Effective Optimization Algorithm Research Articles

Optimal sensor placement of bridge structure based on sensitivity‐effective independence method

Taking the optimal sensor placement problem in bridge structural health monitoring as the study object and relying on the engineering example of a simply supported steel truss bridge, the improved optimal sensor placement method based on sensitivity-effective independence method was proposed. Using the sensitivity coefficient reflecting structural damage, the proposed method could modify the effective independence method reflecting the maximum linear independence. The proposed method further optimizes the sensor placement method. A method for selecting the number of models based on modal closeness was proposed and makes the selection of the number of modes more objective. The example analysis shows that evaluations using this method are effective for multiple evaluation criteria. The method ensures observability of the mode vector and identifiability of structural damage. It is an effective optimal sensor placement algorithm for the bridge structure.

Integrating $$\varepsilon $$-dominance and RBF surrogate optimization for solving computationally expensive many-objective optimization problems

Multi-objective optimization of computationally expensive, multimodal problems is very challenging, and is even more difficult for problems with many objectives (more than three). Optimization methods that incorporate surrogates within iterative frameworks, can be effective for solving such problems by reducing the number of expensive objective function evaluations that need to be done to find a good solution. However, only a few surrogate algorithms have been developed that are suitable for solving expensive many-objective problems. We propose a novel and effective optimization algorithm, $$\varepsilon $$ -MaSO, that integrates $$\varepsilon $$ -dominance with iterative Radial Basis Function surrogate-assisted framework to solve problems with many expensive objectives. $$\varepsilon $$ -MaSO also incorporates a new strategy for selecting points for expensive evaluations, that is specially designed for many-objective problems. Moreover, a bi-level restart mechanism is introduced to prevent the algorithm from remaining in a local optimum and hence, increase the probability of finding the global optimum. Effectiveness of $$\varepsilon $$ -MaSO is illustrated via application to DTLZ test suite with 2 to 8 objectives and to a simulation model of an environmental application. Results on both test problems and the environmental application indicate that $$\varepsilon $$ -MaSO outperforms the other two surrogate-assisted many-objective methods, CSEA and K-RVEA, and an evolutionary many-objective method Borg within limited budget.

A novel musical chairs algorithm applied for MPPT of PV systems

Due to the multiple peaks generated in the power to voltage characteristics of partially shaded photovoltaic (PV) arrays there is an urgent need for an effective optimization algorithm to capture its global peak instead of the local peaks. The required optimization algorithm should converge very fast and accurately capture the global peak. Many metaheuristic optimization algorithms have been introduced to tackle this problem and balance exploration and exploitation performances. These algorithms use a constant number of searching agents (swarm size) through all iterations. The maximum power point tracker (MPPT) of the PV system requires high numbers of searching agents in the initial steps of optimization to enhance explorations, whereas the final stage of optimization requires lower numbers of searching agents to enhance exploitations, which are conditions that are currently unavailable in optimization algorithms. This was the research gap that was the main motive of creating the new algorithm introduced in this paper, where a high number of searching agents is used at the beginning of the optimization steps to enhance exploration and reduce the convergence failure. The number of searching agents should be reduced gradually to have a lower number of search agents at the end of searching steps to enhance exploitation. This need is inspired by the well-known musical chairs game in which the players and chairs start with high numbers and are reduced one by one in each round which enhances the exploration at the start of the search and exploitation at the end of the search steps. For this reason, a novel optimization algorithm called the musical chairs algorithm (MCA) is introduced in this paper. Using the MCA for MPPT of PV systems considerably provided lower convergence times and failure rates than other optimization algorithms. The convergence time and failure rate are the crucial factors in assessing the MPPT because they should be minimized as much as possible to improve the PV system efficiency and assure its stability especially in the high dynamic change of shading conditions. The convergence time was reduced to 20%–50% of those obtained using five benchmark optimization algorithms. Moreover, the oscillations at steady state is reduced to 20%–30% of the values associated the benchmark optimization algorithms. These results prove the superiority of the newly proposed MCA in the MPPTs of the PV system.

Problem-specific multi-objective invasive weed optimization algorithm for reconnaissance mission scheduling problem

With the progress of technology, the multi-agent system is successfully applied in many applications. In this paper, we investigate the problem of multi-agent system reconnaissance mission scheduling, which is the core of the reconnaissance decision support system and can be modeled as an extension of Multi-Mode Multi-Skill Resource-Constrained Project Scheduling Problem. Three objectives are considered in this paper: (1) minimizing the reconnaissance mission’s makespan, (2) minimizing the total cost of allocating reconnaissance agents, and (3) maximizing the total quality of all reconnaissance tasks. An effective problem-specific multi-objective invasive weed optimization algorithm (PS-MOIWO) is proposed for solving the problem. Firstly, a new chromosome structure guaranteeing the feasibility of solutions and an initialization method are proposed. Secondly, we propose a self-adaptive penalty-based constraint handling technique to describe the fitness of each individual and adopt a novel non-dominated sorting method to rank the population. Thirdly, by using the problem-specific knowledge, a local search procedure is developed and incorporated into the PS-MOIWO framework to enhance the exploitation ability. Based on the Taguchi method, algorithm’s suitable parameter combinations are determined. Simulation results based on a set of newly generated reconnaissance instances and the comparisons with some existing algorithms demonstrate the proposed algorithm’s effectiveness.

Multi-objective multi-mode resource-constrained project scheduling with fuzzy activity durations in prefabricated building construction

Different from traditional project management, the prefabricated building (PB) construction project has a complex distributed supply chain model, and the overall project is completed by multi-stage cooperation. Therefore, the implementation process will be restricted by many constraints, and various uncertain factors will also interfere with the smooth implementation of the project. In order to improve the stability and reliability of the PB construction project implementation process, it is very important to study an effective robust project scheduling method considering resource constraints in an uncertain environment. In this paper, we formulate a PB construction resource-constrained project scheduling with multi-objective multi-mode, focus on the uncertainty of the execution time of the execution activity, and constructs the interval value of the execution time to express it through fuzzy theory; also considers the multiple objectives of PB construction project, including time-based profit, and cost-based profit. Secondly, we propose a hybrid cooperative co-evolution algorithm (HCOEA) to obtain the highly robust project scheduling, reduce the impact of the uncertainty of the execution time of the activity on the overall project. Resource-constrained project scheduling problem (RCPSP) is an NP-hard combinatorial optimization problem. This paper also needs to consider the complex combination of time-resource and/or time–cost constraints. At the same time, it is necessary to consider the impact of time changes in different mode combinations. Therefore, how to design an effective multi-objective optimization algorithm is very difficult. This paper design a Hybrid Cooperative Co-evolution Algorithm (HCOEA) with multi-stage representation for the activity sequencing and the resource allocation, further improve the search efficiency. We improve the cooperative co-evolution framework with a self-adaptive mechanism and a self-adaptive selection process. Finally, benchmarks and extended datasets with fuzzy processing time are adopted to test our HCOEA. Computational results show that the HCOEA performs better than the existing state-of-the-art methods.

Wind speed forecasting system based on gated recurrent units and convolutional spiking neural networks

Deep recurrent neural networks, such as gated recurrent units and long short-term memories, have been widely applied in wind speed forecasting. However, the simulations of the dynamics of the neurons in these models are different from the dynamics of natural neurons, and the useful temporal information is not fully extracted. This results in an unsatisfactory forecasting accuracy for practical wind energy management. In this study, under the hypothesis that a wind speed series can be forecasted using only previous observations (without any other information from the outer environment), a hybrid dual temporal information wind speed forecasting system comprising a third-generation spiking neural network is proposed, aiming to better extract temporal information. A fluctuating feature decomposition strategy is adopted to separate the different modes and adaptively transform the original series into several subseries. Subsequently, the third-generation spiking neural network is integrated with a convolution operation to correct and optimize the forecasting performance of a single recurrent deep learning model. Finally, an effective optimization algorithm is applied to obtain a linear combination of the forecasting outputs of each subseries. Four wind datasets collected from the Liaotung Peninsula in China are used to verify the effectiveness of the designed forecasting system. The experiments indicate that the proposed forecasting system achieves MAPEhengshan=1.43%, MAPExianren=1.40%, MAPEdonggang=1.49%, and MAPEdandong=2.56%, thereby showing excellent forecasting performance.

An Effective Hyperparameter Optimization Algorithm for DNN to Predict Passengers at a Metro Station

As one of the public transportation systems, metro is certainly an indispensable part in urban areas of a metropolis today. Several successful results have shown that deep learning technologies might provide an effective way to predict the number of passengers at a metro station. However, most information systems based on deep learning technologies are usually designed and tuned manually by using domain knowledge and trial-and-error; thus, how to find out a set of suitable hyperparameters for a deep neural network (DNN) has become a critical research issue. To deal with the problem of hyperparameter setting for a DNN in solving the prediction of passengers at a metro station, a novel metaheuristic algorithm called search economics for hyperparameter optimization is presented to improve the accuracy rate of such a prediction system. The basic idea of the proposed algorithm is to divide the solution space into a set of subspaces first and then assign a different number of search agents to each subspace based on the “potential of each subspace.” The potential is estimated based on the objective values of the searched solutions, the objective values of the probe solutions, and the computation time invested in each subspace. The proposed method is compared with Bayesian, random forest, support vector regression, DNN, and DNN with different hyperparameter search algorithms, namely, grid search, simulated annealing, and particle swarm optimization. The simulation results using the data provided by the government of Taipei city, Taiwan, indicate that the proposed method outperforms all the other forecasting methods compared in this article in terms of the mean absolute percentage error.

A knowledge‐enhanced deep reinforcement learning‐based shape optimizer for aerodynamic mitigation of wind‐sensitive structures

AbstractStructural shape optimization plays an important role in the design of wind‐sensitive structures. The numerical evaluation of aerodynamic performance for each shape search and update during the optimization process typically involves significant computational costs. Accordingly, an effective shape optimization algorithm is needed. In this study, the reinforcement learning (RL) method with deep neural network (DNN)‐based policy is utilized for the first time as a shape optimization scheme for aerodynamic mitigation of wind‐sensitive structures. In addition, “tacit” domain knowledge is leveraged to enhance the training efficiency. Both the specific direct‐domain knowledge and general cross‐domain knowledge are incorporated into the deep RL‐based aerodynamic shape optimizer via the transfer‐learning and meta‐learning techniques, respectively, to reduce the required datasets for learning an effective RL policy. Numerical examples for aerodynamic shape optimization of a tall building are used to demonstrate that the proposed knowledge‐enhanced deep RL‐based shape optimizer outperforms both gradient‐based and gradient‐free optimization algorithms.

Accessing dynamic functional connectivity using [formula omitted]-regularized sparse-smooth inverse covariance estimation from fMRI

Inferring dynamic functional connectivity (dFC) from functional magnetic resonance imaging (fMRI) is crucial to understand the time-variant functional inter-relationships among brain regions. Because of the sparse property of functional connectivity networks, sparsity-promoting dFC estimation methods, which are mainly based on l1-norm regularization, are gaining popularity. However, l1-norm regularization cannot provide the maximum sparsity solution as the most natural sparsity promoting norm, the l0-norm. But l0-norm is seldom used to infer sparse dFC because an efficient algorithm to address the non-convexity problem of l0-norm is lacking. In this work, we develop a new l0-norm regularization-based inverse covariance estimation method for estimating dFC from fMRI. This novel method employs l0-norm regularizations on both spatial and temporal scales to enhance the spatial sparsity and temporal smoothness of dFC estimates. To overcome the non-convexity of l0-norm, we further propose an effective optimization algorithm based on the coordinate descent (CD). The performance of the proposed l0-norm-based sparse-smooth regularization (L0-SSR) method is examined using a series of synthetic datasets concerning various types of network topology. We further apply the proposed L0-SSR method to real fMRI data recorded in block-design motor tasks from 45 participants for the exploration of task induced dFC. Results on synthetic and real-world fMRI data show that, the L0-SSR method can achieve more accurate and interpretable dFC estimates than conventional l1-norm-based dFC estimation methods. Hence, the proposed L0-SSR method could serve as a powerful analytical tool to infer highly complex, variable, and sparse dFC patterns.

Output space transfer based multi-input multi-output Takagi–Sugeno fuzzy modeling for estimation of molten iron quality in blast furnace

The molten iron quality (MIQ) in the blast furnace (BF) needs on-site sampling and laboratory tests, which is very unfavorable to the control. To estimate multiple molten iron quality indicators (MIQIs) such as molten iron temperature (MIT), silicon content ([Si]), phosphorus content ([P]) and sulfur content ([S]) in real-time, some data-driven multi-input multi-output (MIMO) modeling methods have lately been developed. However, those modeling methods ignore the inter-indicator correlation. Moreover, the above modeling methods do not consider the processing strategy for the samples with missing indicators. To conquer the above two problems in the process of modeling, this paper proposed an output space transfer based MIMO Takagi–Sugeno (T–S) fuzzy modeling method for the four MIQIs. Through sparse learning of the correlation matrix, the inter-indicator correlation is specifically modeled. Due to the full use of inter-indicator correlation, the impact of missing indicators on modeling accuracy is greatly reduced. An effective alternative optimization algorithm is also provided for the corresponding intractable optimization problem. The actual data test shows that the proposed method can greatly improve the accuracy of multiple MIQIs estimations.

Exploitation whale optimization based optimal offloading approach and topology optimization in a mobile ad hoc cloud environment

Widespread availability of network technologies, mobile user request is increased day by day. The larger amount of energy utilization and resource sufficiency of cloud computing is to create the maximum capacity of exploration and exploitation as troublesome. In this paper, we proposed the formation of mobile user behavior based topology and its optimization. During the offloading process, the minimization of response time and the energy consumption is the major goal of this paper. The topology node formations are performed via improved text rank algorithm (ITRA) and neural network (NN) classifiers with Euclidian distance. In this paper, we introduced an effective optimization algorithm of the exploitation whale optimization algorithm (EWOA) and it is the combination of differential evaluation (DE) and whale optimization algorithms (WOA). The offloading process of proposed EWOA produces an optimal outcome of minimized energy consumption and response time. The implementation works of the proposed EWOA are carried out in the VMware platform. The performance of the proposed method is evaluated using different size puzzles, face detection applications, and state-of-art methods. Ultimately, our proposed method produces optimal accuracy and convergence speed with the minimized offloading process.

An improved firefly algorithm based on personalized step strategy

Firefly algorithm has shown good performance for solving various optimization problems. Most of the nature-inspired algorithms have the same problem which can easily trap in local optimal. To overcome this defect, a novel personalized step strategy for firefly algorithm (PSSFA) is presented. It uses large step for the optimal firefly and linearly decreasing step for the other fireflies to improve the ability of exploration. Experiments on 20 test functions show that the proposed algorithm can promote accuracy of the original method. Finally, we integrate PSSFA with k-means clustering for five datasets. The results show that PSSFA is an effective optimization algorithm.

Blind motion deconvolution for binary images

Binary images are prevalent in digital systems and have a wide range of applications including texts, fingerprint recognition, handwritten signatures, stellar astronomy, barcodes, and vehicle license plates. The recorded binary images are often degraded by blur and additive noise due to environmental effects and imperfections in the imaging system. In this paper, we study the problem of recovering the sharp binary image and the blur kernel from the motion degraded observation. We propose a new minimization model by using the binary prior of image pixel and the l0 norm of image gradient to enforce the estimated image to be binary and the image gradient to be sparse respectively. An effective numerical optimization algorithm is applied for solving the proposed model. Extensive experiments for blind binary image deconvolution demonstrate that the proposed method outperforms some existing state-of-the-art methods in terms of visual quality and peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).

Cellular automata-based optimised routing for secure data transmission in wireless sensor networks

ABSTRACT Sensor nodes encompass the capability of controlling and monitoring the environment by measuring the factors, like temperature, pressure, and humidity. Ensuring secure routing in the network is an important and significant task in the wireless sensor network (WSN). Even though various routing protocols are adopted in the network, providing security and maintaining the energy is still a challenging issue in the research community. Thus, an effective particle-based spider monkey optimisation (P-SMO) algorithm is proposed in this research to select the optimal route to manage secure communication in WSNs. Accordingly, the network is simulated and the cluster heads (CHs) are chosen based on the efficient learning automata-based cell clustering algorithm (ELACCA) such that the routing path is established based on the chosen CHs. Thus, the proposed P-SMO algorithm establishes the secure routing path based on the factors, such as energy, delay, consistency factor, and trust. The proposed P-SMO algorithm is the integration of particle swarm optimisation (PSO) and spider monkey optimisation (SMO) algorithm in such a way that the optimal routes are defined effectively. The analysis of the proposed routing protocol proved the effective performance with the maximal number of nodes alive, coverage, energy balancing index, and the average remaining energy of 67, 93.407%, 0.7555, and 0.3402 J, respectively.

Shuffled Frog Leaping Algorithm Based Neural Network and Its Application in Big Data Set

This paper studies the random optimization algorithm to reorganize the leapfrog algorithm. The big data challenge requires an effective optimization algorithm to explore potential data structures using deep neural networks. At first, we introduce the neural network classifier and compare it with the support vector machine. Neural networks are suitable for large data sets and have the complex ability to extract high-level abstract data. And then we have to introduce a large dataset covering cancer data and voice data. Both datasets have large numbers of samples with complex low-level variance. At last we have to use the reorganized leapfrog algorithm to optimize neural network parameters. The random leapfrog algorithm is efficient and robust to a local minimum. The experimental results show that the algorithm has extensive application prospects and is suitable for the classification of big dataset. The neural network parameters can effectively optimized by the improved shuffled frog leaping algorithm.

Joint Transformation Learning via the L2,1-Norm Metric for Robust Graph Matching.

Establishing correspondence between two given geometrical graph structures is an important problem in computer vision and pattern recognition. In this paper, we propose a robust graph matching (RGM) model to improve the effectiveness and robustness on the matching graphs with deformations, rotations, outliers, and noise. First, we embed the joint geometric transformation into the graph matching model, which performs unary matching over graph nodes and local structure matching over graph edges simultaneously. Then, the L2,1 -norm is used as the similarity metric in the presented RGM to enhance the robustness. Finally, we derive an objective function which can be solved by an effective optimization algorithm, and theoretically prove the convergence of the proposed algorithm. Extensive experiments on various graph matching tasks, such as outliers, rotations, and deformations show that the proposed RGM model achieves competitive performance compared to the existing methods.

Enhancing the competitive swarm optimizer with covariance matrix adaptation for large scale optimization

Competitive swarm optimizer (CSO) has been shown to be an effective optimization algorithm for large scale optimization. However, the learning strategy of a loser particle used in CSO is axis-parallel. Then, it may not be able to solve the high ill-conditioned test functions due to the lack of considering the correlation of different component. This paper presents an enhanced competitive swarm optimizer with covariance matrix adaptation to alleviate this problem. Since covariance matrix is independent of the coordinate system, covariance matrix adaptation evolution strategy (CMA-ES) is embedded into CSO. On the one hand, better particles generated by CMA-ES can provide an effective way to capture the efficient search direction. On the other hand, some high-quality particles are employed to estimate the covariance matrix of Gaussian model. Then, the evolution direction information is integrated into the learned Gaussian model to improve search efficiency of the proposed algorithm. Experimental and statistical analyses are performed on CEC2014 benchmark functions, engineering design problems and time series prediction problems. Results show that the proposed algorithm has a superior performance in comparison with other state-of-the-art optimization algorithms and some variants of CSO.

Application of chaotic Fish School Search optimization algorithm with exponential step decay in neural network loss function optimization

The Fish School Search (FSS) algorithm is a heuristic technique for finding globally optimal solutions. This algorithm is characterized by its simplicity in implementation, and high performance. Since the first mention of FSS, this effective optimization algorithm has been of a great interest among researches and practitioners around the globe. Modifications of FSS exist, applied to solve practical problems, including image reconstruction in electrical impedance tomography, finding optimal solutions in assembly line balancing problems, neural network structure optimization. In this paper, we consider a modification of the FSS algorithm, which uses chaos theory to generate uniformly distributed pseudorandom numbers, and incorporates exponential step decay. The described modified optimization algorithm is known as ETFSS, and is characterized by faster convergence speed and better performance. In order to further investigate the performance of the novel optimization algorithm, we apply ETFSS to neural network loss function optimization. In addition, we compare the described approach with other machine learning techniques, such as the support vector machine (SVM) algorithm, k-nearest neighbors (KNN) algorithm and back propagation-based neural network, trained using the adaptive moment estimation (Adam) optimizer. We visualize classification results using T-distributed stochastic neighbor embedding (TSNE) method, and uniform manifold approximation and projection (UMAP) method, in order to provide more details considering classification performance and dataset shape. The obtained results confirm, that ETFSS can produce slightly more accurate classifications when compared to backpropagation.

Stochastic Bigger Subspace Algorithms for Nonconvex Stochastic Optimization

It is well known that the stochastic optimization problem can be regarded as one of the most hard problems since, in most of the cases, the values of f and its gradient are often not easily to be solved, or the F(∙, ξ) is normally not given clearly and (or) the distribution function P is equivocal. Then an effective optimization algorithm is successfully designed and used to solve this problem that is an interesting work. This paper designs stochastic bigger subspace algorithms for solving nonconvex stochastic optimization problems. A general framework for such algorithm is presented for convergence analysis, where the so-called the sufficient descent property, the trust region feature, and the global convergence of the stationary points are proved under the suitable conditions. In the worst-case, we will turn out that the complexity is competitive under a given accuracy parameter. We will proved that the SFO-calls complexity of the presented algorithm with diminishing steplength is O(ϵ-1/1-β) and the SFO-calls complexity of the given algorithm with random constant steplength is O(ϵ-2) respectively, where β ∈ (0.5,1) and ϵ is accuracy and the needed conditions are weaker than the quasi-Newton methods and the normal conjugate gradient algorithms. The detail algorithm framework with variance reduction is also proposed for experiments and the nonconvex binary classification problem is done to demonstrate the performance of the given algorithm.

A comprehensively improved particle swarm optimization algotithm to guarantee particle activity

The particle swarm optimization algorithm has the disadvantages, for instance, the convergence viscosity of the algorithm is reduced at the post evolution phase, the optimization search efficiency is reduced, the algorithm is easy to be inserted with local extremum during the calculation of complex problem of high-dimensional multiple extremum, and the convergence thereof is low. As to the disadvantage of the PSO, we proposed a particle swarm optimization of comprehensive improvement strategy, which is a simple particle swarm optimization with dynamic adaptive hybridization of extremum disturbance and cross (ecds-PSO algorithm). This new comprehensive improved particle swarm algorithm discards the particle velocity and reduces the PSO from the second order to the first order difference equation. The evolutionary process is only controlled by the variables of the particles position. The hybridization operation of increasing the extremum disturbance and introducing genetic algorithm can accelerate the particles to overstep the local extremum. The mathematical derivation and a plurality of comparative experiment provide us the following information: the improved particle swarm optimization is a simple and effective optimization algorithm which can improve the algorithm accuracy, convergence viscosity and ability of avoiding the local extremum, and effectively reduce the calculation complexity.