a high dimensional configuration space; 3) To iteratively search for the best configuration, a tool is necessary to automatically deploy the newly generated configuration and launch tests to measure the corresponding performance. To address these challenges, this paper designs and implements HDConfigor, an automatic holistic configuration parameter tuning tool for log search engines. In order to solve the high dimensional optimization problem, we propose a modified Random EMbedding Bayesian Optimization algorithm (mREMBO) in HDConfigor which is a black-box approach. Instead of directly using a black-box optimization algorithm such as Bayesian optimization (BO), mREMBO first generates a lower dimensional embedded space through introducing a random embedding matrix and then performs BO in this embedded space. Therefore, HDConfigor is able to find a competitive configuration automatically and quickly. We evaluate HDConfigor in an Elasticsearch cluster with different workload scenarios. Experimental results show that compared with the default configuration, the best relative median indexing results achieved by mREMBO can reach $2.07\times $ . In addition, under the same number of trials, mREMBO is able to find a configuration with at least a further 10.31% improvement in throughput compared to Random search, Simulated Annealing and BO.

基于网络分布式随机多任务优化算法

基于网络分布式随机多任务优化算法

Efficient optimization technique for multiple DG allocation in distribution networks

In the last few decades, interest in the integration of Distributed Generators (DGs) into distribution networks has been increased due to their benefits such as enhance power system reliability, reduce the power losses and improve the voltage profile. These benefits can be increased by determining the optimal DGs allocation (location and size) into distribution networks. This paper proposes an efficient optimization technique to optimally allocate the multiple DG units in distribution networks. This technique is based on Sine Cosine Algorithm (SCA) and chaos map theory. As any random search-based optimization algorithm, SCA faces some issues such as low convergence rate and trapping in local solutions during the exploration and exploitation phases. This issue can be addressed by developing Chaotic SCA (CSCA). CSCA is mainly based on the iterative chaotic map which used to update the random parameters of SCA instead of using the random probability distribution. The iterative chaotic map is applied for single and multi-objective SCA. The proposed technique is validated using two stranded IEEE radial distribution feeders; 33 and 69-nodes. Comprehensive comparison among the proposed technique, the original SCA, and other competitive optimization techniques are carried out to prove the effectiveness of CSCA. Finally, a complete study is performed to address the impact of the intermittent nature of renewable energy resource on the distribution system. Hence, typical loads and generation (represented in PV power) profiles are applied. The result proves that the CSCA is more efficient to solve the optimal multiple DGs allocation with minimum power loss and high convergence rate.

A New Multi-Objective Ant Colony Optimisation Algorithm for Solving the Quadratic Assignment Problem

The multi-objective quadratic assignment problem (mQAP) is an NP-hard combinatorial optimisation problem. Real world problems are concerned with multi-objective problems which optimise more objective functions simultaneously. Moreover, QAP models many real-world optimisation problems, such as network design problems, communication problems, layout problems, etc. One of its major applications is the facility location, which is to find an assignment of all facilities to all locations in the way their total is minimised. The multi-objective QAP considers multiple types of flows between two facilities. Over the last few decades several meta-heuristic algorithms have been proposed to solve the multi-objective QAP, such as genetic algorithms, Tabu search, simulated annealing, and ant colony optimisation. This paper presents a new ant colony optimisation algorithm for solving multiple objective optimisation problems, and it is named as the random weight-based ant colony optimisation algorithm (RWACO). The proposed algorithm is applied to the bi-objective quadratic assignment problem and evaluates the performance by comparing with some recently developed multiobjective ant colony optimisation algorithms. The experimental results have shown that the proposed algorithm performs better than the other multi-objective ACO algorithms considered in this study. Keywords: ACO, multi-objective problem, QAP, travelling salesman problem