Search

The fault management process is facilitated by equipping power distribution systems with automated devices, especially remote-controlled switches (RCSs). Although RCS plays a key role in improving the system's reliability, it imposes significant investment, installation, and maintenance costs. Hence, RCSs should be optimally located in distribution feeders for the highest profit. In previous works, reliability-oriented mathematical optimization models have been formulated to reach this goal. However, the number of test solutions exponentially grows with the problem size to find the globally optimal solution. This paper uses machine learning to propose a scalable and easy-to-implement model for optimal switch placement in real power distribution systems. At first, the features of candidate points for installing RCSs are introduced. Then, a learning model is applied to deeply explore the relationship between these features and optimal locations for installing RCSs. After training, the learning-based surrogate model directly determines the optimal RCS placement strategy in real power distribution systems by leveraging knowledge gained from past experiences. Simulation results demonstrate that the proposed surrogate model is approximately 29 times faster than the integer programming-based mathematical model, without a significant loss of accuracy, when implemented on a modified 11 kV network connected to Bus 4 of the Roy Billiton test system. This paper also employs explainable artificial intelligence (XAI) tools to select the most important features, where the Hamming loss is decreased by approximately 5%. [ABSTRACT FROM AUTHOR]

Determining the mining sequence is one of the main objectives in mine planning. However, depending on the size of the analyzed instances, such activity might become an extremely difficult task, despite current computational capacity. In addition, determining a feasible and operational mining sequence is also challenging, so practitioners usually employ strategies to segment and simplify the main problem, such as splitting it into distinct time horizons and aggregating blocks into clusters. This paper aims to perform a critical review about the different clustering methodologies and algorithms used for mining-block aggregation, with the purpose of understanding the proposed solutions and identifying the gaps found in the current literature. The reviewed aggregation strategies encompass the modelling of tabular deposits as sets of layers and grouping of blocks in benches, bench-phases, and mining cuts. Among the optimization techniques evaluated, one may find heuristics, artificial intelligence, and exact approaches, relying on deterministic or uncertainty-based methodologies, considering approximately six decades of studies and covering fifty-eight works published in journals and proceedings from 1967 to 2022. In addition to what is seen within the literature analyzed, we also propose future research directions, such as approaches and algorithms not yet implemented to solve the block aggregation problem, thus presenting opportunities for further research in this field. [ABSTRACT FROM AUTHOR]

Modeling interdependencies in a production process is a vital aspect of process engineering. Being built on the fundamental understanding of the process and capturing underlying physical and chemical mechanisms, this task is traditionally carried out through first-principle modeling. However, due to various assumptions and parameter approximation, such approach sometimes struggles to accurately model process dynamics. This is partially due to the requirements for process stability in mission-critical scenarios, where it is not viable to conduct extensive experiments that would enable the development of comprehensive models, and hence, the process control is still partially dependent on the experience of the process operator. In this paper, a data-driven approach based on production data is introduced to address the challenge of discovering, understanding, and modeling interdependencies in a production process by constructing a constrained multi-objective optimization problem. The applicability of the proposed approach is demonstrated through a case study in the ironmaking industry, whereby a set of Machine Learning and causality-based methods is provided while highlighting the significance of transparency and use of domain knowledge in the development of data-driven models. Based on the real data from a Sintering plant, interdependencies are quantified between five key process parameters, which, when supplemented with other control parameters, result in a higher overall process output leading to reductions in operational costs and environmental impact. Such approach shows potential for applications in a broader context across other fields of science and engineering, where it can be used to improve the discovery of interdependencies in complex real-world industrial settings. • Modeling interdependencies in production is a vital aspect of process understanding. • First-principle models can struggle to accurately model complex process dynamics. • Constrained multi-objective optimization is used to address interdependency modeling. • Interdependencies are identified and quantified between five key parameters. • Higher output, reduced operational costs, and environmental impact are achieved. [ABSTRACT FROM AUTHOR]

Determining the mining sequence is one of the main objectives in mine planning. However, depending on the size of the analyzed instances, such activity might become an extremely difficult task, despite current computational capacity. In addition, determining a feasible and operational mining sequence is also challenging, so practitioners usually employ strategies to segment and simplify the main problem, such as splitting it into distinct time horizons and aggregating blocks into clusters. This paper aims to perform a critical review about the different clustering methodologies and algorithms used for mining-block aggregation, with the purpose of understanding the proposed solutions and identifying the gaps found in the current literature. The reviewed aggregation strategies encompass the modelling of tabular deposits as sets of layers and grouping of blocks in benches, bench-phases, and mining cuts. Among the optimization techniques evaluated, one may find heuristics, artificial intelligence, and exact approaches, relying on deterministic or uncertainty-based methodologies, considering approximately six decades of studies and covering fifty-eight works published in journals and proceedings from 1967 to 2022. In addition to what is seen within the literature analyzed, we also propose future research directions, such as approaches and algorithms not yet implemented to solve the block aggregation problem, thus presenting opportunities for further research in this field. [ABSTRACT FROM AUTHOR]

Modeling interdependencies in a production process is a vital aspect of process engineering. Being built on the fundamental understanding of the process and capturing underlying physical and chemical mechanisms, this task is traditionally carried out through first-principle modeling. However, due to various assumptions and parameter approximation, such approach sometimes struggles to accurately model process dynamics. This is partially due to the requirements for process stability in mission-critical scenarios, where it is not viable to conduct extensive experiments that would enable the development of comprehensive models, and hence, the process control is still partially dependent on the experience of the process operator. In this paper, a data-driven approach based on production data is introduced to address the challenge of discovering, understanding, and modeling interdependencies in a production process by constructing a constrained multi-objective optimization problem. The applicability of the proposed approach is demonstrated through a case study in the ironmaking industry, whereby a set of Machine Learning and causality-based methods is provided while highlighting the significance of transparency and use of domain knowledge in the development of data-driven models. Based on the real data from a Sintering plant, interdependencies are quantified between five key process parameters, which, when supplemented with other control parameters, result in a higher overall process output leading to reductions in operational costs and environmental impact. Such approach shows potential for applications in a broader context across other fields of science and engineering, where it can be used to improve the discovery of interdependencies in complex real-world industrial settings. • Modeling interdependencies in production is a vital aspect of process understanding. • First-principle models can struggle to accurately model complex process dynamics. • Constrained multi-objective optimization is used to address interdependency modeling. • Interdependencies are identified and quantified between five key parameters. • Higher output, reduced operational costs, and environmental impact are achieved. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid algorithm which is mathematically modelled by amalgamating the superior features of recently developed Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), and Crow Search Algorithm (CSA). Researchers have already implemented the aforementioned three algorithms and obtained superior quality results for solving diverse optimization problems. The novel hybrid Variegated GWO Algorithm (VGWO) developed in this proposed research work is initially realized and validated for solving IEEE CEC-C06 2019 benchmark functions. Thereafter, the proposed VGWO is utilized as an optimization tool to solve three emerging and complex power system optimization problems which includes energy management of microgrid systems operated in both islanded and grid-connected mode, dynamic economic emission dispatch and reactive power planning (RPP) problem. A comparative analysis of the proposed VGWO approach with other established metaheuristics is undertaken for each optimization problem. Numerical results show that the novel hybrid VGWO algorithm outperformed an ample number of optimization techniques in providing better quality solutions. The proposed hybrid algorithm yielded a 36.93% reduction in active power loss and 36.80% reduction in operating cost with respect to base case condition for RPP problem. Likewise while solving microgrid energy management problems 9–30% savings was realized in the generation cost compared to the ones mentioned in literature. The capability of handling many complex constraints within a minimum amount of computational time to provide consistently best solutions prioritize the proposed hybrid algorithm among its kinds. Statistical analysis validates the authenticity and viability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The number of scientific publications in different knowledge fields has considerably grown in recent times. This makes difficult for researchers to synthesize all the scientific-technical advances, so automatic summarization methods of scientific papers would be helpful. These methods generate a summary from a reference paper with its most relevant contributions. More specifically, citation-based summarization considers the citation contexts to the reference paper in subsequent publications. For the first time, this problem has been formulated as a multi-objective optimization problem, optimizing the content coverage and the redundancy reduction in a simultaneous way. A Decomposition-based Multi-Objective optimization algorithm for Citation-based Summarization (DMOCS) has been designed, developed, and applied for solving this problem. The results obtained by the proposed approach have improved the existing ones in the scientific literature between 17.47% and 133.50%, increasing the ROUGE percentage improvements when the N -gram is larger. Besides, an exhaustive analysis of the different parts of a scientific paper has been performed, showing that the citations from the citing papers with their corresponding spans in the reference paper impact in the quality of a citation-based summary. • First time the citation-based summarization is solved by multi-objective optimization. • A decomposition-based multi-objective optimization algorithm (DMOCS) is proposed. • An extensive analysis of the best text parts for citation-based summarization is done. • Interesting conclusions about the citing spans and the citation contexts are obtained. • DMOCS improves the results of the different approaches in the scientific literature. [ABSTRACT FROM AUTHOR]

Restricted by factors such as small robot size and harsh operating environment, the inability to obtain the interaction force between the robot arm and the environment through force sensors has become problematic in promoting the application of teleoperation systems in minimally invasive surgery, nuclear waste cleanup, and other fields. To accurately obtain the interaction force without the force sensors, a force observer based on an adaptive sparse general regression neural network (ASGRNN) is proposed in this paper. The proposed force observer uses a machine learning-based approach to obtain estimated force, thus eliminating the need for the dynamic parameters of the robot arm. Also, an innovative feature selection method incorporating the wrapper method and sparse regularization is proposed to select the input features of the force observer. Secondly, two new criteria are defined to eliminate the useless support vectors in the model. In addition, an improved antlion optimization algorithm (IALO) is proposed to optimize the bandwidth parameters of the model. To verify the performance of the proposed force observer, a 6-degree-of-freedom teleoperation robot experimental platform is built and compared with three existing force estimation models. The results show that the proposed force observer outperforms the existing model in terms of estimation accuracy, and the mean square error (MSE) is at least 35.79% lower than the existing model. In conclusion, this paper provides a feasible and effective force observer for a teleoperation system where force sensors are not applicable and dynamic parameters are non-knowable. • A new interaction force observer based on machine learning is proposed. • The proposed force observer does not require a force sensor and does not rely on any robot dynamics parameters. • A new feature selection method is proposed for selecting the input features of the force observer. • An improved ant lion optimization algorithm is proposed to optimize the force observer parameters. • The proposed force observer can accurately predict the interaction forces between the robot arm and the environment. [ABSTRACT FROM AUTHOR]

Due to the remarkable ability of Deep learning (DL) to abstract hidden information, it has been proven to be a powerful tool in many tasks related to the design of metamaterials. DL-aided design techniques can be generally categorized into two types, including forward designs, which are training surrogate models to accelerate the simulation process, and inverse design, which uses inverse modeling techniques to generate the design that satisfies the corresponding requirement. Although generative models have a unique capability to generate multiple designs instantly with random information, they often underperform in accuracy compared to designs based on optimization techniques. In this paper, a hybrid design framework combining the advantages of both DL forward design and the inverse design based on the mixture density network (MDN) is proposed. Then the proposed framework is implemented for the inverse design of S-shaped perforated auxetic metamaterial. The hybrid design framework inherited the one-to-many mapping capability of MDN and has great capability of generating designs with designated mechanical properties at less than 10% relative errors, in most design scenarios (over 95% in the test set), at one to two orders of magnitude less computational cost compared to optimization-based forward design. • A hybrid method with MDN and DNN generates designs at low cost but good accuracy. • A case study of auxetic metamaterial shows the effectiveness of the hybrid method. • The comparison with current DL design models shows the advantage of our model. [ABSTRACT FROM AUTHOR]

In this paper, an improved multithreshold image segmentation method based on the whale optimization algorithm (RAV-WOA) is proposed, with the between-class variance (Otsu method) as the objective function. The proposed RAV-WOA is able to select satisfactory optimal thresholds while ensuring high efficiency and quality when performing image segmentation on grayscale and color images In the current work, a reverse learning strategy was introduced into the initialization of RAV-WOA populations to improve the quality of the initial population of whales. An adaptive weighting strategy was introduced into the RAV-WOA algorithm, which is influenced by the fitness value and the number of iterations, to balance the global search capability of the algorithm with the local exploitation capability. The proposed RAV-WOA is then applied to the Otsu method to solve the multilevel thresholding image segmentation problem. To better verify the effectiveness of the proposed method, this paper compares the RAV-WOA with some classical heuristic algorithms and performs image segmentation experiments on a set of benchmark images with low and high thresholds. The experimental results show that the convergence speed and convergence accuracy of RAV-WOA are significantly better than other algorithms, and the segmentation results of RAV-WOA in multithreshold image segmentation have better quality and stability than other algorithms. [ABSTRACT FROM AUTHOR]

This paper develops a robust strategy based on integrating three mutation phases and adapted control parameters into the Adaptive Guided Differential Evolution algorithm called (mAGDE) to improve diversity and exploration of the original AGDE. The mAGDE performance is evaluated using IEEE CEC'2020 test suite. Furthermore, the mAGDE is employed to identify the solid oxide fuel cell (SOFC) model optimal parameters. Two modes of SOFC operation are investigated, steady and transient states. The results obtained from the proposed mAGDE are compared with a number of recent, well-established and reputed meta-heuristics, including Particle swarm optimization, Teaching learning-based optimization, Whale optimization algorithm, Harris hawks optimization, Marine predators algorithm, Archimedes optimization algorithm, Differential evolution, and the original AGDE. Additionally, the statistical parameters that measure the performance of the proposed optimizer and the other competitors are calculated. The main finding demonstrated the preference and robustness of the suggested mAGDE in constructing the SOFC circuit that closely converges to the actual one. During the steady-state operation, the best fitness value obtained via the suggested mAGDE for operation at 1273 K is 2.2995E−06, while in the transient-state operation, the best SMSE is 1.04. The average cost function is decreased by 43.33% compared to the one obtained by the original AGDE. From the aforementioned assessments, it can be concluded that the proposed mAGDE is outstanding and promising. • This paper introduces a modified mAGDE based on the integration of a modified three-phases AGDE with the DE_SPA algorithm. • A modified three-phases AGDE has been introduced to maintain diversity and convergence of the evolutionary process. • The CEC'2020 test suite problems are used to demonstrate the performance mAGDE against other competitors. • Applying for first time mAGDE to identify the optimal parameters of SOFC model. • The robustness of the proposed strategy-based mAGDE is proved. [ABSTRACT FROM AUTHOR]

In this paper, the applicability of machine learning models and techniques to the Markov–Dubins path planning problem have been explored. Machine learning techniques are already applied to several fields, which range from computer vision, to physics simulation, to item recommendation, to user profiling. This pervasiveness has led to marked improvements in the implementation and support for applying machine learning models, in particular for specialised use cases such as low-power devices, embedded hardware, and real-time applications. On the other hand, the Markov–Dubins path planning problem, which is central in robotic nonholonomic trajectory design, is already covered by established numerical and optimisation techniques. However, the benefits of applying machine learning approaches to this problem remain to be investigated. In particular, there is the need to research potential speed-ups or application domains that would be better solved by a machine learning approach compared to the traditional algorithmic approaches. In this study, we train a state-of-the-art machine learning model in a supervised setting on Markov–Dubins and use it in two different ways: to directly predict the solution, and to filter candidate solutions. Also, a comparison of the quality of these predictions with a state-of-the-art Markov–Dubins solver is made. The results obtained indicate that machine learning approaches are comparable to state-of-the-art solutions: our bare model, directly predicting the solution, appears to be 8.3 times faster than the current standard, sacrificing the accuracy, which amounts to a value close to 92%; the hybrid model that filters the solutions prior to finding the best candidate runs in times that are comparable to the classical solver (58 ms) and has over 98% accuracy. A further comparison with alternative solvers and techniques, such as Optimal Control, NonLinear Programming and Mixed Integer NonLinear Programming has been made, confirming the benefits of the machine learning approach over these, for which the computational times are in the range of seconds. This opens new avenues for interdisciplinary applications of machine learning to more general planning problems (e.g., the same problem in 3D), where the number of possible manoeuvres is large and the computation of each of them requires a considerable computational effort, which makes the brute force trial-and-error infeasible. [Display omitted] [ABSTRACT FROM AUTHOR]

Support vector data description (SVDD) has received a lot of attention due to its outstanding performance to perform one-class classification or novelty detection tasks. However, the same weight is directly imposed on all slack variables in the process of modeling, which may result in degraded learning performance when training data are contaminated by some outliers or mislabeled observations. In this paper, an extended SVDD model is therefore proposed by reformulating the original optimization problem of SVDD with a mixed exponential loss function. Since this loss function can emphasize the importance of the samples that tend to be the target class, and weaken the influence of those tending to be outliers, it can be viewed as a weighted SVDD. However, the weights in the new model are automatically calculated rather than being calculated in advance using some specific manners. To solve the optimization problem of the proposed model effectively, the half-quadratic optimization technique has been adopted to perform the optimization, generating a dynamic optimization algorithm. Meanwhile, the convergence and computational complexity of this dynamic optimization algorithm are analyzed from a theoretical respective. Experimental results on a synthetic data set and some publicly available real world data sets are reported to demonstrate the performance superiority of the new method in comparison with SVDD and other competitive SVDD variants. [ABSTRACT FROM AUTHOR]

To solve the multi-modal multi-objective optimization problems in which the same Pareto Front (PF) may correspond to multiple different Pareto Optimal Sets (PSs), an improved multi-objective particle swarm optimizer with short-term memory and K-means clustering (MOPSO-SMK) is proposed in this paper. According to the framework of multi-objective particle swarm optimization (MOPSO) algorithm, the designs of updating mechanism and population maintenance mechanism are the keys to obtain the optimal solutions. As a significant influence factor of the updating mechanism, the inertia weight has been discussed in this paper. In the improved algorithm, a new update model for the value of pbest based on short-term memory is proposed. The update strategies based on K-means clustering are adopted to obtain the better gbest and elite archive. 16 multi-modal multi-objective optimization functions are used to verify the feasibility and effectiveness of the proposed MOPSO-SMK. As the results show, MOPSO-SMK has more advantages in four indexes (1/PSP, 1/HV, IGDX, and IGDF) compared with other three multi-objective optimization algorithms. [ABSTRACT FROM AUTHOR]

Brain tumor detection is a very important and challenging task. An efficient detection algorithm is of great importance to the practice of brain tumor medicine. In this paper, we propose a novel parallel optimization algorithm based on a shared-memory environment to solve the SVM classifier for brain tumor detection. Firstly, the HOG algorithm is used to extract brain tumor MR image features and compare them with the wavelet transform method. Secondly, SVM with ℓ 1 -norm loss function is utilized as a classifier. Due to its sparsity, the detection speed is significantly faster. Finally, SMP-SGD, SMP-Momentum, SMP-Adagrad, and SMP-Adam algorithms are proposed and applied to the classifier solution. The experimental results show that the HOG algorithm extracts brain tumor MRI features more effectively than the discrete wavelet transform method. The proposed SMP-SGD algorithm and its variants achieved state-of-the-art accuracy and efficiency for brain tumor detection. [ABSTRACT FROM AUTHOR]

A Plate–fin heat exchanger (PFHE) is a compact and efficient thermal device, whose performance strongly depends on its structural design. However, the design optimization of a PFHE is a mixed-integer optimization problem with a strong nonlinear characteristic, which presents significant challenges for existing optimization algorithms. Meta-heuristic algorithms (MAs) are competitive for solving complex non-linear optimization problems. In this paper, an improved dynamic-opposite learning Jaya (DOLJaya) method, the goal is to make the algorithm adaptable to each problem. The results of eighteen unimodal and multi-modal benchmarks and nine hybrid benchmarks demonstrate that the proposed DOLJaya has competitive robustness, efficiency and effectiveness for solving complex nonlinear problems compared to its popular counterparts. At the same time, we selected the optimization of the plate–fin heat exchanger as the industrial test benchmark for optimization, and the results of DOLJaya algorithm have been improved by a maximum average of 108.29% and 7.60% compared with the original Jaya, which are also satisfactory. [ABSTRACT FROM AUTHOR]

Train speed profile is one of the most fundamental optimization issues in rail transportation. The speed profile has several indices, such as travel time and energy consumption. These indices mostly have opposite natures, which leads to a non-linear, complex multi-objective optimization problem. This paper introduces a new heuristic algorithm called Conscious Search (CS) to determine the optimal speed profile. For this purpose, after modeling the train dynamics, we initially analyze the validated heuristic optimization algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Bees Algorithm (BA), and Teaching–learning Based Optimization (TLBO) statistically using sensitivity analysis. Then, using new criteria called the Impact Factor, we show that a trade-off prevails in the global and local search for algorithms. In general, the stronger the algorithm in one domain, the weaker in the other. Subsequently, no algorithm is superior to another. In this regard, CS is proposed as a hybrid algorithm that performs the optimization in two separate global and local phases to address this problem and achieve the ideal optimization. CS steps are designed to dominate other algorithms in both local and global phases. In the evaluation, we obtained the train speed profile using the proposed method and the proven latest versions of the mentioned algorithms. Based on the simulation results, CS outperforms the considered algorithms by a palpable margin, and its solutions dominate others. These results indicate on effectiveness and excellence of the proposed method. • Train speed profile is a multi-objective problem with contradicting costs. • Optimization algorithms are statistically analyzed using sensitivity analysis. • Conscious Search is proposed as a new heuristic algorithm. • New updates and parameters help CS to outperform other algorithms. [ABSTRACT FROM AUTHOR]

Solving multi-objective optimization problems plays an important role in several applications. Recently, the Marine Predator Algorithm (MPA) was introduced for solving single objective optimization problems inspired by the behaviour of marine predator in their search for a prey. This paper proposes a modified MPA based framework called Guided Multi-objective Marine Predator Algorithm (GMOMPA) to solve multi-objective optimization problems. The proposed GMOMPA incorporates an external archive to help store the optimal Pareto set solution and guide the particles during the exploitation of the search space. For obtaining non-dominated solutions, the Pareto dominance concept is utilized while the epsilon dominance is considered to update the archive's sorted solutions. In this context, the epsilon dominance concept extends the diversity and exploration of the solutions. Further, a fast non-dominated solution and crowding distance are introduced to update the particle's position, while maintaining the diversity and ensuring a fast convergence towards the Pareto optimal. The proposed GMOMPA is evaluated on several different benchmark test functions including multi-objective ZDT, DTLZ, UF, and WFG test functions as well as the recent multi-objective multimodal CEC 2020 test functions. Moreover, the performance of the proposed GMOMPA is compared with well-known multi-objective optimization algorithms. The obtained results show that the proposed GMOMPA is a good tool for multi-objective optimization and has significant advantages over several state-of-the-art algorithms in almost all of the test functions. [ABSTRACT FROM AUTHOR]

Multi-objective optimization of detailed building models may lead to high computational time expenses. The work presented in this paper presents an alternative of classical approaches based on the usage of reduced sequences in a sequential and an adaptive approach. The sequential approach runs directly the reduced sequence obtained from a typical day selection algorithm which employs a k- medoids clustering algorithm in a multi-objective optimization study. It was applied on a solar combisystem connected to a building model and showed its capability of reproducing the Pareto front 25 times faster with relative errors not exceeding 5% when considering several parametric configurations of the model and focusing on a limited number of selection criteria during the day selection process. On the other hand, the adaptive approach for optimization named OptiTypSS achieves day selection during the optimization process by combining an iterative day selection algorithm (TypSS) with a genetic algorithm of optimization (NSGA-II). The method succeeded in obtaining results with errors inferior to 3% being therefore as efficient as a classical metamodel adaptive approach yet with relatively higher computational time. Further improvements including parallel simulation during the day selection process can be done to enhance the performance. • Optimization of a solar combi system model using short simulation sequences in a sequential and an adaptive approach. • In the sequential approach, the optimization algorithm converged 25 times faster with relative errors of a maximum of 5%. • In the adaptive approach, the Pareto front superposed the reference one recording relative errors inferior to 2.5%. • Results obtained by the adaptive approach were as efficient as using metamodels with relatively higher computational time. [ABSTRACT FROM AUTHOR]

The preparation process of polyurethane (PU)-modified bitumen involves numerous design parameters and performance response indexes. Due to the variety of polyurethane modifiers, the preparation process of the polyurethane-modified bitumen is not universally applicable. However, the traditional methods such as the response surface method and orthogonal design method have some problems such as low accuracy and a large number of samples required in the preparation process design. Therefore, according to different application environments, the problem of determining the process parameters of the polyurethane-modified bitumen accurately and efficiently needs to be solved urgently. Using Kriging-Particle Swarm Optimization (PSO) algorithm, an efficient process design method for the preparation of polyurethane modified asphalt is proposed in this paper. Combined with the sensitivity analysis method, the relatively sensitive response indexes are screened out to reduce the number of samples and improve the design accuracy. Among them, the dispersion coefficient was evaluated by fluorescence microscopy test using the Christiansen coefficient method to evaluate the uniformity of the dispersed phase of the polyurethane modifier. According to the target performance, the main process parameters of PU modified asphalt were obtained by the Kriging-Particle Swarm Optimization algorithm: shear time 86 min, shear speed 2450 rpm, shear temperature 148 °C, and polyurethane content 18.6%. The polyurethane-modified bitumen prepared by this optimal process met the expected performance indicators. This study achieved the expected results with a small number of samples, indicating that this method can achieve the purpose of designing the ideal process parameters of polyurethane-modified asphalt efficiently. [ABSTRACT FROM AUTHOR]

The task of time series forecasting is to predict the future trend of data based on the collected historical data, providing theoretical and data support for human judgment and decision making. Randomization-based echo state networks (ESNs) are widely used in the research and application field of time series analysis for their simple structure and fast training speed. The core of the ESN is its dynamic reservoir, which the original reservoir are randomly generated and controlled only by parameter sparsity, often performing poorly on complex tasks and affecting the performance of networks. Manual design of the topology of reservoir is difficult, time-consuming and inconvenient to operate, which is not conducive to the development of ESNs. The construction of a suitable reservoir topology for practical application problems to enrich reservoir dynamics is a hot research point for researchers. In this paper, an automatic optimization method is introduced into the topology optimization of ESN (TP-ESN), and the particle swarm optimization algorithm is used to optimize the topological construction of the ESN. The connection structure between the reservoir neurons is first encoded and then iteratively optimized. The optimized structure is decoded and then the reservoir is initialized for ESN training. Prediction results on Mackey–Glass benchmark time series and two electroencephalogram (EEG) datasets demonstrate that TP-ESN method can have better adaptability, stronger prediction ability and stability than several other manually designed ESN reservoir topologies in the case of relatively complex tasks. [ABSTRACT FROM AUTHOR]

Installing Energy Storage Systems (ESSs) to improve electrical infrastructures of Direct-current (DC) railway systems implies considerable investments that must be assessed carefully. Therefore, it is often necessary to combine detailed railway simulations and decision support mechanisms. Unfortunately, most examples in the literature deal with this topic applying only a single-stage optimization approach: the whole installation is undertaken in a single step, assuming the total budget is available. This paper presents a comprehensive methodology to assess the gradual deployment of the installations when the budget is split into different time periods. This approach is a common situation in real projects and has not been studied yet in the literature. Most often, this type of multi-stage problem is tackled by optimizing each stage independently. On the contrary, this paper proposes to take decisions considering the global impact of each stage optimization, rendering a more efficient solution. This paper proposes a multi-stage formulation of two nature-inspired optimization algorithms (Genetic and Fireworks) to address the installation of ESSs in a realistic railway line. Results demonstrate the excellent behavior of the proposed multi-stage optimization. • Multi-stage optimization is proposed for two nature-inspired optimization algorithms. • It is used to improve the electrical infrastructure of DC-electrified railway systems. • Particularly, it optimizes the installation of ESSs (location, power and capacity). • The optimization process makes use of a very realistic railway simulator. • Energy savings and considerable installation's profitability are obtained. [ABSTRACT FROM AUTHOR]

Particle filter (PF) is introduced to tackle the limitations of the Kalman filter which adopts Gaussian in the state and noise of the system. PFs have the problem of sample impoverishment and one approach to solve this problem is to optimize the proposal distribution shown by particles. This paper introduces a novel evolutionary PF based on Opposition based High Dimensional optimization Algorithm (OHDA) to reposition the particles of PF in high probable regions for estimation. OHDA will preserve the diversity of particles while emphasizing the more informative ones by information sharing and angular movement operators. Opposite particles are introduced in this paper to speed up the convergence of PF. Virtual forward movement by angular movement of OHDA is employed to better guide the search process. The optimized PF can improve the performance of the estimation algorithms in problems such as localization and SLAM. In robot localization problem, particles show the location of the robot in a known environment. For SLAM (Simultaneous Localization And Mapping), particles contain the location of the robot as well as estimated map of the environment. The application of the resulting evolutionary particle filter is tested in both localization and SLAM. Comparing the results of the proposed evolutionary particle filter with other algorithms confirms the efficiency of applying OHDA to PF in terms of improving estimation accuracy in the well-known Victoria park dataset and some other generated test environments. Comparing optimization algorithms on FASTSLAM and UFASTSLAM are PSO, FA, MVO, and MGWO. [ABSTRACT FROM AUTHOR]

In advanced database design such as Big Data Warehouses, optimizing large-scale decision support queries passes through the selection of redundant structures such as materialized views and indexes. Materialized View Selection (M V S) is one of the most studied problems in the context of the physical design of advanced databases. It is known as an NP-Hard problem. Several algorithms have been proposed to find, within a reasonable computation time, the appropriate materialized views that reduce as much as possible the query processing cost and the view maintenance cost w.r.t. a storage constraint. By analyzing the state-of-art studies, we figure out that almost all are workload-driven approaches. Their efficiency strongly depends on the number of queries of the considered workload. These approaches manage a small set of queries. Having efficient algorithms for selecting materialized views based on a very large set of queries has become a crucial issue for advanced applications. In this paper, we proposed a Z i g Z a g + approach that uses the multiple view processing plan (M V P P) as a basic data structure that unifies all query plans. Due to the no-unicity of this M V P P , our approach aims at exploring all possible M V V P s, and for each exploration, it identifies materialized views. After this selection, update operations of the current M V P P are performed. Zigzagging from one M V P P to another is guided by the quality of selected views. Intensive experiments have been conducted to evaluate the effectiveness and efficiency of our proposal by considering large workloads and comparing it with state-of-art approaches. [ABSTRACT FROM AUTHOR]

Feature selection, which aims to screen out redundant and irrelevant features from datasets, is integral to machine learning and data mining. Grey Wolf Optimization (GWO) is a recent meta-heuristic algorithm based on swarm intelligence and has wide applicability to various optimization problems due to its fast convergence and few parameters. However, since the wolf pack is always dominated by the three leading wolves (i.e. , α , β and δ), the GWO algorithm suffers from weak exploration throughout the whole optimization process and easily stagnates into local optima. In this paper, an Adaptively Balanced Grey Wolf Optimization (ABGWO) algorithm is proposed to seek out the optimal feature subset for high-dimensional classification. Specifically, to improve the exploration ability of GWO, a random wolf is introduced to cooperate with α , β and δ. A novel level-based strategy is further adopted to select the random wolf. Besides, to dynamically modulate the exploration and exploitation ability in different optimization stages, an adaptive coefficient is introduced to regulate the leadership of α , β , δ and the randomly-selected wolf. Finally, the improvement of exploration and exploitation is validated on 12 high-dimensional datasets provided by Arizona State University and University of California Irvine, and the superiority of ABGWO is further verified by comparing it with seven state-of-the-art feature selection approaches on the aspect of classification accuracy, size of feature subset and computational time. • Introducing a random individual to prevent sharp decline in population diversity. • Semi-random selection prevents the rapid decrease of the convergence speed. • A coefficient factor linearly increases to adaptively adjust the exploration and exploitation. • The improved GWO algorithm for high-dimensional feature selection significantly boosts the classification performance. [ABSTRACT FROM AUTHOR]

In engineering applications, many real-world optimization problems are nonlinear with multiple local optimums. Traditional algorithms that require gradients are not suitable for these problems. Meta-heuristic algorithms are popularly employed to deal with these problems because they can promisingly jump out of local optima and do not need any gradient information. The arithmetic optimization algorithm (AOA), a recently developed meta-heuristic algorithm, uses arithmetic operators (multiplication, division, subtraction, and addition) to solve optimization problems including nonlinear ones. However, the exploration and exploitation of AOA are not effective to handle some complex optimization problems. In this paper, an opposition learning and spiral modelling based AOA, namely OSAOA, is proposed for enhancing the optimization performance. It improves AOA from two perspectives. In the first perspective, the opposition-based learning (OBL) is committed to taking both candidate solutions and their opposite solutions into consideration for improving the global search with a high probability of jumping out of local minima. Then, the spiral modelling is introduced as the second perspective, which is particularly useful in getting the solutions gathering faster and accelerating the convergence speed in the later stage. In addition, OSAOA is compared with other existing advanced meta-heuristic algorithms based on 23 benchmark functions and four engineering problems: the three-bar truss design, the cantilever beam design, the pressure vessel design, and the tubular column design. From our simulations and engineering applications, the proposed OSAOA can provide better optimization results in dealing with these real-world optimization problems. [ABSTRACT FROM AUTHOR]

Harris Hawks Optimization (HHO) is a population-based meta-heuristic optimization algorithm that has been used for the solution of test functions and real-world problems by many researchers. However, HHO has a premature convergence problem. The main motive of the novel approach in this paper is that the performance of an MHA could be improved by simplification and by modifying the way random parameters are determined. The proposed algorithm aims to solve both continuous and discrete optimization problems. HHO is improved in three stages. First, the method to determine the random parameters is modified. Second, the strategy of HHO to produce a new solution is updated. Third, the six-step decision mechanism of HHO is shortened to four. The proposed algorithm is compared to five recently published competitor algorithms by applying to the CEC2019 test functions and a three-dimensional bin packing problem (3D-BPP) dataset with 320 samples. All the algorithms are run on the same computer and the results of 30 independent studies are saved. Minimum, average, and standard deviation values and solution times of CEC2019 functions are used as comparison parameters. For the 3D-BPP, the number of bins and the solution time are used as comparison parameters for in the Wilcoxon test. The proposed algorithm performs better than the selected competitors in terms of its %5 significance level. Moreover, the algorithm proposed in the 3D-BPP data set is the most successful algorithm with its 9745 bins. Besides, the proposed algorithm is also compared to the four most popular algorithms in the literature. The results obtained confirm the validity of the proposed algorithm. [Display omitted] • A new version of HHO is presented • The way random variants are determined increases the performance of HHO. • The decision mechanism of HHO is reduced from 6 to 4. • The performance of MHAs could be increased by simplifying them. • The offered algorithm succeeds in continuous and discrete problems. [ABSTRACT FROM AUTHOR]

For years, meta-heuristic algorithms have been widely studied and many improved versions have been developed: from the evolution of the swarm topologies of the Particle Swarm Optimization algorithm, to the using of machine learning to Differential Evolutionary algorithms. However, the tuning of the fundamental meta-heuristic parameters has been less studied, but may lead to significant improvements on the convergence accuracy of these algorithms. This paper aims at developing an automated methodology to calibrate the parameters of population-based meta-heuristic algorithms for optimization problems. Based on the kriging estimation of the best combination of parameters, the Automated parameter tuning of Meta-heuristics (AptM) methodology gives the optimal algorithm setup for each considered problem in order to lead to a better convergence accuracy. The proposed AptM methodology is used to tune three different meta-heuristic algorithms, each applied to twelve mathematical unimodal or multimodal objective functions. AptM methodology performance is assessed by comparison of classical setups usually used in the literature. The numerical results show that the AptM methodology allows a significant improvement of the convergence accuracy of meta-heuristics with an average improvement of 62.02%, 69.12% and 64.94% on optimization problems defined in dimensions 10, 30 and 50 respectively. An experimental criterion is defined based on the convergence accuracy of the AptM methodology over the classical setups, assessing the AptM performances. The previous experimental criterion allows to compare the AptM methodology over the base-set. The AptM methodology shows a significant improvement of the algorithms performance on 97.2% of the tested problems. [ABSTRACT FROM AUTHOR]

This paper proposes a novel bio-inspired termite queen algorithm (TQA) to solve optimization problems by simulating the division of labor in termite populations under a queen's rule. TQA is benchmarked on a set of 23 functions to test its performance at solving global optimization problems, and applied to six real-world engineering design problems to verify its reliability and effectiveness. Comparative simulation studies with other algorithms are conducted, from whose results it is observed that TQA satisfactorily solves global optimization problems and engineering design problems. [ABSTRACT FROM AUTHOR]

Firefly algorithm (FA) is a meta-heuristic optimization algorithm inspired by the flickering behavior of fireflies. Due to its excellent performance, it has been widely used in engineering field.​ However, FA is a population-based algorithm, which needs to occupy a lot of running memory. It is adverse for some small wind turbines or scenarios with limited memory Therefore, this paper proposes an improved firefly algorithm, called parallel compact firefly algorithm (PCFA). The compact technique helps FA save operation memory, which is advantageous for some usage scenarios limited memory. And the parallel technique helps compact FA achieve better solutions and faster convergence. The proposed PCFA was tested on 28 benchmark functions and applied to the proportional–integral–derivative (PID) parameter tuning of the variable pitch wind turbine. Results demonstrate that (1) PCFA is superior to common compact optimization algorithms not only on less memory consumption, but also on more competitive solutions and faster convergence. (2) PCFA shows the better applicability in the PID parameter tuning of variable pitch wind turbine. It can availably smooth the power output of wind turbine under less memory consumption. [ABSTRACT FROM AUTHOR]

The Combined Heat and Power Economic Dispatch (CHPED) is a real-world optimization problem with several complex constraints that has been a topic of studies around energy systems and optimization processes. This paper attempts to conceptualize a potent algorithm by combining the Modified Grasshopper Optimization Algorithm (MGOA) and the Improved Harris Hawks Optimizer (IHHO) for attaining a better balance between the beginning stages of global search and the latter stages of global convergence. The proposed attempt is abbreviated as MGOA-IHHO. Firstly, the chaotic and Opposition-Based Learning (OBL) methods are invoked to generate the initial population. Second, the mathematical model of the conventional Grasshopper Optimization Algorithm (GOA) is modified using Sine–Cosine Acceleration Coefficients (SCAC) to simulate the global exploration at the initial iterations and graduating to the global convergence at the final stages of optimization. Hence, it is named MGOA. Finally, the adaptive search mechanism integrates the two improved search phases of HHO with a search phase of MGOA to improve the performance of the proposed optimization method. This mechanism investigates the best solution for the aging level of the individual during the optimal evaluation process for choosing an appropriate search phase in MGOA-IHHO. The intended effect of the proposed MGOA-IHHO method is verified with other nature-inspired methods on standard single-objective test functions including 23 benchmark problems, 30 test suits of IEEE Congress on Evolutionary Computation 2017 (CEC2017), and four CHPED problems. The statistical results ascertain that the proposed hybridized MGOA-IHHO is capable of providing promising results when compared with its variants and optimization algorithms introduced in the literature. [Display omitted] • A hybrid Grasshopper optimization algorithm and Harris Hawks optimizer is proposed. • Three mechanisms are applied in the proposed hybrid MGOA-IHHO algorithm. • The MGOA-IHHO algorithm is tested on benchmark functions and CHPED problems. • The results are evaluated based on search capability in cost-effectiveness analysis. • Non-parametrical tests are conducted to demonstrate the efficiency of the MGOA-IHHO. [ABSTRACT FROM AUTHOR]

Automatic image segmentation is a challenging task in computer vision applications, especially in the presence of occluded objects, varying color, and different lighting conditions. The advancement of depth-sensing technologies has introduced RGB-Depth cameras which are capable to generate RGB-Depth images and brought significant changes in computer vision applications. However, the segmentation of RGB-Depth images is a difficult task. Therefore, in this paper, a new segmentation method for RGB-Depth images has been introduced and named as random Henry gas solubility optimization-fuzzy clustering method. Firstly, a random Henry gas solubility optimization algorithm has been developed. Next, the proposed optimization algorithm has been employed to obtain optimal fuzzy clusters which are finally merged through segmentation by aggregating superpixels. The standard NYU depth V2 RGB-Depth indoor image dataset is used for performance evaluation. The proposed segmentation approach has been compared with five different methods namely, kmeans, fuzzy c-means, Henry gas solubility optimization algorithm, chaotic gravitational search algorithm, and J-Segmentation in terms of qualitative and quantitative measures. The result analysis shows that the proposed RGB-D segmentation method outperforms the other considered methods. [ABSTRACT FROM AUTHOR]

Accurate identification of the unknown parameters in fractional chaotic systems is crucial for their precise control. However, the evaluation of these systems is relatively expensive in the sense that its numerical process demands considerable time. Thus, it is essential to design a less time-demanding algorithm with high accuracy. Motivated by this, this paper aims to propose an algorithm with high accuracy and quick convergence speed, leading to a smaller computational budget. To achieve this goal, an Improved Surrogate-Assisted Whale Optimization Algorithm, denoted as ISAWOA, is proposed. A surrogate-assisted model is employed to approximate the fitness function, then, both the Lévy flight and the quadratic interpolation techniques are used to improve the exploration and exploitation capacity of the Whale Optimization Algorithm. The simulation results on 20 classical benchmark functions demonstrate that ISAWOA is able to locate an optimal solution with high accuracy and much faster than 14 other algorithms found in the literature. Finally, the proposed algorithm is validated on several representative fractional chaotic systems where ISAWOA is again able to outperform other methods, both in precision and CPU time. The overall test results show that ISAWOA is a promising algorithm with high accuracy, quick convergence, and that it requires a moderate amount of CPU time when dealing with parameter estimation problems on fractional chaotic systems. [ABSTRACT FROM AUTHOR]

In recent years, brain storm optimization (BSO) algorithm has received much attention in solving classical optimization problems and is used to implement evolutionary classification models. However, in practical applications, large-scale datasets complicate the structure of the classification model, which can have a great impact on the classification performance. In the optimization process, the traditional single-strategy BSO cannot preserve the information of dominant solution well, and its generation strategy is inefficient in solving various complex practical problems. To solve this problem, we introduce feature selection to improve the optimization model structure. Meanwhile, in order to enhance the search capability of BSO, three new generation strategy are embedded in the BSO algorithm in this paper. With the three generation methods of global optimal, local optimal and nearest neighbor, the information of the dominant solution can be better preserved and the search efficiency can be improved. The performance of the proposed generation strategy in solving classification problems is demonstrated on ten datasets with different sizes and dimensions. The experimental results reveal that the new generation strategy can enhance the performance of BSO algorithm for solving classification problems. [ABSTRACT FROM AUTHOR]

Grey wolf optimizer (GWO) is a widespread metaphor-based algorithm based on the enhanced variants of velocity-free particle swarm optimizer with proven defects and shortcomings in performance. Regardless of the proven defect and lack of novelty in this algorithm, the GWO has a simple algorithm and it may face considerable unbalanced exploration and exploitation trends. However, GWO is easy to be utilized, and it has a low capacity to deal with multi-modal functions, and it quickly falls into the optima trap or fails to find the global optimal solution. To improve the shortcomings of the basic GWO, this paper proposes an improved GWO called multi-stage grey wolf optimizer (MGWO). By dividing the search process into three stages and using different population updating strategies at each stage, the MGWO's optimization ability is improved while maintaining a certain convergence speed. The MGWO cannot easily fall into premature convergence and has a better ability to get rid of the local optima trap than GWO. Meanwhile, the MGWO achieves a better balance of exploration and exploitation and has a rough balance curve. Hence, the proposed MGWO can obtain a higher-quality solution. Based on verification on the thirty benchmark functions of IEEE CEC2017 as the objective functions, the simulation experiments in which MGWO compared with some swarm-based optimization algorithms and the balance and diversity analysis were conducted. The results verify the effectiveness and superiority of MGWO. Finally, the MGWO was applied to the multi-threshold image segmentation of Leaf Spot Diseases on Maize at four different threshold levels. The segmentation results were analysed by comparing each comparative algorithm's PSNR, SSIM, and FSIM. The results proved that the MGWO has noticeable competitiveness, and it can be used as an effective optimizer for multi-threshold image segmentation. • An improved multi-stage grey wolf optimizer (MGWO) is proposed. • The multi-stage strategy is used to balance exploration and exploitation. • A part of salp swarm algorithm strategy is introduced into GWO. • The greater performance of MGWO is verified over some advanced algorithms. • MGWO is tested on multi-threshold image segmentation of Leaf Spot Diseases on Maize. [ABSTRACT FROM AUTHOR]

This paper proposes a new multi-objective evolutionary algorithm by adapting the recent Henry Gas Solubility Optimization (HGSO) with multiple objectives. The proposed MOHGSO uses the Pareto dominance relation as means of comparison and integrates two types of archive, while an elite archive is used to store the Pareto solutions found over the evolutionary process, the other external-archives are used to store the local best solutions corresponding to each cluster. Moreover, efficient archiving and leader selection strategies based on the crowding distance computation are proposed to guide the population towards the true Pareto front. The performance of the MOHGSO algorithm is validated through an extensive comparison with three well-known algorithms on twelve test functions and four engineering design problems. The experiments of two widely used metrics in the field called IGD and Sp metrics show the ability of the proposed algorithm in achieving interesting results. Furthermore, the statistical results related to the Wilcoxon test indicate that the proposed algorithm outperforms significantly the selected methods for the above metrics at a 99% confidence level. [ABSTRACT FROM AUTHOR]

This paper presents Outranking-based Particle Swarm Optimisation (O-PSO) a novel metaheuristic to address the multi-objective Unrelated Parallel Machine Scheduling Problem. It is a particle swarm optimisation algorithm enriched with the preferences of the Decision Maker (DM), articulated in a fuzzy relational system based on ELECTRE III. Unlike other multi-objective metaheuristics, O-PSO searches for the Region of Interest (RoI) instead of approximating a sample of the complete Pareto frontier. The RoI is the subset consisting of those Pareto-efficient solutions that satisfy the outranking relations, that is, they are the best solutions in terms of the DM's system of preferences. Therefore, O-PSO not only approximates the Pareto solutions but also supports multicriteria decision analysis of the schedules. The efficiency of O-PSO is validated on a benchmark of synthetic instances from the scientific literature, where the Wilcoxon rank-sum test provides statistical evidence that O-PSO offers high-quality solutions when compared with two state-of-the-art metaheuristics; specifically, O-PSO is capable of generating a greater proportion of solutions (on average, ranging from 7% to 14%) dominating those of the state-of-the-art algorithms, as well as finding more solutions (from 13% to 18%) that satisfy the DM's preferences. O-PSO is also applied to a real-world case study in the transport industry to provide evidence for its applicability. [ABSTRACT FROM AUTHOR]

Coverage plays a vital role in the performance and proper functioning of wireless sensor networks. However, ensuring a network's coverage is met numerous challenges due to sensors having limited sensing range, communication range, and energy. Many coverage problems are NP-hard, one of which is the network coverage with lifetime problem (CTLP). As such, a number of meta-heuristic algorithms have been proposed to solve CTLP in practical scenarios. This paper proposes an approach for CTLP based on the teaching–learning based optimization algorithm (TLBO), which is often employed to address continuous optimization problems. Specifically, a discrete version of multi-objective improved teaching–learning based optimization algorithm (MO-ITLBO) called HTLBO is proposed, employing genetic operators inspired by evolutionary computing methods. Experimental results are extensively compared to those obtained from previous approaches, namely MO-ITLBO, fast elitist non-dominated sorting genetic algorithm (NSGA-II), multi-objective differential evolution (MODE), and multi-objective evolutionary algorithm based on decomposition (MOEA/D). The evaluation shows significant improvements in different metrics, including spacing, hypervolume, non-dominated solutions, and coverage. • We investigate the problem of optimal sensor node placement with three objectives: (i) minimize the number of deployed sensor nodes, (ii) maximize the k -coverage metric, and (iii) maximize the network lifetime. • We propose a hybrid algorithm combining teaching–learning based optimization and evolutionary computing for multi-objective sensor placement. The proposed algorithm introduces multiple teachers to improve the learners' results in different subjects. Moreover, a learner interacts with other learners through a crossover operator. • We compare the proposed algorithm with existing methods, including MODE, MOEAD, MO-TLBO, and NSGA-II on C-metric, spacing-metric, hypervolume-metric, and non-dominated solutions metric to demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

Sine Cosine Algorithm (SCA) that is one of the population-based metaheuristic optimization algorithms basically consists of the updating mechanism based on sine and cosine functions. In this algorithm, a few random and adaptive variables are also utilized for more effective motions of the candidate solutions. SCA has some drawbacks like other some metaheuristic algorithms. SCA tends to be stuck into the local regions in the search space and this affects negatively on the computational effort required to find the best solution point in the search space. This paper presents four different improved versions of SCA. The proposed improvements on original SCA are the innovations on the updating mechanism of SCA. To evaluate the performances of Improved Sine Cosine Algorithms (ImpSCAs), well-known numerical optimization problems including CEC 2014 test suite are used. Firstly, different analyses of the proposed ImpSCAs are dealt with such as the convergence analysis, search history analysis, trajectory analysis, average distance analysis, and computational complexity analysis. Secondly, the proposed four versions of ImpSCAs are compared with the original SCA for CEC 2014 benchmark problems with dimension sizes of 10D, 30D and 50D. Finally, original SCA and ImpSCAs are adapted to select optimal feature combination and they are tested for 10 feature selection datasets taken from the UCI machine learning repository. The benchmark results show that the performances of the ImpSCA 1 , ImpSCA 2 , and ImpSCA 4 are better than that of the original SCA. From the feature selection results, it is observed that three versions of ImpSCAs (except ImpSCA 3) outperform the original SCA in 80% of the datasets. Source codes of ImpSCAs are publicly available at https://github.com/uguryuzgec/ImpSCAs. • We focused to improve the exploration and exploitation ability of the original SCA. • The basic idea is based on the innovations on the update mechanism. • The ISCAs were evaluated using CEC2014 benchmark test suite. • The proposed ISCAs were tested for 10 popular UCI datasets. • Three versions of ISCAs (except ISCA 3) outperform the SCA in 80% of the datasets. [ABSTRACT FROM AUTHOR]

Topology optimization has emerged as a popular approach to refine a component's design and increase its performance. However, current state-of-the-art topology optimization frameworks are compute-intensive, mainly due to multiple finite element analysis iterations required to evaluate the component's performance during the optimization process. Recently, machine learning (ML)-based topology optimization methods have been explored by researchers to alleviate this issue. However, previous ML approaches have mainly been demonstrated on simple two-dimensional applications with low-resolution geometry. Further, current methods are based on a single ML model for end-to-end prediction, which requires a large dataset for training. These challenges make it non-trivial to extend current approaches to higher resolutions. In this paper, we develop deep learning-based frameworks consistent with traditional topology optimization algorithms for 3D topology optimization with a reasonably fine (high) resolution. We achieve this by training multiple networks, each learning a different step of the overall topology optimization methodology, making the framework more consistent with the topology optimization algorithm. We demonstrate the application of our framework on both 2D and 3D geometries. The results show that our approach predicts the final optimized design better (5.76 × reduction in total compliance MSE in 2D; 2.03 × reduction in total compliance MSE in 3D) than current ML-based topology optimization methods. • A deep learning framework consistent with SIMP algorithm for topology optimization. • Framework scalable to high resolution in 3D structural topology optimization. • Uses intermediate density and compliance to improve the final topology prediction. • Framework results validated using ground truth SIMP-based topology optimization. • More than 5x reduction in error compared to a single density-based ML model. [ABSTRACT FROM AUTHOR]

The three-parameter (3-p) Weibull distribution is an extremely important distribution to characterise the statistical behaviour of a large number of real world phenomenons. It is also useful as a failure model in analysing the reliability of different types of mechanical and electrical components/systems. Successful applications of the distribution rely on an accurate estimation of its three parameters because it directly affects the reliability and lifetime analysis. Due to the intricate system of nonlinear equations and the complexity of the likelihood function, derivative-based optimisation methods may fail to converge. Thus, an efficient and effective method for estimating the parameters of the model is important from the practical viewpoint. In this paper, an optimisation scheme based on an acceptance–rejection (AR) mechanism coupled with an elegant nested sampling (NS) technique is proposed to tackle this problem. The idea is to gradually approach the region of optimal solutions through an efficient sampling technique and a reweighting scheme. The AR-NS algorithm allows a good exploration of the parameter space and converges towards higher likelihood regions by decreasing progressively a pre-specified tolerance threshold. The proposed approach gives the entire distributions of the optimal estimates rather than a single point estimates. To demonstrate the practicality and the efficiency of the proposed approach, numerous numerical examples using simulated data and real-world engineering cases will be given. The obtained results show that the AR-NS algorithm is a suitable method for estimating the parameters of lifetime distributions using different distances. • An efficient algorithm for estimating the parameters of parametric lifetime distributions is proposed. • The optimisation scheme approaches gradually the optimal solutions by propagating "good" particles. • The proposed algorithm provides the entire distributions of the optimal estimates rather than a single point estimates. • The efficiency of the optimisation algorithm has been demonstrated through numerous examples. [ABSTRACT FROM AUTHOR]

As an efficient and simple optimization algorithm, particle swarm optimization (PSO) has been widely applied to solve various real optimization problems. However, avoiding premature convergence and balancing the global exploration and local exploitation capabilities of the PSO remains two crucial problems. To overcome these drawbacks of PSO, a hybrid particle swarm optimization with crisscross learning strategy (PSO-CL) algorithm is proposed in this paper. In PSO-CL, in order to well balance the global exploration and local exploitation capabilities of PSO, a search direction adjustment mechanism based on subpopulation division operation is proposed. Meantime, to avoid the premature convergence and enhance the global search ability, a crossover-based comprehensive learning strategy (CCL) is adopted. Additionally, a stochastic example learning strategy (SEL) is introduced, which can assist collective information to be spread among separate sub-swarms, improve the local exploitation ability of the algorithm. 15 classic benchmark functions, CEC2017 test suite and two real-world optimization problems are utilized to verify the promising performance of PSO-CL, experimental results and statistical analysis indicate that PSO-CL has competitive performance compared with state-of-the-art PSO variants. [Display omitted] • A search direction adjustment mechanism based on subpopulation division operation. • A crossover-based comprehensive learning strategy is presented, which can enhance the diversity of swarm. • A stochastic example learning strategy is introduced to further enhance the convergence precise. [ABSTRACT FROM AUTHOR]

The shape optimization of developable surfaces is a pivotal and knotty technique in CAD/CAM and used in many product manufacturing planning operations, e.g., for ships, aircraft wing, automobiles, garments, etc. In this paper, an improved marine predators algorithm (MPA) is used to optimize the shape of shape-adjustable generalized cubic developable Ball (SGCD-Ball, for short) surfaces. Firstly, to solve the problems of shape adjustment and optimization for developable surfaces, we present a class of novel shape-adjustable generalized cubic Ball basis functions, and then construct the SGCD-Ball surfaces with shape parameters by using the presented basis functions. The shapes of the surfaces can be adjusted and optimized expediently by using the shape parameters. Secondly, the shape optimization of developable surfaces is mathematically an optimization problem that can be effectively dealt with by swarm intelligence algorithm. In this regard, by incorporating a quasi-opposition strategy and a differential evolution algorithm to the MPA, an improved MPA called ODMPA is developed to increase the population diversity and enhance its capability of jumping out of the local minima. Furthermore, the superiority of the proposed ODMPA is verified by comparing with standard MPA , modified MPA and several well-known intelligent algorithms on 23 classical benchmark functions, the CEC'17 test suite and three engineering optimization problems, respectively. Finally, by minimizing the energy of the SGCD-Ball surfaces as the evaluation standard, the shape optimization models of the corresponding enveloping and spine curve developable surfaces are established. The ODMPA is utilized to solve the shape optimization models, and the SGCD-Ball surfaces with minimum energy are obtained. Some representative numerical examples demonstrate the superiority of the proposed ODMPA in effectively solving the shape optimization models in terms of precision and robustness. [ABSTRACT FROM AUTHOR]

This paper focuses on the scheduling problem of flexible assembly systems (FASs) without intermediate buffers. The main characteristic of the problem is that as no intermediate buffer exists between consecutive machines, blocking and deadlock constraints must be considered. Petri nets are used to model the considered FASs, and a novel hybrid particle swarm optimization (HPSO) algorithm is proposed to minimize the makespan. The proposed algorithm is the combination of the discrete PSO, particle repairing algorithm, particle improvement strategy, and local search method. First, each candidate solution for the problem is encoded as a permutation with repetition of part numbers, and can be uniquely decoded into a sequence of transitions. To ensure the feasibility of solutions, a repairing algorithm is developed, in which a deadlock avoidance policy is used. Then, a particle improvement policy is proposed to improve the performance of particles. Meanwhile, a local search method is designed and incorporated into HPSO to improve its search ability. Experiments are conducted to verify the effectiveness of the particle improvement policy and local search method. Comparisons between HPSO and ten other algorithms are performed. The comparison results and analysis show that our proposed algorithm can find feasible solutions for all tested instances, and is superior to other scheduling algorithms in terms of finding better solutions and performance stability. [ABSTRACT FROM AUTHOR]

Building a highly accurate model for photovoltaic (PV) cells based on actual sampled data is essential for the simulation, optimization, evaluation, and control of photovoltaic power generation systems. But finding globally optimal model parameters, which give the best fit to experimental data, is a great challenge. In this paper, a new optimization algorithm, called the micro-charge field effect P systems optimization algorithm (MFE-POA) is proposed. Though the analysis of the interaction among ionic substances inside a living cell membrane and considering the algorithm's dual needs of exploration and convergence accuracy, combined with the law of interaction between charges, we designed a novel micro-charge interaction rule based on distance, force characteristics, spatial location, and percentage of search completion, and embedded it into our existing P systems optimization algorithm (POA). Numerical studies and results analysis on some benchmark test functions demonstrate that MFE-POA can produce solutions of high quality and has great stability. The proposed method is applied to the model parameter estimation of the two types of PV cell models and multi-cell PV modules in different environmental conditions. The experimental results clearly argue the effectiveness of our proposed MFE-POA. Comparisons with other methods are presented and the results show that the proposed optimization algorithm is helpful and worth great promoting for parameter estimation in renewable energy modeling and prediction. [Display omitted] • Micro-charge field effect among ionic substances in living cells were discussed. • A novel P systems based optimization algorithm, called MFE-POA, is proposed. • The global search accuracy and stability of MFE-POA were validated. • Parameters of two type of PV cell models were estimated successfully by MFE_POA. • i – v features of PV modules under different conditions were predicted successfully by MFE_POA. [ABSTRACT FROM AUTHOR]

In the context of small sample and poor information, the data often change rapidly and interact with multiple factors which make it a challenge to analyse and predict multivariate sequences efficiently. Due to the fact that real systems usually show fractional order characteristics, the aim of this paper is to consider the approach improving the forecast results of multivariate interval grey prediction model via exploiting fractional accumulation. Noting that fractional accumulation could disturb the exponential law, the connotation prediction method is introduced to balance the disturbance. Correspondingly, a fractional connotation prediction method is constructed. In addition, traditional background value coefficient is optimized by using the particle swarm optimization algorithm (PSO). Therefore, a multivariate interval grey fractional accumulative connotation prediction model with optimized background value coefficient is constructed, in which the interval grey number time series are transformed into kernel series and radius series. Finally, the developed model is applied to clean energy prediction in China to verify the feasibility and validity. [Display omitted] • A multivariate interval grey prediction model via fractional accumulation is bulit. • The dynamic background value is optimized by integral mean value theorem and PSO. • The developed model is applied to model clean energy datum in China. [ABSTRACT FROM AUTHOR]

Tree-Seed Algorithm (TSA) is a meta-heuristic optimization algorithm with good performance in solving continuous optimization problems. However, there is an imbalance between its exploration and exploitation when solving complex problems, mainly lack exploration. To overcome this problem, this paper proposes two tree migration mechanisms: triple-learning-based migration mechanism and sine-random-distribution migration mechanism. The target tree position is migrated by learning from the first three trees in the current iteration. The sine function is added to the tree migration formula to enhance the randomness of tree distribution. In order to verify these migration mechanisms, Triple Tree-Seed Algorithm (TriTSA) has been proposed and compared with TSA on 30 well-known test functions from IEEE CEC 2014. In addition, STSA, SCA, PSO, ABC, Jaya, and TLBO are adopted in some comparative experiments on different dimensions. The experiments show that the tree migration mechanism can improve the optimization capability of the original algorithm effectively. On all 30 benchmark test functions, TriTSA outperforms TSA on 10, 30, 50, and 100 dimensions by 70%, 90%, 90%, and 97% respectively. Finally, the proposed TriTSA is compared with TSA, ABC, PSO, and SCA on solving two classical engineering design problems. It is proved that the proposed algorithm is more applicable in solving practical problems. [ABSTRACT FROM AUTHOR]

Meta-heuristic optimization algorithms aim to tackle real world problems through maximizing some specific criteria such as performance, profit, and quality or minimizing others such as cost, time, and error. Accordingly, this paper introduces an improved version of a well-known optimization algorithm namely Archimedes optimization algorithm (AOA). The enhanced version combines two efficient strategies namely Local escaping operator (LEO) and Orthogonal learning (OL) to introduce the (I-AOA) optimization algorithm. Moreover, the performance of the proposed I-AOA has been evaluated on the CEC'2020 test suite, and three engineering design problems. Furthermore, I-AOA is applied to determine the optimal parameters of polymer electrolyte membrane (PEM) fuel cell (FC). Two commercial types of PEM fuel cells: 250W PEMFC and BCS 500W are considered to prove the superiority of the proposed optimizer. During the optimization procedure, the seven unknown parameters (ξ 1 , ξ 2 , ξ 3 , ξ 4 , λ , R C , and b) of PEM fuel cell are assigned to be the decision variables. Whereas the cost function that required to be in a minimum state is represented by the RMSE between the estimated cell voltage and the measured data. The obtained results by the I-AOA are compared to other well-known optimizers such as Whale Optimization Algorithm (WOA), Moth-Flame Optimization Algorithm (MFO), Sine Cosine Algorithm (SCA), Particle Swarm Optimization Algorithm (PSO), Harris hawks optimization (HHO), Tunicate Swarm Algorithm (TSA) and original AOA. The comparison confirmed the superiority of the suggested algorithm in identifying the optimum PEM fuel cell parameters considering various operating conditions compared to the other optimization algorithms. [ABSTRACT FROM AUTHOR]

Forecasting landslide displacement is an important issue in engineering geology. In this field, it is difficult to accurately forecast the displacement of the step point of a step-type landslide. In this study, a novel grey multivariate model is proposed for forecasting landslide displacement. The proposed model transforms the original sequence by using the Hausdorff derivative operator, determines the model parameters by using the particle swarm optimization algorithm, and uses the trapezoidal integral formula to calculate the predicted value. Two numerical examples show that the average absolute relative error and mean squared error of the proposed model are smaller than those of the recursive discrete multivariate grey model and the multivariable grey model with structure compatibility. The proposed model is used to forecast the displacement of the Bazimen landslide in the Three Gorges Reservoir area of China. The previous month's displacement, precipitation, and change in the reservoir water level are used as input variables. The results show that the performance of the proposed model is superior to that of the extreme learning machine model. This paper provides an effective method for forecasting displacement of the step point of a step-type landslide. [ABSTRACT FROM AUTHOR]