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This work proposes a novel technique to conduct back‐analysis of lateral displacement of deep cement mixing (DCM) columns in deep excavation construction. For the first time, we propose a process to investigate both soil and underground structure end‐to‐end automatically. The novel technique is a complex combination of three crucial factors: (1) a nature‐inspired optimization algorithm (O), (2) a three‐dimensional PLAXIS Geotechnical Engineering software (P) and (3) a modern Python language programming (P), hereinafter called the a nature‐inspired optimization algorithm (O), a three‐dimensional PLAXIS Geotechnical Engineering software (P) and a modern Python language programming (P) (OPP) technique. The novel meta‐heuristic algorithm simulated the co‐evolved partnership behavior of shrimps and goby fishes, termed Shrimp and Goby Association (SGA), which plays an important role in complex analyses. A series of exams to determine the SGA's performance is conducted on 38 benchmark test functions (IEEE Congress on Evolutionary Computation 2017 and 2019) and three real‐world engineering design problems to showcase its applicability. The metaheuristic and PLAXIS 3D analysis work well together, which makes the back‐analysis technique powerfully to determinate stiffness parameters instead of the traditional approaches. Based on the optimized parameters, the lateral deflection of DCM and soil are well predicted for excavation. This study proposes a technique to estimate efficiently the stiffness parameter for very soft soil. As a consequence of the optimization process, an equation to determine the stiffness parameter of DCM columns from laboratory test is also proposed. Based on the obtained results, this research provides a comprehensive methodology for predicting risk, enhancing safety, saving time and money, and effectively designing and constructing underground structures. [ABSTRACT FROM AUTHOR]

Time‐delayed systems frequently appear, especially in sectors such as fluid flow processes, chemical procedures, and the food industry. This paper addresses the optimization of parameters for a fractional order PID (FOPID) controller, which is used to control a time‐delayed system, using five distinct algorithms inspired by nature. These algorithms are NewBAT, Cuckoo search (CS), Firefly (FF), Gray Wolf Optimizer (GWO), and Whale optimization algorithm (WOA). The FOPID controller parameters, namely KP, KI, KD, λ and μ, have been optimized using these algorithms. During the optimization process, the integral of the time absolute error (ITAE) was considered as the primary measurement criterion. In addition to this value, the maximum overshoot, settling time, time to reach the maximum value, and error values were examined. Simulations conducted with the obtained parameters tested the system's resilience to disturbances introduced at the output, and the controller responses were also evaluated during these tests. The reactions of the determined parameters to different reference inputs were analyzed, and the results are presented in graphs and tables. The efficiency and reliability of the optimization algorithms were substantiated by comprehensive statistical analyses. These analyses play a critical role in algorithm selection and objective evaluation of the results. Simulation studies were conducted in the Matlab and Simulink environments. The FOMCON Toolbox was used for fractional‐order processes. [ABSTRACT FROM AUTHOR]

This paper addresses optimization problems subject to an objective function's evaluation‐number constraint arbitrarily given in advance. These problems are practical for black‐box objective functions which cost much money and/or time per evaluation because money and/or time constraints are typically applicable in real‐world projects. This paper suggests a new solution strategy that can be used to adapt metaheuristics to evaluation‐number constraints arbitrarily given in advance. The proposed strategy updates the setting parameters of a target metaheuristic algorithm for every generation by solving a fixed small‐size optimization problem related to the evaluation‐number constraint. It has the advantage of being insensitive to the effects of dimensionality increases in principle. The effectiveness of the proposed strategy is confirmed by numerical experiments with two well‐known metaheuristics, i.e., the particle swarm optimization algorithm and the differential evolution algorithm. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Summary: The efficiency of metaheuristic algorithms depends significantly on the number of fitness value evaluations performed on candidate solutions. In addition to various intelligent techniques used to obtain better results, parallelization of calculations can substantially improve the solutions in cases where the problem is NP‐hard and requires many evaluations. This study proposes a new parallel tabu search method for solving the Maximum Vertex Weight Clique Problem (MVWCP) on the Non‐Uniform Memory Access (NUMA) architectures using the OpenMP parallel programming paradigm. Achieving scalability in the NUMA architectures presents significant challenges due to the high complexity of their memory systems, which can lead to performance loss. However, our proposed Tabu‐NUMA algorithm provides up to 18×$$ 18\times $$ speed‐up with 64 cores for ten basic problem instances in DIMACS‐W and BHOSLIB‐W benchmarks. And it improves the performance of the serial Multi Neighborhood Tabu Search (MN/TS) algorithm for 38 problem instances in DIMACS‐W and BHOSLIB‐W benchmarks. We further evaluate our algorithm on larger datasets with thousands of edges and vertices from Network Data Repository benchmark problem instances, and we report significant improvements in terms of speed up. Our results confirm that the Tabu‐NUMA algorithm is among the best recent algorithms for solving MVWCP on the NUMA architectures. [ABSTRACT FROM AUTHOR]

Summary: This article presents a study on the placement of multiple nulls as well as minimization of side lobe level in the radiation pattern using a planar hexagonal antenna array structure in two different vertical planes. The desired null depth is achieved to suppress the interference signal by the position‐only control of the uniformly excited isotropic antennas in the array structure. The immediate solution to the mentioned computational problem is reached by various meta‐heuristic optimization algorithms such as teaching learning‐based optimization (TLBO), symbiotic organism search (SOS), and moth fly optimization (MFO) within a considerably reduced processing time with a control over the design constrains. Various examples for diversified scenarios are demonstrated to place multiple deep nulls in the radiation pattern without compromising the pattern constraints and all other radiation pattern characteristics. This experiment sought to illustrate and quantify the unique benefits and limitations of proposed technique using three considered meta‐heuristic optimization algorithm. [ABSTRACT FROM AUTHOR]

From an engineering point of view, non‐linear systems are essential to the operation of control systems, because all systems actually have a non‐linear state in nature. In reality, there are many different kinds of non‐linear systems hidden by this negative definition. For successful analysis and control, the identification of non‐linear systems using unknown models is typically necessary. Till now, numerous approaches are developed for identifying non‐linear systems, but it cannot be employed with a large number of components. Moreover, system identification is typically restricted to output and input signals alone, also such systems are rarely used in reality. This is the primary justification for using non‐linear systems in this research. So, this research proposed a non‐linear model of system identification for large‐scale systems under the consideration of two systems: bilinear system and Volterra system. Therefore, a novel algorithm named Self Adaptive Penguin Search Optimization (SAPeSO) is introduced to attain the system characteristics properly and minimize the output variation. Finally, the effectiveness of the proposed work is compared with existing works in terms of various error measures. This research mainly focuses on the application‐oriented engineering problems. In particular, the Mean Absolute Error (MAE) of the proposed work for the Volterra system at 4000 samples is 18.83%, 14.05%, 8.88%, 29.72%, 19.91%, and 6.70% which is better than the existing bald eagle search (BES), arithmetic optimization algorithm (AOA), whale optimization algorithm (WOA), nonlinear autoregressive moving average with exogenous inputs‐ frequency response function + principal component analysis (NARMAX‐FRF+PCA), Global Gravitational Search Algorithm‐Assisted Kalman Filter (CGS‐KF), and sparse regression and separable least squares method (SR‐SLSM) methods, respectively. Finally, the error is minimum for the proposed model when compared with the other traditional approaches. [ABSTRACT FROM AUTHOR]

Summary: The propagation of bias and its data have sensitive and critical security enterprises, which emphasizing the significance of perfecting state‐of‐the‐art intrusion discovery systems. These days malware pitfalls have turned precipitously vigorous, intricate and independently on this bias and its data. The artificial intelligence approaches have turned into the focus for cybersecurity as these are noticed acting as more applicable for dealing with contemporary malware events. Particularly, the neural networks having its robust conception prosecution which capability can challenge an expansive range of cyber pitfalls. This article portrays the advancement and testing of a neural network for bracket procedures. Also, the point election could be regarded as a combinatorial optimization issue. It also includes a new point selection fashion known as the Mayfly metaheuristic algorithm, which incorporates an S‐shaped transfer function for converting it into a double variant of the Mayfly Algorithm. While distinct seeker results were acquired out of different areas of the hunt space employing the Mayfly Algorithm, a lesser result could be assured. also, there are two bracket networks like piled BiLSTM and CNN LSTM that are regarded for the UNSW‐NB15 dataset, which automatically learn features out of the undressed data for landing the vicious train structure patterns and law sequence patterns. This largely lessens the price of artificial features engineering. The proffered bracket networks are examined employing the criteria like accuracy, precision, recall, F1‐score, and AUC score under binary and multi classes. It is observed that the proffered bracket networks achieve 91.87 of accuracy, 92.769 of precision, 90.47 of recall, and 91.6 of F1‐ score. [ABSTRACT FROM AUTHOR]

The regulation of the frequency and line power flow in interconnected power networks is considered to be a key aspect of load frequency control (LFC). This article broaches a modern power network composed of three interconnected control areas including traditional generation units taking into account non‐linearities, also renewable energy sources (RESs) and energy storage (ES) units are involved in the power grid paradigm. Two forms of RESs are included in the analysis, which are photovoltaic (PV) and wind power plants. In addition, the study framework involves three types of ES units, which are batteries of plug‐in electric vehicles (PEVs), flywheel energy storage system (FESS) and capacitive energy storage system (CESS). In this analysis, LFC is accomplished by the use of proportional‐integral‐derivative (PID) controllers in the system control loops. A recent optimization algorithm called Manta Ray Foraging optimization (MRFO) is employed to obtain the optimal gain configuration of the controllers. Real site measurements are imported to the RESs involved in the study aiming to examine the proposed control scheme under realistic conditions. Compared with other rival algorithms, the effectiveness of the MRFO‐based PID controller is validated. Simulation results confirm the efficacy of the proposed control scheme. The findings also ensure the role of ES units in optimizing the time‐domain responses. The main contributions of this paper are applying a new metaheuristic optimization algorithm to solve the LFC problem and introducing a new criteria for judging the system performance in compliance with the harmonic spectrum of the responses in the frequency domain. The results of the simulation are retrieved through a MATLAB model. [ABSTRACT FROM AUTHOR]

The generation of power with load optimization, particularly in the current deregulated electricity market conditions, is a very important process for improved planning and operation of the grid. In addition, it is very important for the system not to experience problems due to congestion, have tensile stability, and protection to increase the share of electricity from renewable sources with the current supply system. This article presents load balancing with the butterfly optimization algorithm (BOA) in a hybridized form to minimize and maximize loads when used in pool and hybrid markets. The methods have been designed to prevent the drawbacks of BOA and generate a better trade‐off between exploration and exploitation abilities by hybridizing it with particle swarm optimization (PSO) and gray wolf optimizer (GWO). Empirical research on other algorithms shows that proposed hybrid BOA‐GWO‐PSO algorithm performs better and shows potential in diverse problems. These studies give it a significant advantage over BOA in general, and when it is employed to solve complex optimization problems validated on benchmark IEEE 30 bus system. A comparative analysis has been conducted to validate the potency of the hybrid BOA‐GWO‐PSO approach with some conventional meta‐heuristic algorithms. Analysis of results by mathematical validation on 23 benchmark functions and application in congestion management by optimal reactive power management (RPM) reveal that the proposed technique has the potent to solve real world optimization problems and is competitive with recent methods reported in state‐of‐ art literature. [ABSTRACT FROM AUTHOR]

The demand for novel and innovative control techniques has increased due to complexity in the process control industries which expect better control with enhanced performance. The optimization of proportional–integral–derivative (PID) controller parameters plays a significant role in the petrochemical and biochemical industries. In all these industries, controlling of continuous stirred tank reactors (CSTRs) is highly deployed whose dynamic characteristics are non‐linear and difficult to control as there is a risk of its temperature deviating from the set point. To have an efficient control, a metaheuristic optimization technique‐based PID, fractional‐order PID controller (FOPID) control evolved using the behaviour of a swarm of birds named coot bird optimization algorithm (CBOA) has been highlighted in this paper. The tuning has been done using the weighted combination of fitness functions, specifically the overshoot (Os) and the settling time (Ts). The simulated results demonstrate the improvement of the time domain response parameters of CBOA tuned PID, FOPID controllers providing an optimum performance when compared to other optimization algorithms and a conventional controller. [ABSTRACT FROM AUTHOR]

This article demonstrates the design and implementation of a stable wideband first‐order microwave integrator. The proposed design is attained by the cascading of eight equal‐length serial transmission lines. The meta‐heuristic optimization algorithms namely real‐coded genetic algorithm, particle swarm optimization, and sine–cosine algorithm are applied to attain the characteristic impedance of serial lines. Further, the proposed integrator, fabricated on RT/Duroid 5880 substrate with permittivity (ϵr ${\epsilon }_{r}$) = 2.2 and height (t) $(t)$ = 0.762 mm. The results are measured using a vector network analyzer, show its conformity with the simulated and ideal result within the frequency range of 4–18 GHz in MATLAB environment, and confirm its eligibility for Ku‐band application, which is compact in size. [ABSTRACT FROM AUTHOR]

Due to the issue of environmental protection coupled with high energy demand, there was an initiation for exploration of different renewable energy sources. This article aims to optimize the total annual cost of hybrids of wind and solar renewable energy system to satisfy the predesigned load. Minimization of the total annual cost of the system by determining appropriate numbers of the components, so that the desired load can be economically and reliably satisfied under the given constraints. Gravitational Search Algorithm (GSA) was employed for the optimization process. GSA is a recently proposed metaheuristic algorithm which is based on Newton's universal gravitational law of gravity and mass interactions. It uses stochastic rules to escape local optima and find the global optimal solutions. MATLAB codes were designed for the developed fitness function and employed algorithm. The proposed methodology was run for the fitness function through the code and the results were discussed. The result was compared with the results of Particle Swarm Optimization (PSO) and also shown that: GSA has some advantage over PSO algorithm. Even though, the algorithm has several parameters to be adjusted, it is strong in both local and global optimal searches. [ABSTRACT FROM AUTHOR]

Summary: Convolutional neural networks (CNN) are deep learning models widely reported as performing well in several image classification tasks. Training the networks for optimal performance is considered an NP‐hard problem. The combination of a metaheuristic algorithm with CNN has been proposed to address this problem because the metaheuristic algorithms have efficient performance, parameter tuning, and stagnation prevention methods. Moreover, there is now an increasing need to apply the best performing metaheuristic algorithm to optimize the parameters, training and learning rate of the CNN models. This study aims to investigate the best performing metaheuristic algorithm for fine‐tuning the weights, biases, and hyperparameters of CNN networks for solving the problem of characterization of abnormalities in breast images. Furthermore, hybrid models consisting of a CNN architecture and five representative metaheuristic algorithms to efficiently detect breast cancer abnormalities are presented. The adopted approach involves training a CNN network using genetic algorithm (GA), whale optimization algorithm (WOA), multiverse optimizer (MVO), satin bower optimization (SBO), and life choice‐based optimization (LCBO) algorithms to optimize only weights and bias of the model. Two categories of experiments were carried out: the first involved training of the CNN model, while the second involved optimizing the training of CNN model with metaheuristic algorithms. A comparative analysis of the impact of the proposed GA, WOA, MVO, SBO, and LCBO metaheuristic algorithms on the performance of the CNN architecture was carried out. Empirical and statistical analyses are presented to validate further the findings obtained in the study. Results obtained showed that MVO, SBO and LCBO outperformed GA, WOA. The classification accuracy obtained for CNN‐GA, CNN‐WOA, CNN‐MVO, CNN‐SBO, and CNN‐LCBO at the fifth epoch was 0.76, 0.75, 0.84, 0.80, and 0.86, respectively. At the same time, the traditional CNN achieved an accuracy of 0.66. The outcome of this study showed that contrary to widespread practice, physics‐based, biology‐based, and human‐based optimization algorithms promise better performance parameter tuning for CNN as compared with evolutionary‐based and swarm‐based metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

As distribution networks operate in their majority in a radial way, this condition becomes one of the most important constraints of distribution network reconfiguration (DNR) problem. There are diverse ways to represent this in DNR, but a lack of studies that provide an analysis of its behavior is noted, specially regarding its influence in metaheuristics and bio‐inspired metaheuristics. This paper presents a discussion between two alternatives to represent this in DNR in conjunction with three new bio‐inspired metaheuristics. One is based on the formation of the incidence matrix of the system and its determinant; the other is a modified approach of a method that establishes a set of forbidden switches through the analysis of the system fundamental loops. To solve DNR, three new bio‐inspired metaheuristics are presented: monarch butterfly optimization (MBO), gray wolf optimizer (GWO), and marine predators algorithm (MPA). A comparison is performed with selective particle swarm optimization (SPSO). To test the alternatives, three systems are used (33‐, 69‐, and 84‐ bus), with results showing the influence of the radiality approach in the results of bio‐inspired metaheuristics. [ABSTRACT FROM AUTHOR]

Recent dazzling progress in computer science and stringent requirements of the design of "more optimized" buildings put forwards the study and research of the various optimization methods in the building sector. This study presents a comprehensive review of the state‐of‐the‐art and the most applicable algorithms applying computational optimization to Steel buildings and different steel design problems. Four different algorithms including Neural Network, Bee and Bat‐Inspired algorithm and Metaheuristic Optimization algorithm have been studied with emphasized on the application of these methods in steel structures. Algorithm Key points are reviewed with more details including configuration of the algorithms and control of building systems, besides particular attention on accuracy and capability of them to be converged as an optimized solution. From different convergence rate, number of unknown parameters and boundaries of solutions It can be concluded that in near future researches should be oriented towards improving the efficiency of the most effective search techniques and realistic approximation methods especially in large‐scale building optimization problems; and reducing time with increasing accuracy for such activities. Further effort should be required to quantify the robustness in optimal answers so as to improve building performance. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a metaheuristic‐based optimization methodology for optimum design of axially symmetric cylindrical reinforced concrete walls that are a type of thin shells. In the methodology, recently proposed metaheuristic algorithm called Jaya algorithm is conducted with well‐known method called flexibility theory. The thickness of the wall is defined as a design variable of the optimization problem and the total material cost, including concrete and steel bars, is the objective function. Reinforced concrete design is done according to rules of regulation ACI 318‐Building code requirements for structural concrete. The proposed method is performed for different numerical cases of design constants to investigate effects of the wall height, dome height and thickness, and specific weight of stored liquid on optimum parameters for different support conditions. The investigation includes the optimum design of axially symmetric cylindrical reinforced concrete walls with and without domes. Analyzes were done for a total of 13,650 different situations, and various expressions such as linear, parabolic, and exponential were also proposed to assist engineers in their practical applications. According to the results of the analysis, it has been revealed that each investigated case has an effect on the optimum results and the proposed equations are successful in finding values quite close to the optimum design. [ABSTRACT FROM AUTHOR]

Summary: There exist numerous high‐dimensional problems in the real world which cannot be solved through the common traditional methods. The metaheuristic algorithms have been developed as successful techniques for solving a variety of complex and difficult optimization problems. Notwithstanding their advantages, these algorithms may turn out to have weak points such as lower population diversity and lower convergence rate when facing complex high‐dimensional problems. An appropriate approach to solve such problems is to apply multi‐agent systems (MASs) along with the metaheuristic algorithms. The present paper proposes a new approach based on the MASs and the concept of agent, which is named MAS as Metaheuristic (MAMH) method. In the proposed method, several basic and powerful metaheuristic algorithms are considered as separate agents, each of which sought to achieve its own goals while competing and cooperating with others to achieve the common goals. Altogether, the proposed method was tested on 32 complex benchmark functions, the results of which indicated the effectiveness and powerfulness of the proposed method for solving high‐dimensional optimization problems. In addition, in this paper, the binary version of the proposed method, called Binary MAMH (BMAMH), was implemented on the email spam detection. According to the results, the proposed method exhibited a higher degree of precision in the detection of spam emails compared to other metaheuristic algorithms and methods. [ABSTRACT FROM AUTHOR]

Feature selection (FS) in data mining is one of the most challenging and most important activities in pattern recognition. In this article, a new hybrid model of whale optimization algorithm (WOA) and flower pollination algorithm (FPA) is presented for the problem of FS based on the concept of opposition‐based learning (OBL) which name is HWOAFPA. The procedure is that the WOA is run first and at the same time during the run, the WOA population is changed by the OBL. And, to increase the accuracy and speed of convergence, it is used as the initial population of FPA. To evaluate the performance of the proposed method, experiments were carried out in two steps. The experiments were performed on 10 datasets from the UCI data repository and Email spam detection datasets. The results obtained from the first step showed that the proposed method was more successful in terms of the average size of selection and classification accuracy than other basic metaheuristic algorithms. In addition, the results from the second step showed that the proposed method which was a run on the Email spam dataset performed much more accurately than other similar algorithms in terms of accuracy of Email spam detection. [ABSTRACT FROM AUTHOR]

The optimal operation of fuel cells in changing environmental and variable load conditions requires mathematical modeling. The electrochemical behavior of polymer electrolyte membrane fuel cells (PEMFC) is commonly described with a semi‐empirical model requiring fuel cell specific model parameter values. A large number of different nature inspired, heuristic optimization methods have been proposed for this PEMFC parameter estimation problem. In this study, those studies are listed and critically reviewed. In particular, the aim is to elaborate the generalization ability of the results and discuss the fair comparison of the algorithms used for the parameter estimation of the polarization curve. The observations made in this review could further increase the quality of future contributions in this particular area, as well as applications of heuristic optimization methods in other related problems in fuel cell systems. [ABSTRACT FROM AUTHOR]

Spotted hyena optimizer (SHO) is a recently developed swarm‐based algorithm in the field of metaheuristic research, for solving realistic engineering design constraint and unconstrained difficulties. To resolve complicated nonlinear physical world tasks, at times, SHO reveals deprived performance concerning to explorative strength. So, to enhance the explorative strength along with exploitation in the search region, an attempt has been made by proposing the enhanced version of classical SHO. The suggested method is designated as space transformation search (STS)‐SHO. In STS‐SHO, a new evolutionary technique named as STS technique has been incorporated with original SHO. The suggested method has been assessed by IEEE CEC 2017 benchmark problems. The efficacy of the said method has been proven by using standard measures such as given performance metrics in CEC 2017, complexity analysis, convergence analysis, and statistical implications. Further as real‐world application, the said algorithm has been applied to train pi‐sigma neural network by means of 13 benchmark datasets considered from UCI depository. From the article it can be concluded that the suggested method STS‐SHO is an effective and trustworthy algorithm, which has the ability to resolve real‐life optimization complications. [ABSTRACT FROM AUTHOR]

The objective of moveout parameter inversion is to derive sets of parameter models that can be used for moveout correction and stacking at each common midpoint location to increase the signal‐to‐noise ratio of the data and to provide insights into the kinematic characteristics of the data amongst other things. In this paper, we introduce a data‐driven user‐constrained optimization scheme that utilizes manual picks at a point on each reflector within a common midpoint gather to constrain the search space in which an optimization procedure can search for the optimal parameter sets at each reflection. The picks are used to create boundary curves which can be derived approximately via an optimization technique or analytically via the derivation of an analytical bounds function. In this paper, we derive analytical forms of bounds functions for four different moveout cases. These are normal moveout, non‐hyperbolic moveout, azimuthally dependent normal moveout and azimuthally dependent non‐hyperbolic moveout. The optimization procedure utilized here to search for the optimal moveout parameters is the particle swarm optimization technique. However, any metaheuristic optimization procedure could be modified to account for the constraints introduced in this paper. The technique is tested on two‐layer synthetic models based on three of the four moveout cases discussed in this paper. It is also applied to an elastic forward modelled synthetic model called the HESS model, and finally to real 2D land data from Alaska. The resultant stacks show a marked improvement in the signal‐to‐noise ratio compared to the raw stacks. The results for the normal moveout, non‐hyperbolic moveout and azimuthally dependent normal moveout tests suggest that the method is viable for said models. Results demonstrate that our method offers potential as an alternative to conventional parameter picking and inversion schemes, particularly for some cases where the number of parameters in the moveout approximation is 2 or greater. [ABSTRACT FROM AUTHOR]

This work introduces metaheuristic approaches for designing resilient and cost-effective multiprotocol label switching (MPLS) networks, a technology that is gaining prominent importance since most of the global data traffic is Internet traffic, and most internet protocol (IP) traffic within service provider backbones is being supported upon the IP/MPLS technology. Our approach is innovative because it integrates an overlay network design problem with the effective usage of traffic-engineering features of this technology. Due to the resulting complexity and a high level of technological detail, we decided to use metaheuristics to find solutions to prospective scenarios for two real-world applications. The best results were achieved using evolutionary algorithms and GRASP (Greedy Randomized Adaptive Search Procedure). The relative improvements for some of these scenarios are outstanding and reveal how using the protection mechanisms provided by newer technologies may advance efficiency standards more than legacy protection schemas. [ABSTRACT FROM AUTHOR]

For the past two decades, nature-inspired optimization algorithms have gained enormous popularity among the researchers. On the other hand, complex system reliability optimization problems, which are nonlinear programming problems in nature, are proved to be non-deterministic polynomial-time hard (NP-hard) from a computational point of view. In this work, few complex reliability optimization problems are solved by using a very recent nature-inspired metaheuristic called gray wolf optimizer (GWO) algorithm. GWO mimics the chasing, hunting, and the hierarchal behavior of gray wolves. The results obtained by GWO are compared with those of some recent and popular metaheuristic such as the cuckoo search algorithm, particle swarm optimization, ant colony optimization, and simulated annealing. This comparative study shows that the results obtained by GWO are either superior or competitive to the results that have been obtained by these well-known metaheuristic mentioned earlier. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Heat Exchanger Network (HEN) synthesis is an important field of study in process engineering. However, obtaining optimal HEN design is a complex task. When mathematically formulated, it may require sophisticated methods to achieve good solutions. The complexity increases even more for large-scale HEN. In this work, a hybrid meta-heuristic method is presented. A rather simple Simulated Annealing approach is used for the combinatorial level, while a strategy named rocket fireworks optimization is developed and applied to the continuous domain. An advantage over other approaches is that the algorithm was written in C++, which is free and faster when compared to many other languages. The developed method was able to provide the lowest costs solutions reported so far to six cases well studied in the literature. An important feature of the approach here proposed is that, differently from other approaches, it does not split HEN into smaller problems during the optimization. © 2016 American Institute of Chemical Engineers AIChE J, 63: 1582-1601, 2017 [ABSTRACT FROM AUTHOR]

Heat exchanger networks (HENs) are a useful method of reducing the energy costs and environmental impact of industrial processes. Therefore, many papers focusing on solving HEN design problems efficiently have been published in the last years and several techniques have been proposed for their solution. The high complexity of the problem has led to an increment in the number of papers that use metaheuristics to solve it. The main reason is the capability of metaheuristics to handle complex problems in a feasible computational time. Additionally, this approach can easily take into account investment costs, and it offers more flexibility in the definition of the objective function and problem restrictions. In this paper, we review the most relevant papers that use metaheuristic techniques to resolve the HEN design problem. We also propose a method for grouping the different analyzed metaheuristics. It is based on a series of characteristics that allows the correct characterization of the different approaches. [ABSTRACT FROM AUTHOR]

In recent years, the field of combinatorial optimization has witnessed a true tsunami of 'novel' metaheuristic methods, most of them based on a metaphor of some natural or man-made process. The behavior of virtually any species of insects, the flow of water, musicians playing together - it seems that no idea is too far-fetched to serve as inspiration to launch yet another metaheuristic. In this paper, we will argue that this line of research is threatening to lead the area of metaheuristics away from scientific rigor. We will examine the historical context that gave rise to the increasing use of metaphors as inspiration and justification for the development of new methods, discuss the reasons for the vulnerability of the metaheuristics field to this line of research, and point out its fallacies. At the same time, truly innovative research of high quality is being performed as well. We conclude the paper by discussing some of the properties of this research and by pointing out some of the most promising research avenues for the field of metaheuristics. [ABSTRACT FROM AUTHOR]

SUMMARY Cells assignment to switches plays an important role in the area of resource management in cellular network. Efficient homing of cells to switches can have a significant impact on handoff and cabling costs. This problem is an NP-hard problem, which cannot be solved by conventional optimization algorithms. It is therefore necessary to use metaheuristic methods to solve it. This research proposes a hybrid heuristic approach for further improving the quality of solutions. This approach applies API ant algorithm in combination with one of the best heuristics used to solve this problem: Tabu Search. A key element in the success of this hybrid approach is the use simultaneously an intensified research on several areas of research space. The implementation of these algorithms has been subject to extensive tests. In comparison with previous research, the proposed method is efficient, regarding quality of solutions provided for moderate-sized and large-sized cellular mobile networks. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The production behavior of farm households has been a topic of interest for a long time. We explore the optimal production strategies from a household's perspective under the assumption of separability between consumption and production. The data used for this study come from farm households located in five provinces of China. To identify the impacts of various parameters on farmers' production behavior, optimal solutions and the households' actual production behaviors are compared. Results show that most of the households overinvested on farm land, labor force, materials, and machinery. Compared to existing production efficiency estimates, households, regardless of size, and exhibit different degrees of nonseparability. The sensitivity analysis shows a similar result, but finds that large households become more efficient if they have a higher production coefficient. The optimized result obtained in this paper not only provides farmers with decision‐support information on efficient resource allocation, but also helps policymakers to formulate better agricultural policies. Recommendations for Resource Managers A household production model is constructed to assess whether production and consumption behaviors are different among Chinese farmers. A genetic algorithm that belongs to a metaheuristic cluster is applied to search for the optimal solution, given the parameters obtained from using a ridge regression model. The following implications could be recommended based on the findings of the paper: The goodness of the convergence of the algorithm helps to find the optimal rice production decision, when facing a large number of input variables.The evidence that can support the separability among farm household's production behavior which to some degree supports nonseparability.As some of the external parameters, such as nonagricultural income, as well as farm scale, are increased, the optimal solution shows the separability among some households.Unless farm size is increased substantially, there is a need to support farmers in production behavior so that they can be efficient and profitable rather than just subsistent. [ABSTRACT FROM AUTHOR]