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Cloud computing allows users to access software and hardware resources over the network. Task scheduling plays a major role in achieving cost-effective execution. It is a process of allocating resources to certain tasks in order to optimize one or more criteria. Metaheuristics provide promising solutions for task scheduling by leveraging bio-inspired techniques. In order to solve the task scheduling problem, we present an algorithm that uses a new meta-heuristic algorithm namely, WO (Walrus Optimizer). The proposed method is known as WO-based Task Scheduling Algorithm. Its main objective is to decrease execution cost, load balancing, resource utilization, and makespan. WOTSA is compared against several popular meta-heuristic algorithms. According to the experimental results, WOTSA improves performance in terms of different benchmarks. [ABSTRACT FROM AUTHOR]

Background and Objectives: This paper explores the realm of optimization by synergistically integrating two unique metaheuristic algorithms: The Wild Horse Optimizer (WHO) and the Fireworks Algorithm (FWA). WHO, inspired by the behaviors of wild horses, demonstrates proficiency in global exploration, while FWA emulates the dynamic behavior of fireworks, thereby enhancing local exploitation. The goal is to harness the complementary strengths of these algorithms, achieving a harmonious balance between exploration and exploitation to enhance overall optimization performance. Methods: The study introduces a novel hybrid metaheuristic algorithm, WHOFWA, detailing its design and implementation. Emphasis is placed on the algorithm's ability to balance exploration and exploitation. Extensive experiments, featuring a diverse set of benchmark optimization problems, including general test functions and those from CEC 2005, CEC 2019, and 2022, assess WHOFWA's effectiveness. Comparative analyses involve WHO, FWA, and other metaheuristic algorithms such as Reptile Search Algorithm (RSA), Prairie Dog Optimization (PDO), Fick’s Law Optimization (FLA), and Ladybug Beetle Optimization (LBO). Results: According to the Friedman and Wilcoxon signed-rank tests, for all selected test functions, WHOFWA outperforms WHO, FWA, RSA, PDO, FLA, and LBO by 42%, 55%, 74%, 71%, 48%, and 52%, respectively. Finally, the results derived from addressing real-world constrained optimization problems using the proposed algorithm demonstrate its superior performance when compared to several well-regarded algorithms documented in the literature. Conclusion: In conclusion, WHOFWA, the hybrid metaheuristic algorithm uniting WHO and FWA, emerges as a powerful optimization tool. Its unique ability to balance exploration and exploitation yields superior performance compared to WHO, FWA, and benchmark algorithms. The study underscores WHOFWA's potential in tackling complex optimization problems, making a valuable contribution to the realm of metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

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Optimizers in Convolutional Neural Networks play an important role in many advanced deep learning models. Studies on advanced optimizers and modifications of existing optimizers continue to hold significant importance in the study of machine tools and algorithms. There are a number of studies to defend and the selection of these optimizers illustrate some of the challenges on the effectiveness of these optimizers. Comprehensive analysis on the optimizers and alteration with famous activation function Rectified Linear Unit (ReLU) offered to protect effectiveness. Significance is determined based on the adjustment with the original Softmax and ReLU. Experiments were performed with Adam, Root Mean Squared Propagation (RMSprop), Adaptive Learning Rate Method (Adadelta), Adaptive Gradient Algorithm (Adagrad) and Stochastic Gradient Descent (SGD) to examine the performance of Convolutional Neural Networks for image classification using the Canadian Institute for Advanced Research dataset (CIFAR-10). [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Covid-19 Detection Using Deep Correlation-Grey Wolf Optimizer." The correction states that references [26-32] in the article are irrelevant to the topic and should be deleted. The authors apologize for any inconvenience caused and provide an updated version of the article. The scientific conclusions of the article remain unaffected. [Extracted from the article]

In this paper, a mixed binary integer mathematical programming model is developed for integration of Multimode Resource-Constrainted Project Scheduling Problem (MRCPSP) under bonus-penalty policies and Quantity Discount Problem in Material Ordering (QDPMO) with the objective of minimizing the total project cost. By proving a theorem, an important property of the optimum solution of the problem is found, which reduces the search space significantly compared to previous studies. Since the Resource-Constraint Project Scheduling Problem (RCPSP) belongs to the class of problems that are NP-hard, four hybrid meta-heuristic algorithms called COA-GA, GWO-GA, PSO-GA, and GA-GA are developed and tuned to solve the problem. Each of the proposed algorithms consists of outside and inside search components, which determine the best schedule and materials procurement plan, respectively. Finally, a set of standard PROGEN test problems is solved using the proposed hybrid algorithms under fixed CPU time. The results show that the COA-GA algorithm outperforms others. [ABSTRACT FROM AUTHOR]

Copyright of Journal Computer Technology is the property of ECORFAN-Mexico S.C. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An ameliorated Frenet trajectory optimization (AFTO) method based on artificial emotion (AE) and an equilibrium optimizer (EO) is proposed for the local trajectory planning of an unmanned ground vehicle (UGV). An artificial emotional potential field (AEPF) model is established to simulate AE. To realize a humanoid driving mode with emotional intelligence, AE is introduced into the Frenet trajectory optimization (FTO) method to determine the optimal trajectory. Based on the optimal discrete goal state of the FTO method, a first-sampling-then-optimization (FSTO) framework combining the FTO method with the EO is designed to obtain the optimal trajectory in a continuous goal state space. With different AEPF levels corresponding to different types of obstacles, simulation results show that the AEPF effectively adjusts the trajectory into different levels of safe distance between the UGV and obstacles corresponding to the humanoid driving mode. From the results of 30 independent experiments based on the AEPF, the FSTO framework in the AFTO method is effective for optimizing the trajectory of the FTO method at a lower cost. Moreover, the effectiveness of the proposed method for different types of roads is verified on a straight road and a curved road with obstacles in simulation. The improvement based on emotional intelligence and trajectory optimization in the AFTO method provides a humanoid driving mode for the UGV in the continuous goal state space. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms have been significantly applied in addressing various real-world prediction problem, including in disease prediction. Having a reliable disease prediction model benefits many parties in providing proper preparation for prevention purposes. Hence, the number of cases can be reduced. In this study, a relatively new meta-heuristic algorithm namely Barnacle Mating Optimizer (BMO) is proposed for short term dengue outbreak prediction. The BMO prediction model is realized over real dengue cases data recorded in weekly frequency from Malaysia. In addition, meteorological data sets were also been employed as input. For evaluation purposes, error analysis relative to Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Mean Absolute Deviation (MAD) were employed to validate the performance of the identified algorithms which includes the comparison between BMO against Moth Flame Optimizer (MFO) and Grey Wolf Optimizer (GWO) algorithms. Upon simulation, the superiority is in favour to BMO by producing lower error rates. [ABSTRACT FROM AUTHOR]

As a result of an incredibly fast growth of the number and diversity of smart devices connectable to the internet, commonly through open wireless sensor networks (WSNs) in internet of things (IoT), the access of attackers to the network traffic in the form of intercepting, eavesdropping and rebroadcasting has become much easier. Anomaly or intrusion detection system (IDS) is an efficient security mechanism, however despite the maturity of anomaly detection technologies for wired networks, current technologies with high computational complexity are improper for resource-limited WSNs in IoT and they also fail to detect new WSN attacks. Furthermore, dealing with the huge amount of intrusion wireless traffic collected by sensors, causing slow detecting process, higher resource usage and inaccurate detection. Hence, considering WSN limitations for developing an IDS in IoT, establishes a significant challenge for security researchers. This paper proposes a new model to develop a support vector machine (SVM)-based lightweight IDS (LIDS) using combination concepts of genetic algorithm (GA) and mathematical equations of grey wolf optimizer (GWO) which is called GABGWO. The GABGWO through applying two new crossover and mutation operators tries to find the most relevant traffic features and eliminate worthless ones, in order to increase the performance of the LIDS. The performance of LIDS is evaluated using AWID real-world wireless dataset under two scenarios with and without using GABGWO. The results showed a promising behavior of the proposed GABGWO algorithm in choosing optimal traffics, decreasing the computational costs and providing high accuracies for LIDS. The hybrid algorithm is also compared to pure GA and GWO and other recent methods and it is found that its performance is better than them. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) with hundreds of narrow and consecutive spectral bands provides substantial information to discriminate various land-covers. However, the existence of redundant features/bands not only gives rise to increasing of computation time but also interferes the classification result of hyperspectral images. Obviously, it is a very challenging problem how to select an effective feature subset from original bands to reduce the dimensionality of the hyperspectral dataset. In this study, a novel unsupervised feature selection method is suggested to remove the redundant features of HSI by feature subspace decomposition and optimization of feature combination. Feature subset decomposition is achieved by the fuzzy c-means (FCM) algorithm. The optimal feature selection is based on the optimization process of grey wolf optimizer (GWO) algorithm and maximum entropy (ME) principle. To evaluate the effectiveness of the proposed method, experiments are conducted on three well-known hyperspectral datasets, Indian Pines, Pavia University, and Salinas. Six state-of-the-art feature selection methods are used to compare with the proposed method. Experimental results successfully confirm the superior performance of our proposal with respect to three classification accuracy indices overall accuracy (OA), average accuracy (AA) and kappa coefficient (κ). [ABSTRACT FROM AUTHOR]

In a photovoltaic system, a dc–dc power optimizer (DCPO) is an option to maximize the output power. At the same time, data links among DCPOs are often required for system monitoring and controlling. This paper proposes a novel power line communication method for the DCPOs, in which the data of a DCPO are modulated into the control loop of a power converter, and then transmitted through the series-connected dc power line to other DCPOs. In the process of communication, differential phase-shift keying modulation and discrete Fourier transformation demodulation are employed. To analyze the quality of communication, the communication model of the system is built, based on a small-signal model. Furthermore, the noises of the system, including switching, maximum power point tracking, and additive white Gaussian noise, are discussed and measured to evaluate the signal-to-noise ratio. Lastly, an experimental system including six DCPOs is established and tested, which verifies the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Highlights • An enhanced algorithm for solving multi-objective optimization problems is proposed. • The proposed algorithm improves the original Multi-Objective Grey Wolf Optimizer. • This improvement is related to the decomposition of multi-objective problems. • The proposed approach is evaluated on benchmark functions and real-world applications. • Statistical analysis corroborates the good performance of the proposed algorithm. Abstract An optimization technique aims to find the best solution to an optimization problem. If the problem considers only one objective function, the best solution will provide the optimal value for such objective. However, if the problem considers two or more objectives, the selection of solutions will not be that straightforward since such objective functions are usually in conflict. For this kind of optimization problems, the use of analytical or exact methods becomes impractical. Thus, heuristic or metaheuristic approaches have to be applied for finding the optimal solutions or, at least, approximate solutions to the optimum. As a consequence, a wide variety of metaheuristics inspired by nature has been proposed for solving optimization problems. Among them, the Grey Wolf Optimizer is a metaheuristic of recent creation that in the last few years has attracted the attention of many researchers. Furthermore, a multi-objective extension of this technique was recently introduced proving its high performance comparable to other multi-objective optimization methods. In this paper, a multi-objective grey wolf optimizer based on the decomposition is introduced. Our proposed algorithm approximates Pareto solutions cooperatively by defining a neighborhood relation among the scalarizing subproblems in which the multi-objective problem is decomposed. The performance of our proposed method is compared against those achieved by six state-of-the-art bio-inspired techniques showing its high performance in both, well-known benchmark problems and two real-life engineering problems. [ABSTRACT FROM AUTHOR]

Left ventricular non-compaction is characterized by the presence of multiple trabecules in the left ventricle myocardium, associated with multiple inter-trabecular recesses communicated with the ventricular cavity. The medical community needs an objective quantification of non-compacted cardiomyopathy, characterized by a trabeculated mass in the left ventricle myocardium. A software tool for the automatic quantification of the exact hyper-trabeculation degree in the left ventricle myocardium for a population of hypertrophic cardiomyopathy (QLVTHC) patients is developed and tested. End-diastolic cardiac magnetic resonance images of the patients are the input of the software, while the volumes of the compacted zones and the trabeculated zones are necessary to produce the percentage quantification of the trabecular zone with respect to the compacted zone. Significant improvements are obtained with respect to the manual process, by saving valuable diagnosis time. The development of a self-optimized software tool (SOST) based on the outputs of 50 patients with hypertrophic cardiomyopathy automatically produces the volumes of the compacted zones and the trabeculated zones, as a percentage quantification. Now, the SOST is tested with a different population of patients, with different characteristics. Besides, a parallelization for the detection of the external layer of the compacted zone allows the real-time analysis per slice in a patient, obtaining important speedups with regard to the QLVTHC proposed and the manual process used traditionally by cardiologists. [ABSTRACT FROM AUTHOR]

Abstract Metamaterials with hyperbolic equal-frequency contours possess unique properties, which offer novel means in elastic wave manipulation. The existing designs are either resonant-structure based that have large damping loss or have a relatively narrow working frequency range. In this paper, a topology optimization approach is developed to design non-resonant hyperbolic elastic metamaterials over a broad frequency range. Based on the physical nature of hyperbolic metamaterials, a two-step topology optimization design scheme is formulated and solved by a gradient-based optimizer. By engineering the dispersion band structure, non-resonant hyperbolic metamaterials have been successfully designed. Novel features such as negative refraction, wave partial focusing and super-resolution imaging have been numerically demonstrated. Highlights • A topology optimization scheme is proposed for hyperbolic elastic metamaterial design. • Structural integrity is ensured by imposing constraint on static effective modulus. • Novel wave features are demonstrated over broad frequency range. • Directional and polarization-dependent transmission has been achieved. [ABSTRACT FROM AUTHOR]

Abstract The hybrid flowshop scheduling problem (HFSP) has been widely studied in the past decades. The most commonly used criterion is production efficiency. Green criteria, such as energy consumption and carbon emission, have attracted growing attention with the improvement of the environment protection awareness. Limited attention has been paid to noise pollution. However, noise pollution can lead to health and emotion disorder. Thus, this paper studies a multi-objective HFSP considering noise pollution in addition to production efficiency and energy consumption. First, we formulate a new mixed-integer programming model for this multi-objective HFSP. To realize the green scheduling, one energy conservation/noise reduction strategy is embedded into this model. Then, a novel multi-objective cellular grey wolf optimizer (MOCGWO) is proposed to address this problem. The proposed MOCGWO integrates the merits of cellular automata (CA) for diversification and variable neighborhood search (VNS) for intensification, which balances exploration and exploitation. Finally, to validate the efficiency and effectiveness of the proposed MOCGWO, we compare our proposal with other well-known multi-objective evolutionary algorithms by conducting comparison experiments. The experimental results show that the proposed MOCGWO is significantly better than its competitors on this problem. Highlights • A mathematical model for a HFSP considering noise pollution is formulated. • A multi-objective cellular grey optimizer is presented to solve this problem. • One energy conservation/noise reduction strategy is proposed. • A variable neighborhood search based on problem property is developed. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a variant based on Lèvy flight was proposed to enhance the performance of two recently proposed optimizers. The first optimizer used in the study is Sine-Cosine Algorithm (SCA) while the second is Whale Optimization Algorithm (WOA). Both optimizers are composed of two phases of random walks in each optimization iteration and both have stagnation and premature convergence problems. Lèvy flight is used to replace the walk based on cosine function in the SCA and the spiral motion in the WOA as well. The Lèvy-based search guarantees a fraction of solutions to be generated apart from the current best solution and hence tolerates for optimizer stagnation, premature convergence, and allows for local optima avoidance. A smooth control of the scale of the Lèvy random walk is also proposed to ensure a smooth adaptation of exploration to exploitation switching. The proposed variants, as well as the original algorithms, were benchmarked using a set of unimodal, multimodal, fixed-dimension multimodal and composite benchmark functions. The evaluation is performed using a set of assessment indicators and results prove the capability of the proposed variants to outperform the original optimizers. Graphical abstract Highlights • Novel modifications for the SCA and WOA are proposed. • Random walk based on levy flight to enhance the convergence speed and global optima. • The proposed variants were benchmarked using a set of 29 test functions, • The evaluation is performed using a set of assessment indicators. • Results prove the capability of our variants to outperform the original optimizers. [ABSTRACT FROM AUTHOR]

Abstract This paper aims to optimize the monitoring trajectory of unmanned surface vessel (USV) attached with optical camera in applications such as sailboat race. It can be taken as the waypoints optimization problem with the index of minimal energy consumption and obstacle avoidance under water current. Grey wolf optimizer (GWO), which is a novel intelligent method imitating the leadership hierarchy and hunting mechanism of wolf swarm, is utilized to obtain the optimal trajectory. Considering the unsatisfactory searching ability of GWO in complex scenarios, the improved grey wolf optimizer (IGWO) is then proposed by introducing the grey wolf individual memory, the nonlinear convergence factor, and the selected initial population obtained from the tangent method. Finally the simulation results demonstrate the robustness, efficiency and feasibility of IGWO in different cases. [ABSTRACT FROM AUTHOR]

In this paper, a novel multi-verse optimized fuzzy-PID (proportional–integral–derivative) controller with derivative filter (fuzzy-PIDF) is proposed for load frequency control of a two-area multi-source power system, with each area consisting of a reheat thermal and a hydro unit. The superiority of the Multi-Verse Optimizer algorithm is demonstrated against some recently published modern heuristic optimization techniques. For comparative analysis of the proposed control scheme, the conventional integral, proportional–integral, and PIDF controllers are also implemented. The effect of the thyristor-controlled phase shifter, superconducting magnetic energy storage, and doubly fed induction generators, considered individually and in various combinations, is investigated. Further, with an integral controller optimized using Multi-Verse Optimizer, the value of the integral of time multiplied absolute error, one of the performance indicators, is reduced by 85.61%, 83.91%, 70.42%, 67.69%, and 73.15% compared with optimization of the integral controller using Ant Lion Optimizer, Grey Wolf Optimizer, Differential Evolution, Bacterial Foraging, and Particle Swarm Optimization, respectively. Also, to quote a representative case, with Multi-Verse Optimizer, the settling times in respect of frequency deviations of area-1, area-2, and tie-line power are improved by 94.36%, 98.28%, and 95.44%, respectively, as compared with the results obtained using Ant Lion Optimizer. Further, robustness of the proposed control scheme is also investigated against variation of system parameters within ±10% besides random step load disturbances. Of all the controllers implemented here, the proposed optimized fuzzy-PIDF controller exhibits the best performance. Modeling and simulations were carried out using MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

In this paper, an efficient Grey Wolf Optimizer (GWO) search algorithm is presented for solving the optimal power flow problem in a power system, enhanced by wind power plant. The GWO algorithm is based on meta-heuristic method, and it has been proven to give very competitive results in different optimization problems. First, by using the proposed technique, the system independent variables such as the generators' power outputs as well as the associated dependent variables like the bus voltage magnitudes, transformer tap setting and shunt VAR compensators values are optimized to meet the power system operation requirements. The Optimal power flow study is then performed to assess the impact of variable wind power generation on system parameters. Two standard power systems IEEE30 and IEEE57 are used to test and verify the effectiveness of the proposed GWO method. The obtained results are then compared with others given by available optimization methods in the literature. The outcome of the comparison proved the superiority of the GWO algorithm over other meta-heuristics techniques such as Modified Differential Evolution (MDE), Enhanced Genetic Algorithm (EGA), Particle Swarm Optimization (PSO), Biogeography Based Optimization (BBO), Artificial Bee Algorithm (ABC) and Tree-Seed Algorithm (TSA). [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is the presentation of a technique to be integrated in a numerical airfoil optimization scheme, for the exact satisfaction of a strict equality cross-sectional area constraint.Design/methodology/approach An airfoil optimization framework is presented, based on Area-Preserving Free-Form Deformation (AP FFD) technique. A parallel metamodel-assisted differential evolution (DE) algorithm is used as an optimizer. In each generation of the DE algorithm, before the evaluation of the fitness function, AP FFD is applied to each candidate solution, via coupling a classic B-Spline-based FFD with an area correction step. The area correction step is achieved by solving a sub problem, which consists of computing and applying the minimum possible offset to each one of the free-to-move control points of the FFD lattice, subject to the area preservation constraint.Findings The proposed methodology is able to obtain better values of the objective function, compared to both a classic penalty function approach and a generic framework for handling constraints, which suggests the separation of constraints and objectives (separation-sub-swarm), without any loss of the convergence capabilities of the DE algorithm, while it also guarantees an exact area preservation. Due to the linearity of the area constraint in each axis, the extraction of an inexpensive closed-form solution to the sub problem is possible by using the method of Lagrange multipliers.Practical implications AP FFD can be easily incorporated into any 2D shape optimization/design process, as it is a time-saving and easy-to-implement repair algorithm, independent from the nature of the problem at hand.Originality/value The proposed methodology proved to be an efficient tool in facing airfoil design problems, enhancing the rigidity of the optimal airfoil by preserving its cross-sectional area to a predefined value. [ABSTRACT FROM AUTHOR]

In this paper, an improved version of the Grey Wolf Optimizer (GWO) is proposed to improve the exploration and the exploitation ability of the GWO algorithm. This improvement is performed through using the chaotic logistic map, the Opposition-Based Learning (OBL), the differential evolution(DE), and the disruption operator (DO). Where, the chaotic logistic map and the OBL are used to initialize the candidate solutions and these approaches avoid the drawbacks of the random population and increase the convergence of the algorithm. Then, the DE operators are combined with the GWO algorithm, in which, the DE operators work as a local search mechanism to improve the exploitation ability of the GWO through updating the population. Also, after updating the solutions by using a hybrid between the GWO and the DE, the DO is used to enhance the exploration ability, in which, the DO is used to maintain the diversity of the population. Therefore, the combinations with chaotic logistic map, OBL, DE, and DO, provide the GWO with tools to better balance between the exploration and the exploitation of the search space without affecting the computational time required for this task. The proposed algorithm, called COGWO2D, is compared with other seven algorithms through a set of experimental series that have been performed over two benchmark functions, the classical CEC2005, and the CEC2014. Also, the performance of the proposed algorithm to improve the classification of the galaxy images is evaluated, where it is used as a feature selection method. The aim of this experiment is to select the optimal subset of features from the extracted features of the galaxy images. The experimental results support the efficacy of the proposed approach to find the optimal solutions of the global optimization problem, as well as, increase the accuracy of the classification of the galaxy images. [ABSTRACT FROM AUTHOR]

Grey wolf optimizer (GWO) is a newly developed metaheuristic inspired by hunting mechanism of grey wolves. The paramount challenge in GWO is that it is prone to stagnation in local optima. This paper proposes a cellular grey wolf optimizer with a topological structure (CGWO). The proposed CGWO has two characteristics. Firstly, each wolf has its own topological neighbors, and interactions among wolves are restricted to their neighbors, which favors exploitation of CGWO. Secondly, information diffusion mechanism by overlap among neighbors can allow to maintain the population diversity for longer, usually contributing to exploration. Empirical studies are conducted to compare the proposed algorithm with different metaheuristics such as success-history based adaptive differential evolution with linear population size reduction (LSHADE), teaching-learning based optimization algorithm (TLBO), effective butterfly optimizer with covariance matrix adapted retreat phase (EBOwithCMAR), novel dynamic harmony search (NDHS), bat-inspired algorithm (BA), comprehensive learning particle swarm optimizer (CLPSO), evolutionary algorithm based on decomposition (EAD), ring topology PSO (RPSO), crowding-based differential evolution (CDE), neighborhood based crowding differential evolution (NCDE), locally informed particle swarm (LIPS), some improved variants of GWO and GWO. Experimental results show that the proposed method performs better than the other algorithms on most benchmarks and engineering problems. [ABSTRACT FROM AUTHOR]

Highlights • A method for generating hard-to-solve instances in TSP is proposed. • Statistical distribution of distances in instances is correlated with hardness. • Fitness function for generating hard-to-solve instances is proposed. • Some examples of hard-to-solve instances are generated and shown. Abstract Travelling salesman problem is a classical combinatorial optimization problem. Instances of this problem have been used as benchmark for testing the performance of many proposals of discrete optimizer algorithms. However, the hardness or the difficulty degree to solve the instances has not been usually addressed. In the past the evaluation of the difficulty of the instances has required to obtain a high-quality solution, ideally the optimal one. However, this type of strategy burdens the evaluation with large processing times. In this work, diverse indirect measures for evaluating the hardness to solve instances of the travelling salesman problem are proposed. These evaluations are inferred from the spatial attributes of previously evaluated instances, and later correlated with the hardness of the instances. Finally, where a significant correlation is found, a linear model is built and linked to a genetic algorithm. As a consequence of this work, mechanisms for hardening instances of travelling salesman problem are implemented. [ABSTRACT FROM AUTHOR]

This study aimed to provide guidance on the advantages and limitations of a new optimizer, “photon optimizer” (PO), when compared with its predecessor, “progressive resolution optimizer” (PRO), for intensity-modulated arc therapy (IMAT) plans. Eleven study plans that included a cohort of prostate, head and neck, and brain treatment sites were optimized using both PRO and PO algorithms. A plan template using the same objectives for the same number of iterations was used for each optimized plan to obtain hypothetical treatment plans that would be comparable with a clinical plan. Analysis was performed using plan conformity-based parameters such as target volume coverage factor, conformation number and homogeneity indices, and plan complexity assessment parameters such as small aperture score, modulation indices, and monitor unit variation with arc angle for prostate, brain and head, and neck IMAT treatment plans. Plan conformality analysis demonstrated that conformation numbers, target volume coverage factors, and homogeneity indices produced by the 2 optimizers were comparable for most anatomic sites. IMAT treatment plans produced using the PRO optimizer were found to be less complex than plans produced using the PO optimizer, in terms of multileaf collimator (MLC) leaf position variability and modulation complexity scores. Similarly, the PRO optimizer was shown to produce treatment plans that used fewer monitor units (and generally fewer monitor unit per degree of arc rotation) than PO optimizer. This study demonstrated that the PO optimizer can produce IMAT treatment plans with a similar degree of dose conformity to the target volume and generally improved organ at risk sparing, compared with the PRO optimizer. Better coverage to organs at risk produced by plans optimized using PO was observed to have higher MLC variability and monitor units. Therefore, careful evaluation of treatment plan conformity and complexity before assessing its deliverability is recommended when implementing the routine use of PO optimizer. [ABSTRACT FROM AUTHOR]

Internet of vehicles (IoV) is a branch of the internet of things (IoT) which is used for communication among vehicles. As vehicular nodes are considered always in motion, hence it causes the frequent changes in the topology. These changes cause major issues in IoV like scalability, dynamic topology changes, and shortest path for routing. Clustering is among one of the solutions for such type of issues. In this paper, the stability of IoV topology in a dynamic environment is focused. The proposed metaheuristic dragonfly-based clustering algorithm CAVDO is used for cluster-based packet route optimization to make stable topology, and mobility aware dynamic transmission range algorithm (MA-DTR) is used with CAVDO for transmission range adaptation on the basis of traffic density. The proposed CAVDO with MA-DTR is compared with the progressive baseline techniques ant colony optimization (ACO) and comprehensive learning particle swarm optimization (CLPSO). Numerous experiments were performed keeping in view the transmission dynamics for stable topology. CAVDO performed better in many cases providing minimum number of clusters according to current channel condition. Considerable important parameters involved in clustering process are: number of un-clustered nodes as a re-clustering criterion, clustering time, re-clustering delay, dynamic transmission range, direction, and speed. According to these parameters, results indicate that CAVDO outperformed ACO-based clustering and CLPSO in various network settings. Additionally, to improve the network availability and to incorporate the functionalities of next-generation network infrastructure, 5G-enabled architecture is also utilized. [ABSTRACT FROM AUTHOR]

Purpose: Accuracy of dose calculation models and robustness under various uncertainties are key factors influencing the quality of intensity modulated proton therapy (IMPT) plans. To mitigate the effects of uncertainties and to improve the dose calculation accuracy, an all‐scenario robust IMPT optimization based on accurate Monte Carlo (MC) dose calculation was developed. Methods: In the all‐scenario robust IMPT optimization, dose volume histograms (DVHs) were computed for the nominal case and for each uncertainty scenario. All scenarios were weighted by DVH values dynamically throughout optimization iterations. In contrast, probabilistic approach weighted scenarios with fixed scenario weights and worst case optimizations picked one single scenario — the worst scenario for each iteration. Uncertainties in patient setup and proton range were considered in all clinical cases studied. Graphics processing unit (GPU) computation was employed to reduce the computational time in both the MC dose calculation and optimization stages. A previously published adaptive quasi‐Newton method for proton optimization was extended to include robustness. To validate the all‐scenario algorithm extension, it was compared with the single scenario optimization target volume (OTV) based approach in clinical cases of three different disease sites. Additional comparisons with worst case optimization methods were conducted to evaluate the performance of the all‐scenario robust optimization against other robust optimizations. Results: The all‐scenario robust IMPT optimization spared organs at risk (OARs) better than the OTV‐based method while maintaining target coverage and improving the robustness of targets and OARs. Compared with composite and voxel‐wise worst case optimization, the all‐scenario robust optimization converged faster, and arrived at solutions of tighter DVH robustness spread, better target coverage and lower OAR dose. Conclusion: An all‐scenario robust IMPT treatment planning system was developed using an adaptive quasi‐Newton optimization method. The optimization system was GPU‐accelerated and based on MC dose calculation. Improved performance was observed in clinical cases when compared with worst case optimization methods. [ABSTRACT FROM AUTHOR]

In this paper, two recent heuristic optimization algorithms are presented to optimally manage the operation of the micro-grid (MG) with installed renewable energy sources (RESs); krill herd (KH) optimization and ant lion optimizer (ALO) algorithms. The first algorithm is used for solving single-objective function represents either total operation cost or total pollutant emission injected from the installed generating units while ALO is applied to solve the multi-objective function of both total operating cost and emission. The problem is formulated as nonlinear constrained objective function with equality and inequality constraints. In this work; the devices installed in MGs are photovoltaic panel (PV), wind turbine (WT), micro-turbine (MT), fuel cell (FC), battery and grid. Two scenarios are studied; the first one is optimizing MG with installing all RESs within specified limits in addition to grid, while the second scenario is operating both PV and WT at their rated powers. The obtained results are compared with different reported algorithms like genetic algorithm (GA), Fuzzy self-adaptive PSO (FSAPSO) and others programmed like particle swarm optimization (PSO), grey-wolf optimizer (GWO) and whale optimization algorithm (WOA). For first scenario; the proposed KH gives the best optimal cost of 105.94 €ct while the best emission is 420.57 kg, the best optimal cost and emission of 592.86 €ct 339.71 kg are obtained via KH in the second scenario. [ABSTRACT FROM AUTHOR]

In the framework of prospective activities for a demonstration power plant, DEMO will be the next step for fusion energy following ITER tokamak. Some of the key design top-level questions can be addressed using macroscopic system-level codes. Those system codes aim to model the whole fusion plant with all its subsystems and identify the impact of their interactions on the design choices. The code SYstem COde for MOdelling REactors (SYCOMORE) is a modular system code developed to address key questions relevant to the tokamak fusion reactor design by giving a global view of technology and physic domains. Among all components, SYCOMORE provides a representation of the magnet system, which is of importance regarding some factor of merits, e.g., fusion power or cost. SYCOMORE is ultimately coupled with an optimizer, scanning a high number of operation configurations and ranking them along selected merits. This scanning requires fast computation for each scanned point, so the magnets modeling must meet a tradeoff between simplicity and accuracy. In this paper, we describe that the way toroidal field (TF), central solenoid (CS), and poloidal field (PF) systems modeling in SYCOMORE was chosen taking into consideration the driving design criteria used in usual magnet design method (temperature margin, copper maximum temperature during quench, mechanical resilience in stainless steel structural parts, etc). The specificities of the reduced magnet representation chosen approach will be exposed, and the benchmarking of the simplified model of the two main systems (TF and CS) applied on DEMO reactor configuration will be compared with the output of a more sophisticated design method using an elaborated tool (CEA design tool) that is used to establish the CEA design of DEMO magnets. Together with this benchmark, a discussion on the limits of this approach will be conducted. PF system implementation being in an early stage, the winding pack design module will be exposed (with its associated benchmarking part with CEA design tool), but not the part of PF current reconstruction with respect to system features. Parametric explorations of DEMO configurations will be reported along different conceptual choices related to the magnets (e.g., superconductor material and performances, and structure resilience limits), and the impact on the magnet system main design features will be exposed. Main lines of the future development work will also be presented. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a quantum inspired binary grey wolf optimizer (QI-BGWO) to solve unit commitment (UC) problem. The QI-BGWO integrates quantum computing concepts with BGWO to improve the hunting process of the wolf pack. The inherent properties of Q-bit and Q-gate concepts in quantum computing help in achieving better balance between exploration and exploitation properties of the search process. The position update processes of wolves at different hierarchy levels are replaced by Q-bit's individual probabilistic representation along with dynamic rotation angle and coordinate rotation gate. Therefore, solution approaches exploit the search properties of GWO and quantum computing using quantum bits, gates, superposition principle etc., to solve the unit commitment schedule efficiently. The results and statistical analysis demonstrates the effectiveness of proposed approaches in solving the UC problem and establishes the significance of proposed approaches among existing binary and quantum computing based heuristic approaches. [ABSTRACT FROM AUTHOR]

Grey wolf optimizer algorithm was recently presented as a new heuristic search algorithm with satisfactory results in real-valued and binary encoded optimization problems that are categorized in swarm intelligence optimization techniques. This algorithm is more effective than some conventional population-based algorithms, such as particle swarm optimization, differential evolution and gravitational search algorithm. Some grey wolf optimizer variants were developed by researchers to improve the performance of the basic grey wolf optimizer algorithm. Inspired by particle swarm optimization algorithm, this study investigates the performance of a new algorithm called Inspired grey wolf optimizer which extends the original grey wolf optimizer by adding two features, namely, a nonlinear adjustment strategy of the control parameter, and a modified position-updating equation based on the personal historical best position and the global best position. Experiments are performed on four classical high-dimensional benchmark functions, four test functions proposed in the IEEE Congress on Evolutionary Computation 2005 special session, three well-known engineering design problems, and one real-world problem. The results show that the proposed algorithm can find more accurate solutions and has higher convergence rate and less number of fitness function evaluations than the other compared techniques. [ABSTRACT FROM AUTHOR]

In this paper, a scheme which combines finite-time model-assisted active disturbance rejection control and novel greedy criterion-based salp swarm algorithm is developed for attitude tracking problem of hypersonic reentry vehicle with multiple disturbances. To simplify control structure, the control scheme is designed within the framework of active disturbance rejection control completely. To lessen tuning parameters, a sigmoid function-based tracking differentiator is employed to generate a more realizable attitude transient profile instead of utilizing conventional nonlinear tracking differentiator. Taking known model information as model-assisted term, finite-time model-assisted extended state observers are constructed to estimate the lumped disturbance in attitude and angular rate loop with employment of function fal . To achieve rapid response and strong robustness, finite-time model-assisted control laws are derived by utilizing function fhan . Finally, a novel greedy criterion-based salp swarm algorithm is used to optimize control parameters in finite-time model-assisted control laws to achieve optimal solution that minimizes the tracking error and energy consumption. Comparative simulations are conducted to illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to propose and validate an intuitive method for training and to validate knowledge‐based planning (KBP) systems based on a patient‐specific plan quality scoring. Methods: A sample of 80 clinical plans of prostate cancer patients were ranked on the basis of the Adjusted Plan Quality Metric (APQM%). This quality metric was computed normalizing the Plan Quality Metric (PQM%) score to the best possible OAR sparing estimated by the Feasibility DVH (FDVH) algorithm. Two different plan libraries were created, purging all the plans below the first quartile or below the median the APQM% distribution. These libraries were used to populate and train two RapidPlan models: respectively, the APMQ25% and the APMQ50% models. No further refinements or actions were undertaken on these two models. Their performances were benchmarked against another two RapidPlan models. An Uncleaned model, which was populated and trained with the initial sample of 80 plans, and a Cleaned model, obtained through the standard iterative cleaning and refinement process suggested by the vendor and in literature. The outcomes of a planning test based on 20 patients within the training library (closed loop) and 20 patients outside of the training library (open‐loop) were compared through various DVH metrics and the PQM% score. Results: The selection through APQM% thresholding roughly preserves the geometric variety of the Cleaned model; only the APMQ50% model showed a modest broadness reduction. The models generated through APQM% thresholding showed target coverage and OARs sparing equal or superior to the Uncleaned and Cleaned models both for the closed‐ and the open‐loop tests. No significant differences were found between the four models. PQM% analysis ranked the overall plan quality as: 86.5 ± 6.5% APQM50%, 83.1 ± 5.9% APQM25%, 80.39 ± 10.6% Cleaned and 79.4 ± 8.5% Uncleaned in the closed‐loop test; 84.9 ± 7.6% APQM50%, 82.6 ± 7.9% APQM25%, 80.39 ± 10.6% Cleaned and 79.4 ± 8.5% Uncleaned in the open‐loop test. Conclusions: Forward feeding a RapidPlan model through a thresholding selection based on APQM% is proven to produce equal or better results than a model based on a manually and iteratively refined population. A tighter APQM% threshold turns approximately into a higher average quality of plans generated with RapidPlan. A trade‐off must be found between the mean quality of the KBP library and its numerosity. The proposed KBP feeding method helps the KBP user, because it makes the model refinement more intuitive and less time consuming. [ABSTRACT FROM AUTHOR]

Although several meta-heuristics have been developed in the last two decades, most of them are population-based, undergo many steps along with several parameters that make them hard to understand and code. In addition, there are same procedures in recent metaheuristics which make them similar. So, the researchers usually are confused to select a metaheuristic and cannot find any superiority or at least in any algorithms. Because of this, the researchers still use the old algorithms instead of the recent ones. Contrary to previous work, this paper aims to develop a simple, intelligent and new single-solution algorithm that has just four main steps and three simple parameters to tune. Social Engineering Optimizer (SEO) starts with two initial solutions divided into attacker and defender. The attacker obtains the rules of Social Engineering techniques to reach its desired goals. By these simple features, the algorithm does precisely both intensification and diversification phases. The basis of the algorithm depends on how an attacker attacks to a defender by four different associated techniques. Finally, the proposed SEO is applied to solve a set of benchmark functions, important engineering and multi-objective optimization problems. The result shows its superiority in comparison with other well-known and recent meta-heuristics. [ABSTRACT FROM AUTHOR]

This novel article presents the multi-objective version of the recently proposed the Grey Wolf Optimizer (GWO) known as Non-Dominated Sorting Grey Wolf Optimizer (NSGWO). This proposed NSGWO algorithm works in such a manner that it first collects all non-dominated Pareto optimal solutions in achieve till the evolution of last iteration limit. The best solutions are then chosen from the collection of all Pareto optimal solutions using a crowding distance mechanism based on the coverage of solutions and leadership hierarchy of grey wolfs in nature to guide hunting of wolfs towards the dominated regions of multi-objective search spaces. For validate the efficiency and effectiveness of proposed NSGWO algorithm is applied to a set of standard unconstrained, constrained and engineering design problems. The results are verified by comparing NSGWO algorithm against Multi objective Colliding Bodies Optimizer (MOCBO), Multi objective Particle Swarm Optimizer (MOPSO), non-dominated sorting genetic algorithm II (NSGA-II) and Multi objective Symbiotic Organism Search (MOSOS). The results of proposed NSGWO algorithm validates its efficiency in terms of Execution Time (ET) and effectiveness in terms of Generalized Distance (GD), Diversity Metric (DM) on standard unconstraint, constraint and engineering design problem in terms of high coverage and faster convergence. [ABSTRACT FROM AUTHOR]

Clustering based on nature-inspired algorithms is considered as one of the fast growing areas that aims to benefit from such algorithms to formulate a clustering problem as an optimization problem. In this work, the search capabilities of a recent nature-inspired algorithm called Multi-verse Optimizer (MVO) is utilized to optimize clustering problems in two different approaches. The first one is a static clustering approach that works on a predefined number of clusters. The main objective of this approach is to maximize the distances between different clusters and to minimize the distances between the members in each cluster. In an attempt to overcome one of the major drawbacks of the traditional clustering algorithms, the second proposed approach is a dynamic clustering algorithm, in which the number of clusters is automatically detected without any prior information. The proposed approaches are tested using 12 real and artificial datasets and compared with several traditional and nature-inspired based clustering algorithms. The results show that static and dynamic MVO algorithms outperform the other clustering techniques on the majority of datasets. [ABSTRACT FROM AUTHOR]

The progression over wireless technologies paves the way for the emergence of wireless body area networks (WBAN) towards several motivating applications. Specifically, in terms of health concern applications, both the performance and reliability is regarded as the essential elements of WBANs. Many of the soft computational methodologies employed the manual modeling of fuzzy logic controllers (FLCs) by evolutionary algorithms in WBAN. This existing model encodes the entire control parameters of “FLCs” membership functions. This leads to the degradation of network performance by maximizing the latency. In order to rectify this issue, here we propose a hybrid firefly grey wolf optimizer (hybrid FGWO) approach for the optimal modeling of “FLC”. The major goal behind our proposed work relays on the optimal selection of control parameters from the “FLCs” with hybrid FGWO. The modeling of “FLCs” is carried out with CLFB (cross-layer fuzzy logic dependent back-off controller) mechanism to control the frequent access of channels. The efficiency of the “FLCs” model is enhanced by utilizing the coding technique known as unrestricted coding scheme. The performance of our hybrid FGWO approach is contrasted with three conventional “EAs”. Two major modeling goals are established whereas, the initial goal aims for the modeling of “FLCs” on particular configuration of network and the second goal aims on the modeling of “FLCs” over multiple network configurations. The “FLCs” modeled by means of our proposed hybrid FGWO approach exhibits its performance in terms of throughput, latency and packet delivery ratio with some of the challenging algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new method for solving multiobjective optimization problems with a large number of decision variables. The proposed method called weighted optimization framework is intended to serve as a generic method that can be used with any population-based metaheuristic algorithm. After explaining some general issues of large-scale optimization, we introduce a problem transformation scheme that is used to reduce the dimensionality of the search space and search for improved solutions in the reduced subspace. This involves so-called weights that are applied to alter the decision variables and are also subject to optimization. Our method relies on grouping mechanisms and employs a population-based algorithm as an optimizer for both original variables and weight variables. Different grouping mechanisms and transformation functions within the framework are explained and their advantages and disadvantages are examined. Our experiments use test problems with 2–3 objectives 40–5000 variables. Using our approach on three well-known algorithms and comparing its performance with other large-scale optimizers, we show that our method can significantly outperform most existing methods in terms of solution quality as well as convergence rate on almost all tested problems for many-variable instances. [ABSTRACT FROM PUBLISHER]

This study proposes a modified convolutional neural network (CNN) algorithm that is based on dropout and the stochastic gradient descent (SGD) optimizer (MCNN-DS), after analyzing the problems of CNNs in extracting the convolution features, to improve the feature recognition rate and reduce the time-cost of CNNs. The MCNN-DS has a quadratic CNN structure and adopts the rectified linear unit as the activation function to avoid the gradient problem and accelerate convergence. To address the overfitting problem, the algorithm uses an SGD optimizer, which is implemented by inserting a dropout layer into the all-connected and output layers, to minimize cross entropy. This study used the datasets MNIST, HCL2000, and EnglishHand as the benchmark data, analyzed the performance of the SGD optimizer under different learning parameters, and found that the proposed algorithm exhibited good recognition performance when the learning rate was set to [0.05, 0.07]. The performances of WCNN, MLP-CNN, SVM-ELM, and MCNN-DS were compared. Statistical results showed the following: (1) For the benchmark MNIST, the MCNN-DS exhibited a high recognition rate of 99.97%, and the time-cost of the proposed algorithm was merely 21.95% of MLP-CNN, and 10.02% of SVM-ELM; (2) Compared with SVM-ELM, the average improvement in the recognition rate of MCNN-DS was 2.35% for the benchmark HCL2000, and the time-cost of MCNN-DS was only 15.41%; (3) For the EnglishHand test set, the lowest recognition rate of the algorithm was 84.93%, the highest recognition rate was 95.29%, and the average recognition rate was 89.77%. [ABSTRACT FROM AUTHOR]

In this work, a novel model predictive control (MPC) scheme is introduced, by integrating direct and indirect neural control methodologies. The proposed approach makes use of a robust inverse radial basis function (RBF) model taking into account the applicability domain criterion, in order to provide a suitable initial starting point for the optimizer, thus helping to solve the optimization problem faster. The performance of the proposed controller is evaluated on the control of a highly nonlinear system with fast dynamics and compared with different control schemes. Results show that the proposed approach outperforms the rivaling schemes in terms of response; moreover, it solves the optimization problem in less than one sampling period, thus effectively rendering MPC-based controllers capable of handling systems with fast dynamics. [ABSTRACT FROM AUTHOR]

For printable electronics fabrication, a major challenge is the print resolution and accuracy delivered by a drop-on-demand piezoelectric inkjet printhead. In order to meet the challenging requirements of printable electronics fabrication, this paper proposes a novel restructured artificial bee colony optimizer called HABC for optimal prediction of the droplet volume and velocity. The main idea of HABC is to develop an adaptive and cooperative scheme by combining life-cycle, Powell's search and crossover-based social learning strategies for complex optimizations. HABC is a more biologically-realistic model that the reproduce and die dynamically throughout the foraging process and the population size varies as the algorithm runs. With the crossover operator, the information exchange ability of the bees can be enhanced in the early exploration phase while the Powell's search enables the bees deeply exploit around the promising area, which provides an appropriate balance between exploration and exploitation. The proposed algorithm is benchmarked against other four state-of-the-art bio-inspired algorithms using both classical and CEC2005 test function suites. Then HABC is applied to predict the printing quality using nano-silver ink. Statistical analysis of all these tests highlights the significant performance improvement due to the beneficial combination and shows that the proposed HABC outperforms the reference algorithms. [ABSTRACT FROM AUTHOR]

The construction and application of a multiple nitrogen liquefier unit (NLU) optimal scheduling tool is discussed. Constrained by customer demands and subject to electricity spot market prices over a week-ahead horizon, a retrospective optimiser (RO) determines the minimum scheduling costs. Plant start-up penalties and inter-site optimisation capabilities are incorporated into the optimisation model to emulate realistic operational flexibilities and costs. Using operational data, actual process schedules are compared to the RO results leading to improved process scheduling insights; such as increasing afternoon NLU operation during the spring to utilise lower power pricing caused by high solar generation. The RO is used to output a trackable load management key performance indicator to quantify potential and achieved scheduling improvements. Subsequently, correlations between renewable energy generation and spot market power prices are developed. Forecast pricing is used within a predictive optimiser (PO) to automatically generate an optimal schedule for the week ahead to meet projected customer demands. The RO provides potential hindsight savings of around 11%, and the PO up to 8% (representing significant cost savings for such energy intensive processes). [ABSTRACT FROM AUTHOR]

The integration of scheduling workers to perform tasks with the traditional vehicle routing problem gives rise to the workforce scheduling and routing problems (WSRP). In the WSRP, a number of service technicians with different skills, and tasks at different locations with pre-defined time windows and skill requirements are given. It is required to find an assignment and ordering of technicians to tasks, where each task is performed within its time window by a technician with the required skill, for which the total cost of the routing is minimized. This paper describes an iterated local search (ILS) algorithm for the WSRP. The performance of the proposed algorithm is evaluated on benchmark instances against an off-the-shelf optimizer and an existing adaptive large neighbourhood search algorithm. The proposed ILS algorithm is also applied to solve the skill vehicle routing problem, which can be viewed as a special case of the WSRP. The computational results indicate that the proposed algorithm can produce high-quality solutions in short computation times. [ABSTRACT FROM AUTHOR]