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This article investigates a 27-degree-of-freedom dynamic model of a full-scale railway vehicle. The controlling action is performed by developing a decentralized control structure with five independent Fraction Order Proportional Integral Derivative (FOPID) controllers three types of random track irregularities, vertical profile, lateral alignment, and cross-level. A novel metaheuristic optimization technique named hybrid Particle Swarm Optimization-Gray Wolf Optimization (hybrid PSO-GWO) is proposed to optimize active force for an active suspension system. The simulated results show that the hybrid metaheuristic algorithm outperforms with a significant reduction in vehicle vibration ensuring enhanced ride comfort of the car body. [ABSTRACT FROM AUTHOR]

This research presents a comprehensive comparative analysis of optimization techniques for achieving the optimal design of cellular beams. The incorporation of gaps within cellular beams reduces the weight of the beam and increases section height, resulting in the production of lighter and stronger sections. The Taguchi method is employed to fine-tune the parameters of the Grey Wolf Optimizer, enabling the achievement of a robust design. The performance of each algorithm is evaluated through three design examples, facilitating comprehensive comparisons among the seven algorithms. Moreover, the study encompasses the modeling and analysis of optimally designed cellular beams using finite element software. [ABSTRACT FROM AUTHOR]

This study is a first attempt to provide an effective tool for crack prediction in beam-type structures using the peridynamics (PD) theory and metaheuristic-based optimization. The norm of the difference between the response of the tested beam and that of the trial model (analyzed using PD), is minimized to find the position and depth of crack. Model calibration is discussed thoroughly. Five well-known metaheuristics as well as an upgraded charged system search algorithm are considered. Results reveal that the proposed procedure can effectively localize and detect the crack severity, even in noise contaminated cases. [ABSTRACT FROM AUTHOR]

Lightweight cellular structures are investigated intensively in additive manufacturing and require optimization strategies to distribute both patterns, cross-sections sizes and type, and even materials. There exist several parametric optimization techniques that permit not only obtaining lightweight structures but also ensuring maintaining the rigidity. Among these techniques, design of experiments is time-consuming, and the values of the optimal parameters are estimated based on their possible range rather than being determined precisely. An original scientific strategy is to use the Kriging swarm optimization technique to achieve the global optimum using a minimal amount of computer simulations. Therefore, the objective of this paper is to determine the optimal cross-sections of structures using bio-inspired "L-systems". These L-systems-based structures were created and distributed along the directions of the principal stress lines (PSLs), then mimicking material growth and distribution inside biological structures. Two PSLs directions were separately investigated for each load case; the first direction is used as a guide for the growth of L-systems and the second one serves for branch extensions and limitations. To do so, a metamodel is used upon particle swarm optimization technique (PSO) and several case studies are conducted to improve the stiffness-to-weight ratio while respecting the additive manufacturability. [ABSTRACT FROM AUTHOR]

Purpose: This work is dedicated to disk brake rotor optimization using parametric and topological optimizations considering three conflicting objectives: mass, temperature variation, and breaking time. The rotor had explicit equations modeled and the Multi-objective Lichtenberg Algorithm (MOLA), which is executable in Matlab®, performed a parametric optimization to find the general rotor design parameters. Several optimization techniques have been developed last few years, however, the ones that have presented better results are meta-heuristics associated with posteriori decision-making techniques. Thus, in this work, this powerful and recently created multi-objective meta-heuristic was applied. The MOLA found more than 3000 solutions and the TOPSIS was used for decision-making. Then, the rotor was designed in the SolidWorks® 3D software and the ANSYS software was applied to perform topological optimization, where more mass was removed and analysis regarding the work stresses was done. To the best author's knowledge, this is the first work to consider multi-objective parametric and topological optimization for this structure at the same time in the literature. A considerable mass reduction was obtained. It was possible to find a rotor weighing only 164.8 g with the lowest safety factor across the entire rotor equals 2.02. Therefore, a rotor optimized with reliable, lightweight, and that allows a low braking time was found. Also, these results show that the methodology used can be applied in other structures with complex parameterization. [ABSTRACT FROM AUTHOR]

Combinational logic circuits (CLCs) are basic building blocks of a system and optimization of these circuits in terms of reduced gates, transistors, or levels will lead to reduced area on chip, reduced power, and improved speed. Simulated annealing (SA) is a thermo-inspired metaheuristic used for solving various engineering and non-engineering problems. SA is also used for the realization and optimization of CLCs. Circuits with a large number of inputs and outputs require more generations for realization. Realization of the optimal circuit with fewer generations is desired as realization time increases with increase in the number of generations. In this paper, an attempt is made to realize circuits using population-based SA with fewer generations. SA with partitioning approach is proposed in this paper for circuits that could not be realized with fewer preset generations. To evaluate the performance of the proposed work, benchmark circuits from LGSynth'91 are considered, and it is observed that the success rate improved and realization time reduced with the proposed partitioning approach. During the evaluation, it is also observed that the gate count was reduced by 2.5–77.39% and the transistor count was reduced by 7.69–95.53% on using proposed work with fewer generations over circuits reported in the literature. [ABSTRACT FROM AUTHOR]

Engineering optimization typically involves a large number of nonlinear constraints; therefore, effective constraint handling techniques (CHTs) are sought for metaheuristic optimization algorithms. Modified repair-based CHT is proposed here for a multi-objective evolutionary algorithm based on decomposition (MOEA/D). This CHT is: (1) adaptive to the share of infeasible solutions in a population; (2) free of problem-specific heuristics that users typically need to provide for repair; and (3) without control parameters. Infeasible solutions with superior decomposition function value are repaired using information contained in the neighbourhoods of the current population. The approach is tested on four multi-objective problems: a common mathematical optimization benchmark problem, two truss optimization problems and a real-world structural design of a tanker ship. A few prominent CHTs and metaheuristic algorithms are used for comparison. With the proposed CHT, MOEA/D shows improved convergence speed and spread of the Pareto front, providing competitive results in comparison to the other algorithms. [ABSTRACT FROM AUTHOR]

Many optimization problems in engineering and industrial design applications can be formulated as optimization problems with highly nonlinear objectives, subject to multiple complex constraints. Solving such optimization problems requires sophisticated algorithms and optimization techniques. A major trend in recent years is the use of nature-inspired metaheustic algorithms (NIMA). Despite the popularity of nature-inspired metaheuristic algorithms, there are still some challenging issues and open problems to be resolved. Two main issues related to current NIMAs are: there are over 540 algorithms in the literature, and there is no unified framework to understand the search mechanisms of different algorithms. Therefore, this paper attempts to analyse some similarities and differences among different algorithms and then presents a generalized evolutionary metaheuristic (GEM) in an attempt to unify some of the existing algorithms. After a brief discussion of some insights into nature-inspired algorithms and some open problems, we propose a generalized evolutionary metaheuristic algorithm to unify more than 20 different algorithms so as to understand their main steps and search mechanisms. We then test the unified GEM using 15 test benchmarks to validate its performance. Finally, further research topics are briefly discussed. [ABSTRACT FROM AUTHOR]

In recent years, many studies have been used in feed-forward neural network (FNN) to develop decision-making systems. The primary objective is to get the least error by finding the best combination of control parameters. It has been observed that FNNs using meta-heuristics techniques always converges very quickly towards the optimal positions but suffers from slow searching speeds at later stages of generation. Due to slow convergence, it is a prevalent phenomenon that traditional optimization does not ensure to find global optima. As a result, it falls under local optima. Recently, another meta-heuristic optimization-based algorithm called sine cosine algorithms (SCA) was introduced to solve the aforementioned issues. The algorithm is fundamentally predicated on two trigonometric functions, one being sine and the other being cosine. However, like other traditional approaches, SCA has a tendency to be stuck in sub-optimal regions due to poor exploration and exploitation capabilities. This paper proposes an improved version of SCA named chaotic oppositional SCA (COSCA) by integrating with chaos theory and oppositional based learning into the SCA optimization process. It is an incipient training method employed to train an FNN. Three benchmark problems are used to examine the precision and performance of FNNs equipped with COSCA, COPSO, OSCA, SCA, PSO, and backpropagation. The experimental results showed that, relative to other meta-heuristic optimization techniques, the COSCA technique is able to improve performance. [ABSTRACT FROM AUTHOR]

This article presents a hybrid method based on analytical and metaheuristic optimization techniques for the optimal placement of distributed generations (DGs) and shunt capacitors (SCs) into radial distribution systems (RDSs) to minimize power loss. In the hybrid method, a meta-heuristic called the Salp Swarm Algorithm is utilized to determine the optimal locations of DGs and SCs, while an efficient analytical technique is applied to calculate the optimal sizes. To check the robustness of the proposed method, two standard IEEE systems are employed; IEEE 33-bus and 69-bus, and compared with existing optimization techniques. Also to confirm the ability of the proposed method on improving performance, it applied to an actual 94-bus Portuguese RDS. The obtained results show the feasibility of the proposed method in optimal allocating of the DG and SC into RDS. [ABSTRACT FROM AUTHOR]

Whale optimization algorithm (WOA) is a novel-innovative swarm-based meta-heuristic algorithm with excellent performance, but it may still be trapped into local extremum for troublesome problems. To this end, an improved multi-objective whale optimization algorithm (IMOWOA) is proposed to cover the shortages. Firstly, in the search stage of WOA, individual difference is considered to strengthen the exploration ability, and evolution operators are introduced to regenerate the stagnated population to prevent premature convergence. Next, the performance of IMOWOA is compared with MOWOA and other classical optimization algorithms, and a series of multi-objective test functions are used. The results on the convergence and diversity of Pareto front confirm that IMOWOA has better feasibility and competitiveness. Finally, integrated with the least squares support vector regression (LSSVR) model, IMOWOA is applied to the deterministic optimization of vehicle structural crashworthiness. The conclusion testified the efficiency of IMOWOA in the field of vehicle crashworthiness. [ABSTRACT FROM AUTHOR]

In this paper a practical power system planning and control strategy based on a new adaptive sine cosine arithmetic optimization algorithm (ASC_AOA) is proposed to enhance the technical performances of radial distribution (RD) systems. The main objective considered in this study is to optimize the location and the size of shunt compensators based STATCOM devices and multi distributed generation to reduce the total power losses, and to maximize the loading margin stability of practical RD systems. As well confirmed in many recent researches, the success of metaheuristic algorithms is related to the interactivity between intensification and diversification stages. In this study, an adaptive process based on sine and cosine functions is incorporated within the standard AOA to guide the search process toward the best solution. The particularity of the proposed variant (ASC_AOA) has been validated on many benchmark functions, and also applied for optimal automation control of multi DGs and shunt compensators based STATCOM Controllers to enhance the performances of various types of RD systems, such as the 33-bus, the 69-bus, and 85-bus. Obtained results are compared to many recent optimization methods. It is confirmed that the proposed variant achieves the best solution in all of the cases studies elaborated. The proposed variant seems to be a competitive technique and an alternative tool for solving various combinatorial planning and control problems of modern RD systems. [ABSTRACT FROM AUTHOR]

Power system network is growing day by day as per the increased demand due to technological development around the globe. The increased power demand needs optimized planning for all the sections of power system. Economic dispatch is one of the important areas in which new optimization techniques can add to the overall improvement in the power system planning. Recent technological improvement in microgrids results in economic and efficient operation considering both conventional and non-conventional methods of generation. This paper highlights the optimal scheduling requirement in the advanced power system control by introducing a new proposed metaheuristic hybrid approach, i.e., Chaotic Spotted Hyena Optimization (CSHO). The proposed approach improves and intensifies the conventional approach and the results obtained are compared with some well-known published approaches using standard data. The CSHO is implemented on an islanded microgrid having five distributed generators out of which three are conventional generators and two are renewable sources. Three objectives are tested on the microgrid-(1) Economic scheduling, (2) Environmental emission and (3) CEED for Microgrid and the results of proposed approach are tested in four scenarios and compared with the PSO, DE, SOS, GWO and WOA approaches. The result achieved from the proposed approach is remarkable over previous approaches and showing enhanced economic operation, which are verified and presented in different sections of paper. [ABSTRACT FROM AUTHOR]

Reconfiguration of PV array has emerged as a promising solution to improve the power under partial shading conditions (PSC). The three main reconfiguration techniques are the physical array relocation method, electrical array reconfiguration (EAR), and the switching matrix generation based on optimization. The physical relocation techniques are complicated as they require hard labor. In the case of EAR, the optimal design of the switching matrix is still challenging. Therefore, to overcome these issues, this paper proposes a recent metaheuristic technique of Adaptive-JAYA optimization for the optimum reconfiguration of the PV array. Adaptive-JAYA is chosen for its simplicity and reliability, which makes it consume less memory, reducing the burden on processors. The proposed approaches are applied on a 9 × 9 PV array under eight shading patterns and also on a 9 × 15 PV array to analyze the competency of the proposed approach on unsymmetrical shading. The MATLAB results are quantitatively analyzed, and a comprehensive comparison is performed with many existing reconfiguration techniques. It is proved that the proposed Adaptive-JAYA approach improves the output power by 26.12%, 22.20%, 7.61%, and 7.68% compared to the TCT for the four shading patterns, i.e. SW, LW, SN, and LN. [ABSTRACT FROM AUTHOR]

As the automated fiber placement (AFP) manufacturing technology is developed, curvilinear fiber path composite structures received extensive attention. Therefore, developing a design framework capable of optimizing such structures is a significant challenge for researchers and engineers in this domain. In this article, an open-source ABAQUS/MATLAB-based framework is developed for the bending-induced buckling design of variable-stiffness (VS) composite cylinders made using the AFP method. The framework is based on an interface between ABAQUS FE packages with MATLAB environment using Python scripting language. In this framework, the optimized fiber angle distribution of steered plies and associated bending-induced buckling load of its FE model is obtained by applying a meta-heuristic optimization algorithm. The developed Python script submits dimensions, angle distributions, as well as loading and boundary conditions to ABAQUS/CAE. This framework can be customized to meet industrial demands. To show such flexibility, different types of metaheuristic optimization algorithms and aspect ratios are applied, and the associated problems are optimized separately. In addition to the simplicity and versatility of the proposed framework, the results indicate the higher performance of a novel metaheuristic, the so-called Water Strider Algorithm (WSA). Moreover, this framework can be used for finite element modeling and analysis in the metamodeling step for composite cylinders with higher aspect ratios. [ABSTRACT FROM AUTHOR]

This article represents an application of the grey wolf optimisation (GWO) algorithm to solve the most optimistic combinatorial problems for optimal distribution network reconfiguration (DNR) and allocation of distributed generators (DGs) in a system. In this work, a metaheuristics algorithm is utilised to minimise the active power losses (APL) and enhance the voltage profile. Various scenarios were considered in this context to compare the performance of the proposed algorithm under voltage and current capacity constraints. Furthermore, a detailed validation via comparison of the results is being carried out with other methods from the exhaustive literature. The proposed algorithm reduces the APL by 63.13%, 56.19%, and 34.27% with DNR in IEEE 33, 69 and 118-bus systems. Similarly, APL reduction by 69.61%, 82.09%, and 36.08% with DNR considering DGs simultaneously. The results show that the proposed algorithm is an effective and promising method to solve problems similar to this work. [ABSTRACT FROM AUTHOR]

One of the salient features of real-world networks such as social networks is the existence of community structures. Because of the importance of groups and communities in social networks, various algorithms have been proposed to identify communities in this type of dynamic networks. In this paper, we present a new approach to community recognition in dynamic social networks, which is multi-objective and metaheuristic. Our approach is to improve the Grey Wolf Optimizer algorithm and the Label Propagation algorithm and to combine the two algorithms for better performance. We performed our experiments on two artifi- cial and real datasets, and the results show that our proposed method performs better compared to present algorithms in terms of both quality and detection speed. We also applied our proposed algorithm to 23 base functions, which performed better than the other metaheuristic algorithms. At the end, the performance of our proposed algorithm is compared to six other clustering methods on nine datasets from the UCI machine learning laboratory. The simulation results show the effectiveness of the proposed algorithm for solving data clustering problems. [ABSTRACT FROM AUTHOR]

The optimization problems are becoming more complicated, requiring new and efficient optimization techniques to solve them. Many bio-inspired meta-heuristic algorithms have emerged in the last decade to solve these complex problems as most of these algorithms may be trapped into local optima and could not effectively solve all types of optimization problems. Hence, researchers are still trying to develop new and better optimization algorithms. This paper introduces a novel biologically-based optimization algorithm called circulatory system-based optimization (CSBO). CSBO is modeled based on the function of the body's blood vessels with two distinctive circuits, i.e. pulmonary and systemic circuits. The proposed CSBO algorithm is tested on a wide variety of complex functions of the real world and validated with the standard meta-heuristic algorithms. The results indicate that the CSBO algorithm successfully achieves the optimal solutions and avoids local optima. Note that the source code of the CSBO algorithm is publicly available at . [ABSTRACT FROM AUTHOR]

Road accident is one of the important problems in the world which caused large number of deaths. In a road crash dataset, the fatal crash samples, often constitute very small proportion in comparison with non-fatal crash samples. Accurate prediction of fatal crashes, as a minority class, is one of the important challenges in such imbalanced sample distribution in the most of machine learning algorithms. This study introduced data leveling methods based on two metaheuristic optimization algorithms (biogeography-based optimization and invasive weed optimization) to obtain more balanced data. Then, three machine learning algorithms including decision tree, support vector machine (SVM) and k-nearest neighbor were applied for obtained balanced dataset. Performances of the prepared models were evaluated by improving the accuracy of the models in detecting the fatal crashes. It is found that data leveling methods of imbalanced dataset with metaheuristic algorithms improve the performance of crash prediction models in detecting fatal crashes especially in SVM algorithm up to 100% compared to previous studies. Also, results of sensitivity analysis on the developed model represented that head-on crashes, curved roads, and large type vehicles can increase the probability of fatal crashes up to 27.2%, 29%, and 36.8% at high posted speed limit, respectively. Also, two-vehicle crashes are much more likely to be involved in fatal crashes than single-vehicle crashes. [ABSTRACT FROM AUTHOR]

Flower pollination-based algorithm (FPA), inspired from the pollination process of flowers, is a population-based metaheuristic optimisation algorithm for solving real-world problems. This article introduces the feature of insectivorous plants in catching the pollinators (preys) with the solution procedure of FPA for avoiding sub-optimal traps and models the dental image segmentation problem as an optimisation problem. This enhanced FPA (EFPA) is then applied in solving the formulated multilevel segmentation problem of dental images. The proposed EFPA-based method optimises the threshold values for dental images by effectively exploring the problem space. The segmented images along with PSNR values of the proposed method are compared with FPA-based and OTSU-basedsegmentation methods for exhibiting its superior performance. [ABSTRACT FROM AUTHOR]

PSO is a remarkable tool for solving several optimisation problems, like global optimisation and many real-life problems. It generally explores global optimal solution via exploiting the particle – swarm's memory. Its limited properties on objective function's continuity along with the search space and its potentiality in adapting dynamic environment make the PSO an important meta-heuristic method. PSO has an inherent tendency of trapping at local optimum which affects the convergence prematurely, when trying to solve difficult problems. This work proposed a modified version of PSO called as FPSO, where frequency-wave-sound is employed to exit from any encountered local optimum; if it is not the optimal solution. This FPSO mimics the characteristics of the waves by using three parameters, namely amplitude, frequency and wavelength. FPSO is then compared and analysed with other renowned algorithms like conventional PSO, Grey Wolf Optimisation (GOW), Multi-Verse Optimiser (MVO), Moth-Flame Optimisation (SL-PSO), Sine Cosine Algorithm (PPSO) and Butterfly Optimisation Algorithm (BOA) on 23 bench marking test bed functions. The performance is evaluated using various measures including trajectory, search history, average fitness solution and best optimisation-solution. The obtained results show that the FPSO algorithm beats other metaheuristic algorithms and confirmed its better performance on 2-dimensional test functions. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic crisis has caused many difficulties worldwide. One of the most critical problems is that the emergency departments (EDs) have become overcrowded. Because this prob)lem can increase patients’ queue length and waiting times in EDs, this paper provides a mixed)integer non-linear mathematical model considering a preemptive M/M/C queueing system to solve the problem and optimize a benefit function concerning the number of servers and treatment rate. In this model, different patient priorities, which are modified according to Covid-19 patients, are considered. This model is then solved using an exact approach and a meta-heuristic algorithm, the grasshopper optimization algorithm, for two shifts of the ED of a hospital in order to consider non stationery arrival rate in Varamin, Iran. The results of both algorithms confirmed the effective)ness of the proposed model. Moreover, to justify using the preemptive model, a comparison between the preemptive and non-preemptive models is conducted. An extensive sensitivity ana)lysis is presented, and finally, a list of managerial insights is provided for managers to improve their service system further. [ABSTRACT FROM AUTHOR]

This study examines the capability of various optimization algorithms and proposes novel hybrid algorithms for more precise prediction of open-edge cracks in cantilever beams. The natural frequencies of the beam with a crack are obtained by modal analysis and experimentally validated by impact testing. The performance of Harris hawk optimization (HHO), electrostatic discharge algorithm (ESDA), pathfinder algorithm (PFA) and Henry gas solubility optimization (HGSO) algorithms from the literature is evaluated to determine the location and depth of an open-edge crack for an Euler–Bernoulli beam. Then, hybrid algorithms (HHO-NM, ESDA-NM and PF-NM) are proposed to improve the results of the aforementioned algorithms. Simulation results show that the proposed hybrid algorithms yield much more precise results with fewer function evaluations than the previously introduced algorithms and, therefore, have superior crack detection capability. Statistical post hoc analysis shows that the proposed hybrid algorithm can be considered a high-performance algorithm, which can significantly improve the efficiency of crack detection applications. [ABSTRACT FROM AUTHOR]

CO2 emission reduction in large-scale pavement network maintenance planning has been an immense concern. The conventional single-objective optimisation modelsoverlook environmental issues such as CO2 emission. However, the introduced multi-objective optimisation aims to enhance the network condition and minimise CO2 emissions simultaneously. Two single-objective (coyote optimisation algorithm and genetic algorithm) and two multi-objective metaheuristic algorithms (multi-objective coyote optimisation algorithm and non-dominated sorting genetic algorithm) are employed to assess the effectiveness of the introduced environmental approach. Pavement maintenance planning optimisation requires the deterioration function formula and treatment improvement equation to be modelled. Hence, a new machine learning method called 'differential evolutionary programming' is introduced, which can provide the output-input formula. Differential evolutionary programming predicts the pavement deterioration value and overlay improvement with R2 of 0.992 and 0.970, respectively. The results indicate that the coyote optimisation algorithm's objective function is 66% lower than that of the genetic algorithm. Likewise, the multi-objective coyote optimisation algorithm reduces the first objective function by 72% on average compared to the non-dominated sorting genetic algorithm. The grey relational analysis is performed to compare single-objective and multi-objective optimal solutions. All optimal solutions presented by multi-objective modelling dominates the single-objective optimisation optimal solution based on the grey relational grade. [ABSTRACT FROM AUTHOR]

The profit-based unit commitment is a maximisation problem, which consists of the revenue minus costs subject to related constraints. However, with increasing concerns for the global warming, environmental limitations must also be observed. The objective of this study is to develop a novel heuristic optimisation algorithm for solving profit-based unit commitment, considering selling reserve, satisfying demand, and environmental emissions. Without emission limitations, the results show profits between 0.02% and 17.60% for profit-based unit commitment, as compared with other algorithms reported in the literature. As far as emission limitations are considered, the profits of 1.65–118.71% and emission cost reduction of 2.16–7.79% are achieved, as compared with those found in literature. [ABSTRACT FROM AUTHOR]

A prominent and realistic problem in magnetics is the optimal design of a brushless direct current (BLDC) motor. A key challenge is designing a BLDC motor to function efficiently with a minimum cost of materials to achieve maximum efficiency. Recently, a new metaheuristic optimization algorithm called the Coronavirus Herd Immunity Optimizer (CHIO) is reported for solving global optimization problems. The inspiration for this technique derives from the idea of herd immunity as a way of combating the coronavirus pandemic. A variant of CHIO called Multi-Objective Coronavirus Herd Immunity Optimizer (MOCHIO) is proposed in this paper, and it is applied to optimize the BLDC motor design optimization problem. A static penalty constraint handling is introduced to handle the constraints, and a fuzzy-based membership function has been introduced to find the best compromise results. The BLDC motor design problem has two main objectives: minimizing the motor mass and maximizing the efficiency with five constraints and five decision/design variables. First, MOCHIO is tested with benchmark functions and then applied to the BLDC motor design problem. The experimental results are compared with other competitors are presented to confirm the viability and dominance of the MOCHIO. Further, six performance metrics are calculated for all algorithms to assess the performances. [ABSTRACT FROM AUTHOR]

The changing weather conditions and Partial Shading Situation (PSS) create numerous challenges in harvesting available maximum power from the solar Photovoltaic (PV) systems. The limitations of classical and bio-inspired optimization-based Maximum Power Point Tracking (MPPT) methods are incapable of extracting maximum power under PSS. Therefore, this paper presents a Modified Seagull Optimization Algorithm (MSOA) based MPPT approach by incorporating Levy Flight Mechanism (LFM) and the formula for heat exchange in Thermal Exchange Optimization (TEO) in the original Seagull Optimization Algorithm (SOA) for accurate tracking of Global Maximum Power Point (GMPP) under transient and steady state operating conditions. The MSOA increases the capability of optimization in finding the optimal value of boost DC-DC converter's duty cycle, D, for operating at GMPP. The superiority of the presented MPPT approach is contrasted with SOA MPPT under uniform irradiation situation and partial shading situations using Matlab Simulink platform. With the presented MSOA MPPT, the settling time and percentage maximum overshoot are reduced by 0.92 times and 0.55 times in comparison to SOA MPPT with increased efficiency. The hardware results validated the simulation results proving the proposed MSOA MPPT as an efficient MPPT for solar PV systems. [ABSTRACT FROM AUTHOR]

The removal of irrelevant and insignificant features from the high-dimensional dataset is a necessary prerequisite for the exploration of information. Meta-heuristic optimization techniques have been widely used in the field of knowledge discovery over the last few years. The whale optimization algorithm (WOA) is a swarm-based metaheuristic technique that is often used in the field of dimensionality reduction. Among the various WOA-based feature selection techniques in the literature, not a single technology illuminates the stability issue of WOA. Stability is often identified as a sensitivity to the disruption of input data during the process of selecting significant features. In this study, a new feature selection model based on improved WOA (iWOA) is proposed to select significant features from a high-dimensional microarray dataset. The stability of the results obtained is evaluated with the existing stability index that satisfies all the required characteristics of the stability measure. In addition, the results of the proposed model are compared with other contemporary meta-heuristics techniques. The proposed iWOA proposes its identification as a well-stable feature selection technique according to the strength of the stability index agreement. [ABSTRACT FROM AUTHOR]

The present study investigates the capability of two metaheuristic optimization approaches, namely the Butterfly Optimization Algorithm (BOA) and the Genetic Algorithm (GA), integrated with machine learning models in Suspended Sediment Load (SSL) prediction. The Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), and Multiple Linear Regression (MLR) are applied as the predictive data-driven models. Independent input variables, i.e., the water temperature (T), river discharge (Q), and specific conductance (SC) are used for the prediction of SSL based on several statistical indices. The results indicate that the performances of all studied models were close to one another; moreover, the metaheuristic algorithms were found to increase the accuracy of the ANFIS and ANN models for approximately 11.73 percent and 4.30 percent, respectively. In general, the BOA outperformed the GA in enhancing the optimization performance of the learning process in the applied machine learning models. [ABSTRACT FROM AUTHOR]

A novel method for system identification of small-scale fixed-wing Unmanned Aerial Vehicles (UAVs) using a metaheuristics (MHs) approach is pro- posed. This investigation splits the complex aerodynamic model of UAV into long- itudinal and lateral dynamics sub-systems. The system identification optimisation problem is proposed to find the UAV aerodynamic and stability derivatives by minimizing the R-squared error between the measurement data and the flight dynamic model. Thirteen popular optimisation algorithms are applied for solving the proposed UAV system identification optimisation problem while each algorithm is tested for 10 independent optimisation runs. By performing the Freidman’s rank test, statistical analysis of the experiment work was carried out while, based on the fitness value, each algorithm is ranked. The outcomes demonstrate the dominance of the L-SHADE algorithm, with mean R-square errors of 0.5465 and 0.0487 for longitudinal and lateral dynamics, respectively. It is considered superior to the other algorithms for this system identification problem. [ABSTRACT FROM AUTHOR]

Shipyards face tough problems such as ineffective production control and poor planning. Identifying appropriate work packages is one of the challenges of ship production planning. Work packages consist of a group of sub-works. The activities of block production that must be completed at each work station are referred to as sub-works. This study proposes a two-stage hierarchical model for assessing work packages in a balanced way. The paper focuses on providing a predetermined completion time rather than minimising the project's completion time. Work packages are determined within the two stages. The first stage is developed to identify the boundaries of the metaheuristic search. A metaheuristic search procedure is implemented in the second stage. The simulated annealing (SA) optimisation algorithm is the foundation of this procedure. The findings reveal a link between sub-work size and work package configuration. Accordingly, smaller-sized sub-works provide a more balanced work package configuration. [ABSTRACT FROM AUTHOR]

This paper introduces a new meta-heuristic algorithm called Thieves and Police Algorithm (TPA), for solving probabilistic optimization problems. The proposed algorithm is designed in five different phases each following a certain objective. In this algorithm, conventional matters between the thieves' team and the police are inspired as a social phenomenon. Motivation of thieves for stealing more properties is utilized in this algorithm to seek the optimum solution, and police track that result in fear and no risk taking of the thieves to enter areas under police surveillance is inspired to omit areas with weaker solutions. Algorithm optimization procedure ends with apprehending the leader thief by police as the optimum solution. The performance of the TPA is evaluated in terms of local optima avoidance, exploration, exploitation, effectiveness, and convergence properties using a set of classical and modern test functions. Comparing the outcomes of other meta-heuristic optimization algorithms, the successful performance of the TPA in solving different optimization problems is confirmed. Moreover, the efficiency of the TPA in terms of solving problems with probabilistic nature is assessed by solving an optimization problem in power engineering area, known as Probabilistic Power Flow (PPF), in a Micro-Grid with renewable energy resources. Based on the outcomes, the proficiency of the proposed algorithm is confirmed by solving the PPF problem and producing timely acceptable results. Hence, the TPA algorithm is suggested to solve complicated problems, with probabilistic nature, in the optimization field and other optimization problems. [ABSTRACT FROM AUTHOR]

Social group optimization (SGO) is a human-based metaheuristic optimization technique which shows accurate results for different benchmark functions but has not been studied for civil engineering structural health monitoring problems. This article deals with the use of SGO for damage analysis of different modelled civil engineering structures and a real-life American Society of Civil Engineers (ASCE) benchmark structure using a stiffness-based objective function. It is observed that SGO is not able to identify the damage in the structures owing to the algorithm becoming trapped in local optima. To improve the performance of SGO, a modified social group optimization (MSGO) is proposed, which deals with the drawbacks of SGO in dealing with the complicated objective functions of civil structures. It is observed that MSGO shows accurate damage detection capability, with errors of less than 1%, in the civil structures considered, even in the presence of noise. [ABSTRACT FROM AUTHOR]

An influential observation is any point that has a huge effect on the coefficients of a regression line fitting the data. The presence of such observations in the data set reduces the sensitivity and validity of the statistical analysis. In the literature there are many methods used for identifying influential observations. However, many of those methods are highly influenced by masking and swamping effects and require distributional assumptions. Especially in the presence of influential subsets most of these methods are insufficient to detect these observations. This study aims to develop a new diagnostic tool for identifying influential observations using the meta-heuristic binary particle swarm optimization algorithm. This proposed approach does not require any distributional assumptions and also not affected by masking and swamping effects as the known methods. The performance of the proposed method is analyzed via simulations and real data set applications. [ABSTRACT FROM AUTHOR]

Optimisation of automatic tool changer (ATC) indexing problem, where cutting tools are allocated to the stations on a turret magazine of a CNC machine, is one of the challenging problems in machining. The aim of the problem is to minimise the total indexing time of ATC. This problem becomes even more challenging if duplication of cutting tools is allowed and a bidirectional ATC is used. The problem has a unique feature which has not been stressed yet by other researchers, that is, although ATC indexing (master problem) is the main optimisation problem, objective function evaluation of this problem is a standalone optimisation problem (sub problem) indeed. Although an approximation algorithm does not guarantee optimality for the master problem, the subproblem must be solved optimally; otherwise, deficiencies arising from ill-defined objective function might be encountered. Considering this interesting future, a novel methodology, which employs a shortest path algorithm, is developed. Thus, the subproblem of this complicated problem can be optimally solved. Moreover, two metaheuristics, based on threshold accepting and descent first improvement greedy methodologies, are proposed for generating efficient solutions. Finally, several benchmarking instances are generated and solved to test the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper aims to provide a novel approach for improving urban land cover classification accuracy, which combines the Elephant Herding Optimization (EHO) algorithm as a meta-heuristic optimization method with the Random Forest (RF) classifier. The proposed approach involves both classifier hyperparameter tuning and feature selection for a data set of a selected urban area. The EHO and the RF algorithms were both utilized in a hybrid system (EHO-RF) to find an optimal classification model with a tuned set of hyperparameters and features (predictor variables). EHO-RF model tuning was followed by backward feature elimination using RF, which further reduced data dimensionality. This work also combines two well-known optimization and feature selection algorithms to enhance accuracy assessment of the proposed approach. Grid search optimization was combined with Variable Selection Using Random Forests (VSURF) algorithm to build an optimization system (Grid-VSURF) that is similar to the workflow of EHO-RF. Objective functions for both Grid-VSURF and EHO-RF were designed to perform 10-fold cross-validation to reduce over-fitting. An area in Deerfield Beach city, Florida, USA, was selected as the study area representing an urban land cover. The testing dataset used in this study represents a performance benchmarking dataset, which has been utilized in many studies. EHO-RF results showed a significant improvement with an overall map accuracy of 83.83%, compared with Grid-VSURF results with an overall map accuracy of 74.75%. The number of input features was significantly reduced by both approaches with 82.30% reduction using EHO-RF with backward elimination and 89.70% reduction using Grid-VSURF. [ABSTRACT FROM AUTHOR]

In this study, a hybrid metaheuristic algorithm called Hybrid Tabu Search (HTS) is proposed for the weight optimization of planar steel structures. The effectiveness of HTS lies in improving the initial conditions of a tabu search algorithm with a greedy search algorithm and a swap operator used in the phase of section selection. The effectiveness of the proposed method is tested and validated on three different benchmark examples. C♯ software is developed to perform the structural analyses. Numerical results obtained by using HTS showed that the proposed method achieved significantly lighter frame weights in most of the benchmark examples. When the method was examined in terms of computational efficiency, HTS was at a level that can compete with other algorithms examined within this study, although it did not provide a significant advantage. [ABSTRACT FROM AUTHOR]

Seagrass provides numerous valuable ecosystem services across a wide range of climatic regions. However, in terms of area and habitat, this resource is in decline globally and there is an urgent need for accurate mapping of extant meadows and biomass to support sustainable seagrass blue carbon conservation and management. This study develops a novel method for a binary mapping of seagrass distribution and estimating seagrass above-ground biomass (AGB) by applying a suite of advanced machine learning (ML) algorithms combined with and without a metaheuristic optimization approach (particle swarm optimization – PSO) to various combinations of multispectral (Sentinel-2) and synthetic aperture radar (Sentinel-1) remote sensing data. Our results reveal that the Sentinel-1 data has potential for the binary mapping of seagrass meadows using an extreme gradient boosting (XGB) model (scores of precision (P) = 0.82, recall (R) = 0.90, and F1 = 0.86) but is less effective at estimating AGB. The optimal method for estimation of AGB used both Sentinel-1 and Sentinel-2 imagery, the XGB model, and PSO optimization (coefficient of determination (R2) = 0.75, root mean squared error (RMSE) = 0.35, Akaike information criteria (AIC) = 24.80, Bayesian information criteria (BIC) = 44.70). Our findings contribute novel and advanced methods for seagrass detection and improvement of AGB estimation, which are fast and reliable, use open-source data and software and should be easily applicable to intertidal zones across many regions of the world. [ABSTRACT FROM AUTHOR]

This manuscript proposes a novel metaheuristic, which is an amalgamation of the quantum concept and the gravitational search particle swarm optimization technique. The inclusion of quantum concepts in the algorithm enhances its capability, as in quantum space there is no restriction on the movement of particle and the solution can be obtained with smaller population and faster convergence. An adaptive contraction expansion factor is also introduced which ensures better exploration of the algorithm. The technique is applied to solve the combined economic emission dispatch of a hybrid solar thermal unit operating in New Delhi, India for full and reduced solar radiation. To verify the effectiveness of the proposed technique, it is also applied to solve the combined economic emission dispatch of a 10 unit, 2000 MW system. It is observed that the proposed approach provides a fuel cost saving of up to 1300$ when compared with other referred techniques. [ABSTRACT FROM AUTHOR]

During the past decade, solving complex optimization problems with metaheuristic algorithms has received considerable attention among practitioners and researchers. Hence, many metaheuristic algorithms have been developed over the last years. Many of these algorithms are inspired by various phenomena of nature. Evolutionary intelligence is a research field that models the behaviour of insects or animals. Several algorithms arising from such models have been proposed to solve a wide range of complicated optimizable systems. In this paper, a novel evolutionary technique called Artificial Spider Algorithm (ASA) for solving optimization tasks in unconstrained problems with high nonlinearity is proposed. The ASA is based on the simulation of spider behaviour. For this purpose, a new metaphysical method according to spinning web and hunting insects via spider is inspired in nature. In order to illustrate the proficiency of the proposed approach, it is compared to other well-known evolutionary methods. The comparison investigates several test functions that are commonly considered within the literature of evolutionary algorithms. The result shows a high performance and effectiveness of this method for searching a global optimum, as well as the cost reduction noticeably for various benchmark functions. [ABSTRACT FROM AUTHOR]

Flow-shop sequence-dependent group scheduling (FSDGS) problem has been extensively investigated in the literature also due to many manufacturers who implemented the concept of group technology to reduce set-up costs, lead times, work-in-process inventory costs, and material handling costs. On the other hand, skilled workforce assignment (SWA) to machines of a given shop floor may represent a key issue for enhancing the performance of a manufacturing system. As the body of literature addressing the group scheduling problems ignored up to now the effect of human factor on the performance of serial manufacturing systems, the present paper moves in that direction. In particular, an M-machine flow-shop group scheduling problem with sequence-dependent set-up times integrated with the worker allocation issue has been studied with reference to the makespan minimization objective. First, a Mixed Integer Linear Programming model of the proposed problem is reported. Then, a well-known benchmark arisen from the literature is adopted to carry out an extensive comparison campaign among three properly developed metaheuristics based on a genetic algorithm framework. Once the best procedure among those tested is selected, it is compared with an effective optimization procedure recently proposed in the field of FSDGS problems, being this latter properly adapted to run the SWA issue. Finally, a further analysis dealing with the trade-off between manpower cost and makespan improvement is proposed. [ABSTRACT FROM AUTHOR]

U-shaped assembly lines are regarded as an efficient configuration in Just-In-Time manufacturing. Balancing the workload in these lines is an unsolved problem that attracted significant research within the past two decades. We present a novel integer programming formulation for U-shaped line balancing problems, where cycle time, the interval between two consecutive outputs, is known and the aim is to minimize the number of workstations. To enhance the efficiency of the LP relaxation of the new formulation, we present three types of logic cuts (assignable-station-cuts, task-assignment-cuts and knapsack-cuts) that exploit the inherent logic of the problem structure. The new formulation and logic cuts are tested on an extensive set of benchmark problems to provide a comparative analysis with the existing models in the literature. The results show that our novel formulation augmented by assignable-station-cuts is significantly better than the previous formulations. [ABSTRACT FROM AUTHOR]

Combined heat and power (CHP) generation is a competent configuration for simultaneous production of thermal and electric energy. The interdependency of heat and power outputs of CHP units presents complications and non-convexities in modelling and optimization. The nonlinear constrained economic dispatch optimization problem becomes more complex when one considers the pollution produced by generating units, transmission losses and valve-point effects of thermal units. This article uses the stochastic fractal search algorithm to solve the bi-objective combined heat and power economic dispatch (CHPED) problem. The CHPED problem has bounded feasible operating regions and many local minima. The compromise programming method is used to transform the two objective functions into an aggregated objective function. The results are compared with previous results obtained by many different methods reported in the literature. The results reveal that the proposed algorithm achieves a better near-global solution and compares favourably with other commonly used global optimization techniques. [ABSTRACT FROM AUTHOR]

A three unequal area interconnected power system under deregulated environment having multi-sources of generation is considered. The first area consists of solar thermal and thermal unit, the second area consists of thermal and hydro unit, and the third area contains thermal and gas units as generating units with proper generation rate constraint (GRC). Cascaded proportional integral-proportional derivative with filter coefficient (CPIPDF) is employed as a secondary controller. A recent meta-heuristic algorithm called moth flame optimization (MFO) is considered to optimize the controller parameters. System performances are compared with the classical controllers like proportional-integral (PI), proportional-integral-derivative (PID), proportional-integral-derivative with filter coefficient (PIDF) and cascaded proportional-integral-proportional-derivative (CPIPD) controller. MFO tuned CPIPDF controller performs better than all other controllers considered in this paper for different electrical transactions. Robustness of the optimum gains of CPIPDF controller obtained at poolco transaction condition is evaluated. In addition, system performance is studied with variation in disco participation matrix (DPM). [ABSTRACT FROM AUTHOR]

The whale optimization algorithm (WOA) is an intelligence-based technique that simulates the hunting behaviour of humpback whales in nature. In this article, an adaptation of the original version of the WOA is made for handling binary optimization problems. For this purpose, two transfer functions (S-shaped and V-shaped) are presented to map a continuous search space to a binary one. To illustrate the functionality and performance of the proposed binary whale optimization algorithm (bWOA), its results when applied on twenty-two benchmark functions, three engineering optimization problems and a real-world travelling salesman problem are found. Furthermore, the proposed bWOA is compared with five well-known metaheuristic algorithms. The experimental results show its superiority in comparison with other state-of-the-art metaheuristics in terms of accuracy and speed. Finally, Wilcoxon's rank-sum non-parametric statistical test is carried out at the 5% significance level to judge whether the results of the proposed algorithm differ from those of the other comparison algorithms in a statistically significant way. [ABSTRACT FROM AUTHOR]

This article presents two effective optimization-based model-updating approaches for structural damage identification using the modal residual force (MRF) concept. The first objective function employs a direct data-fitting procedure to inspect the amount of approaching between entries of the calculated MRF vectors for the monitored and analytical models of the structure. The second objective function uses the modal assurance criterion as a geometric tracing criterion to evaluate the amount of accordance between two vectors. The proposed objective functions are solved with a novel metaheuristic optimization technique, the grey wolf optimization algorithm. Four numerical examples are studied to demonstrate the efficiency of the proposed methods. Moreover, studies are conducted not only to assess the robustness of the methods in more realistic circumstances, but also to justify the necessity of using these techniques. Based on the obtained results, the second objective function is more sensitive to structural damage than the first objective function. [ABSTRACT FROM AUTHOR]

Interior Search Algorithm (ISA) is a recently proposed metaheuristic inspired by the beautification of objects and mirrors. However, similar to most of the metaheuristic algorithms, ISA also encounters two problems, i.e., entrapment in local optima and slow convergence speed. In the past, chaos theory has been successfully employed to solve such problems. In this study, 10 chaotic maps are embedded to improve the convergence rate as well as the resulting accuracy of the ISA algorithms. The proposed Chaotic Interior Search Algorithm (CISA) is validated on a diverse subset of 13 benchmark functions having unimodal and multimodal properties. The simulation results demonstrate that the chaotic maps (especially tent map) are able to significantly boost the performance of ISA. Furthermore, CISA is employed as a feature selection technique in which the aim is to remove features which may comprise irrelevant or redundant information in order to minimize the classification error rate. The performance of the proposed approaches is compared with five state-of-the-art algorithms over 21 data sets and the results proved the potential of the proposed binary approaches in searching the optimal feature subsets. [ABSTRACT FROM AUTHOR]

This study proposes a new hybrid biogeography-based optimization (BBO) technique to achieve a better balance between exploitation and exploration sides of BBO. The proposed hybrid metaheuristic algorithm, namely HBBPSGWO, enhances the exploration ability of BBO by combining it with the exploration side of particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms. The proposed hybrid approach is integrated with two classical machine learning models, namely artificial neural network (ANN) and adaptive neuro-fuzzy system (ANFIS), for 1-day-ahead streamflow prediction in a catchment. Daily rainfall and discharge value from 1979 to 2015 of the catchment Fal at Tregony (United Kingdom) is used to validate the performance efficiency of the proposed hybrid algorithm, the HBBPSGWO along with ANN and ANFIS separately. The results demonstrate that the HBBPSGWO-ANN/HBBPSGWO-ANFIS improves the BBO convergence by avoid the trapping it into local minima and the performance has significantly improved compare to basic BBO-based ANN (BBO-ANN)/BBO-based ANFIS (BBO-ANFIS) and PSO-based ANN (PSO-ANN)/PSO-based ANFIS (PSO-ANFIS). In testing phase, root mean square error (RMSE) of HBBPSGWO-ANN is found lowest (0.901) compare to BBO-ANN (0.949) and PSO-ANN (0.926). Whereas in the case of integrated ANFIS, the RMSE of HBBPSGWO-ANFIS is also found minimum (0.741) compare to BBO-ANFIS (0.805) and PSO-ANFIS (0.828). The finding of this research concludes that the proposed hybrid metaheuristic algorithm has better capability to predict the daily streamflow. Moreover, the convergence of HBBPSGWO requires a smaller number of iterations to run in comparison to BBO and PSO. [ABSTRACT FROM AUTHOR]

The Artificial Bee Colony (ABC) algorithm, which simulates the intelligent foraging behavior of the honeybee colony, is one of the most preferred swarm intelligence-based metaheuristic methods for combinatorial optimization problems. In this study, the local search ability of the ABC algorithm, which can be spread to different regions of the solution space, is developed with the pheromone approach of ant colony optimization (ACO). The effects of the method, named pheromonal ABC (pABC), to the standard ABC and its competitiveness with other metaheuristic methods was presented with testing with popular benchmark problems in the NP-hard problem class. For 40 different benchmark problems, while 15 results with ABC have reached the most successful results were obtained in the literature, 25 results obtained with pABC have reached to literature. While ABC best results were behind literature with a percentage of up to 1.12%, pABC best results were behind the percentage of up to 0.63% [ABSTRACT FROM AUTHOR]

Salp Swarm Algorithm (SSA) is a novel swarm intelligent algorithm with good performance. However, like other swarm-based algorithms, it has insufficiencies of low convergence precision and slow convergence speed when dealing with high-dimensional complex optimisation problems. In response to this concerning issue, in this paper, we propose an improved SSA named as WASSA. First of all, dynamic weight factor is added to the update formula of population position, aiming to balance global exploration and local exploitation. In addition, in order to avoid premature convergence and evolution stagnation, an adaptive mutation strategy is introduced during the evolution process. Disturbance to the global extremum promotes the population to jump out of local extremum and continue to search for an optimal solution. The experiments conducted on a set of 28 benchmark functions show that the improved algorithm presented in this paper displays obvious superiority in convergence performance, robustness as well as the ability to escape local optimum when compared with SSA. [ABSTRACT FROM AUTHOR]