Search

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]

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: 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]

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]

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]

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]

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]

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]

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]

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]