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Supply chain efficiency is a major challenge in today's business environment, where efficient resource allocation and coordination of activities are essential for competitive advantage. Traditional efficiency strategies often struggle for resources for the complex and dynamic network. In response, bio-inspired metaheuristic algorithms have emerged as powerful tools to solve these optimization problems. Referring to the random search nature of metaheuristic algorithms and emphasizing that no metaheuristic algorithm is the best optimizer for all optimization applications, the No Free Lunch (NFL) theorem encourages researchers to design newer algorithms to be able to provide more effective solutions to optimization problems. Motivated by the NFL theorem, the innovation and novelty of this paper is in designing a new meta-heuristic algorithm called Bobcat Optimization Algorithm (BOA) that imitates the natural behavior of bobcats in the wild. The basic inspiration of BOA is derived from the hunting strategy of bobcats during the attack towards the prey and the chase process between them. The theory of BOA is stated and then mathematically modeled in two phases (i) exploration based on the simulation of the bobcat's position change while moving towards the prey and (ii) exploitation based on simulating the bobcat's position change during the chase process to catch the prey. The performance of BOA is evaluated in optimization to handle the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100, as well as to address CEC 2020. The optimization results show that BOA has a high ability in exploration, exploitation, and balance them during the search process in order to achieve a suitable solution for optimization problems. The results obtained from BOA are compared with the performance of twelve well-known metaheuristic algorithms. The findings show that BOA has been successful in handling the CEC 2017 test suite in 89.65, 79.31, 93.10, and 89.65% of the functions for the problem dimension equal to 10, 30, 50, and 100, respectively. Also, the findings show that in order to handle the CEC 2020 test suite, BOA has been successful in 100% of the functions of this test suite. The statistical analysis confirms that BOA has a significant statistical superiority in the competition with the compared algorithms. Also, in order to analyze the efficiency of BOA in dealing with real world applications, twenty-two constrained optimization problems from CEC 2011 test suite and four engineering design problems have been selected. The findings show that BOA has been successful in 90.90% of CEC2011 test suite optimization problems and in 100% of engineering design problems. In addition, the efficiency of BOA to handle SCM applications has been challenged to solve ten case studies in the field of sustainable lot size optimization. The findings show that BOA has successfully provided superior performance in 100% of the case studies compared to competitor algorithms. [ABSTRACT FROM AUTHOR]

Rapid technological advances have made daily life easier and more convenient in recent years. As an emerging technology, the Internet of Things (IoT) facilitates interactions between physical devices. With the advent of sensors and features on everyday items, they have become intelligent entities able to perform multiple functions as services. IoT enables routine activities to become more intelligent, deeper communication, and processes more efficient. In the dynamic landscape of the IoT, effective service discovery is key to optimizing user experiences. A Quality of Service (QoS)-aware service discovery technique is proposed in this paper to address this challenge. Through whale optimization and genetic algorithms, our method aims to streamline decision-making processes in IoT service selection. The bio-inspired optimization techniques employed in our approach facilitate the discovery of services more efficiently than traditional methods. Our results demonstrate superior performance regarding reduced data access time, optimized energy utilization, and cost-effectiveness through comprehensive simulations. [ABSTRACT FROM AUTHOR]

Breast cancer is considered one of the significant health challenges and ranks among the most prevalent and dangerous cancer types affecting women globally. Early breast cancer detection and diagnosis are crucial for effective treatment and personalized therapy. Early detection and diagnosis can help patients and physicians discover new treatment options, provide a more suitable quality of life, and ensure increased survival rates. Breast cancer detection using gene expression involves many complexities, such as the issue of dimensionality and the complicatedness of the gene expression data. This paper proposes a bio-inspired CNN model for breast cancer detection using gene expression data downloaded from the cancer genome atlas (TCGA). The data contains 1208 clinical samples of 19,948 genes with 113 normal and 1095 cancerous samples. In the proposed model, Array-Array Intensity Correlation (AAIC) is used at the pre-processing stage for outlier removal, followed by a normalization process to avoid biases in the expression measures. Filtration is used for gene reduction using a threshold value of 0.25. Thereafter the pre-processed gene expression dataset was converted into images which were later converted to grayscale to meet the requirements of the model. The model also uses a hybrid model of CNN architecture with a metaheuristic algorithm, namely the Ebola Optimization Search Algorithm (EOSA), to enhance the detection of breast cancer. The traditional CNN and five hybrid algorithms were compared with the classification result of the proposed model. The competing hybrid algorithms include the Whale Optimization Algorithm (WOA-CNN), the Genetic Algorithm (GA-CNN), the Satin Bowerbird Optimization (SBO-CNN), the Life Choice-Based Optimization (LCBO-CNN), and the Multi-Verse Optimizer (MVO-CNN). The results show that the proposed model determined the classes with high-performance measurements with an accuracy of 98.3%, a precision of 99%, a recall of 99%, an f1-score of 99%, a kappa of 90.3%, a specificity of 92.8%, and a sensitivity of 98.9% for the cancerous class. The results suggest that the proposed method has the potential to be a reliable and precise approach to breast cancer detection, which is crucial for early diagnosis and personalized therapy. [ABSTRACT FROM AUTHOR]

The classical influence maximization (IM) in social network analysis aims at selecting a group of influential nodes as seed so that the number of nodes activated by the set is maximized when the spreading process completes. The single stage seeding problem would ignite all the seeds at the very beginning of the diffusion and let the influence diffuses passively with no additional supervision. However, the effect of scheduled seeding activation operations on improving the marginal profit tends to be ignored. More importantly, the practical scenarios that need external seeding activities can not be well depicted. In this paper, seed activation strategies are investigated for the adaptive influence maximization (AIM) problem under general feedback models. A novel bio-inspired meta-heuristic policy named discrete scheduled particle swarm optimization (DSPSO) is proposed to tackle the intractable problem confronted by AIM, i.e., which nodes need to be ignited at the right time during the process of influence spreading? Following the framework, the proposed policy selects optimal size of nodes into the seed set in each round to ensure the continuation of the spreading process. Dynamic encoding mechanism for the particle individuals is constructed. To make a full exploration of the solution space, a local search strategy specifically for the discrete network topology is implemented on the historical best individuals of the swarm. Extensive experiments on four social networks under different activation feedback models demonstrate the advantage of the scheduled seeding mechanism over the single stage seeding approaches, and show that the proposed DSPSO outperforms the existing greedy strategy as well as the topological heuristics. [ABSTRACT FROM AUTHOR]

This paper proposes a new meta-heuristic optimization algorithm called Cleaner Fish Optimization algorithm (CFO) inspired by cleaner fish. The CFO simulates the movement behavior of cleaner fish when performing "cleaning services" and the behavior of female may change its sex to become a male, and defines two modes of position update. In addition, a two-generation cycle operation strategy is proposed to realize the optimization process. To verify the effectiveness of the CFO algorithm, 23 well-known CEC benchmark functions, CEC-2017 benchmark functions and 4 engineering design problems are adopted. Simulation results show that the proposed algorithm has a faster convergence rate and better optimal solution when it compared with several other meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

Chimp optimization algorithm (ChOA) is a robust nature-inspired technique, which was recently proposed for addressing real-world challenging engineering problems. Due to the novelty of the ChOA, there is room for its improvement. Recognition and classification of marine mammals using artificial neural networks (ANNs) are high-dimensional challenging problems. In order to address this problem, this paper proposed the using of ChOA as ANN's trainer. However, evolving ANNs using metaheuristic algorithms suffers from high complexity and processing time. In order to address this shortcoming, this paper proposes the fuzzy logic to adjust the ChOA's control parameters (Fuzzy-ChOA) for tuning the relationship between exploration and exploitation phases. In this regard, we collect underwater marine mammals sounds and then produce an experimental dataset. After pre-processing and feature extraction, the ANN is used as a classifier. Besides, for having a fair comparison, we used a benchmark audio database of marine mammals. The comparison algorithms include ChOA, coronavirus optimization algorithm, harris hawks optimization, black widow optimization algorithm, Kalman filter benchmark algorithms, and also comparative benchmarks include convergence speed, local optimal avoidance ability, classification rate, and receiver operating characteristics (ROC). The simulation results show that the proposed fuzzy model can tune the boundary between the exploration and extraction phases. The convergence curve and ROC confirm that the convergence rate and performance of the designed recognizer are better than benchmark algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes the Love Evolution Algorithm (LEA), a novel evolutionary algorithm inspired by the stimulus–value–role theory. The optimization process of the LEA includes three phases: stimulus, value, and role. Both partners evolve through these phases and benefit from them regardless of the outcome of the relationship. This inspiration is abstracted into mathematical models for global optimization. The efficiency of the LEA is validated through numerical experiments with CEC2017 benchmark functions, outperforming seven metaheuristic algorithms as evidenced by the Wilcoxon signed-rank test and the Friedman test. Further tests using the CEC2022 benchmark functions confirm the competitiveness of the LEA compared to seven state-of-the-art metaheuristics. Lastly, the study extends to real-world problems, demonstrating the performance of the LEA across eight diverse engineering problems. Source codes of the LEA are publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/159101-love-evolution-algorithm. [ABSTRACT FROM AUTHOR]

Sparse Code Multiple Access (SCMA) is a potential Non-Orthogonal Multiple Access (NOMA) scheme that is widely employed in 5G wireless networks to address rigid improvisations through system capacity, reduced latency, and user-controlled connection. The design of the optimum codebook is critical to the operation of the uplink SCMA-based NOMA system. This paper proposes an optimum parity check polar coding (PCPC)—Probabilistic Shaping(PS)-based uplink SCMA codebook design using a biologically inspired—Hybrid-Firefly—Bat algorithm (HFFBA) for the enhancement of average mutual information (AMI) and a reduction in the bit error rate (BER). The application of optimal PCPC aids in reducing the receiver complexity in the uplink NOMA systems. Furthermore, the diversity gain and process reliability has been improved with the merging of PCPC with SCMA. The proposed system's performance is evaluated over Rayleigh and Additive White Gaussian Noise (AWGN) channels for codebook size of 8 and 16 and aims to improve the performance through AMI and BER. The proposed HFFBA codebook achieved an SNR of 12 dB for AWGN and 18 dB for the Rayleigh channel for M = 4 for a BER of 10–3. The same was attained at an SNR of 21.4 dB for AWGN and 23.6 dB for the Rayleigh channel for M = 16. The gain in SNR for M = 4 to16 for the AWGN and Rayleigh channels are 9.4 dB and 5.6 dB respectively. The optimized codebooks with such biologically inspired algorithms adapt to the changing wireless network conditions and could achieve an increase in data rate and reduced error rate compared to existing systems. [ABSTRACT FROM AUTHOR]

Due to using nature-inspired metaheuristic algorithms to solve real-world applications in different fields, this area has become very popular among researchers because of the flexibility of using optimization algorithms. Therefore, these algorithms can be measured based on their operator, diversity of populations, and balancing between the exploration and exploitation phases. The Grey Wolf Optimization algorithm is one of the competitive metaheuristic algorithms that was proposed in 2014. It mimics the hunting mechanism of wolves. It uses three types of wolves: alpha, beta, and delta for searching in the search space. Despite having competitive performance, GWO has problems regarding local optima and has low exploration. Therefore, enhancement GWO (EGWO) is proposed in this paper in order to solve these problems. EGWO used different methods to improve the performance of GWO: using gamma, z-position, and golden ratio. MGWO is evaluated using CEC2019 ten benchmark functions. Statistical results proved that EGWO outperforms well in six functions out of ten compared to GWO, fitness-dependent optimizer (FDO), cat swarm optimizer, and FOX optimizer. It is also used to solve two real-world applications such as pressure vessel design and 10-bar truss design problems. Results showed that EGWO is very competitive against GWO, FDO, and FOX. [ABSTRACT FROM AUTHOR]

The complexity of engineering optimization problems is increasing. Classical gradient-based optimization algorithms are a mathematical means of solving complex problems whose ability to do so is limited. Metaheuristics have become more popular than exact methods for solving optimization problems because of their simplicity and the robustness of the results that they yield. Recently, population-based bio-inspired algorithms have been demonstrated to perform favorably in solving a wide range of optimization problems. The jellyfish search optimizer (JSO) is one such bio-inspired metaheuristic algorithm, which is based on the food-finding behavior of jellyfish in the ocean. According to the literature, JSO outperforms many well-known meta-heuristics in a wide range of benchmark functions and real-world applications. JSO can also be used in conjunction with other artificial intelligence-related techniques. The success of JSO in solving diverse optimization problems motivates the present comprehensive discussion of the latest findings related to JSO. This paper reviews various issues associated with JSO, such as its inspiration, variants, and applications, and will provide the latest developments and research findings concerning JSO. The systematic review contributes to the development of modified versions and the hybridization of JSO to improve upon the original JSO and present variants, and will help researchers to develop superior metaheuristic optimization algorithms with recommendations of add-on intelligent agents. [ABSTRACT FROM AUTHOR]

The biologically inspired metaheuristic algorithm obtains the optimal solution by simulating the living habits or behavior characteristics of creatures in nature. It has been widely used in many fields. A new bio-inspired algorithm, Aphids Optimization Algorithm (AOA), is proposed in this paper. This algorithm simulates the foraging process of aphids with wings, including the generation of winged aphids, flight mood, and attack mood. Concurrently, the corresponding optimization models are presented according to the above phases. At the phase of the flight mood, according to the comprehensive influence of energy and the airflow, the individuals adaptively choose the flight mode to migrate; at the phase of attack mood, individuals use their sense of smell and vision to locate food sources for movement. Experiments on benchmark test functions and two classical engineering design problems, indicate that the desired AOA is more efficient than other metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

In recent years, the number and severity of natural disasters have increased. These disasters always carry the risks of mortality and injury. But its mortality risks are much higher for people with disabilities than for normal people. One of the most important issues during natural disasters is to pay attention to the provision of accommodation and the possibility of evacuating people with disabilities. In this paper, a Mixed-Integer Linear Programming (MILP) model is proposed for shelter locations, evacuation routing, compatibility constraints in the context of disability, and time windows to picking up people with disabilities from different locations and transporting them to shelters. This study considers the time required for the movement of people with disabilities, heterogeneous vehicles, different capacities, several depots, and several shelters. The intended objective functions are the total distance traveled by vehicles, the maximum distance of a tour, and a hybrid objective function created by the weighted combination of both mentioned objective functions. A new Giza Pyramids Construction (GPC) algorithm is used to solve the model. Using the approach of the GPC algorithm, which is one of the soft computing methods, greatly reduces the computation complexity. To examine the performance of the proposed algorithm, thirty instances at different scales are generated with and without time window constraints. For validation, the results obtained from the proposed algorithm are compared with the results obtained from four algorithms including SA, IWO, DE, and EPC. The results show that the GPC algorithm performed better than other algorithms in solving the model. According to the results, the proposed GPC algorithm is 5%, 44%, 10%, and 4% better than SA, IWO, DE, and EPC, respectively. Furthermore, considering the time window significantly increases the total network evacuation time. Based on our experiments the proposed model and solution approach can be helpful in solving the real problems related to the evacuation of people with disabilities during natural disasters. [ABSTRACT FROM AUTHOR]

The rapid development of data science has led to the emergence of high-dimensional datasets in machine learning. The curse of dimensionality is a significant problem caused by high-dimensional data with a small sample size. This paper proposes a novel hybrid binary dragonfly algorithm (HBDFA) in which a distance-based similarity evaluation algorithm is embedded before the dragonfly algorithm (DA) searching behavior to select the most discriminating features. The two-step feature selection mechanism of HBDFA enables the method to explore the feature space reduced by the distance-based similarity evaluation algorithm. The model was evaluated on two datasets. The first dataset contained 200 reports from 4 evenly distributed categories of Daily Mail Online: COVID-19, economy, science, and sports. The second dataset was the publicly available Spam dataset. The proposed model is compared with binary versions of four popular metaheuristic algorithms. The model achieved an accuracy rate of 96.75% by reducing 66.5% of the top 100 features determined on the first dataset. Results on the Spam dataset reveal that HBDFA gives the best classification results with over 95% accuracy. The experimental results show the superiority of HBDFA in searching high-dimensional data, improving classification results, and reducing the number of selected features. [ABSTRACT FROM AUTHOR]

Cuckoo Search Algorithm (CSA) is a nature-inspired metaheuristic optimization algorithm. The optimization algorithm was inspired by the life story of a family of birds called the Cuckoo. In this paper, an application of the CSA, is implemented to estimate model parameters of deep-seated faults by using gravity anomalies. To increase the efficiency of the algorithm, parameter tuning studies are carried out for the optimum algorithm-based control parameters. In addition, the reliability and possible uncertainties of the obtained solutions were examined using probability density function analysis. The model parameters (z1, z2, α, x0 and ρ) for noisy and noise-free synthetic models were obtained by using CSA. These obtained parameters are very compatible with the true model parameters for noise-free data, similarly well-nigh compatible results for noisy data. As a result we can say that the method works successfully when applied to synthetic data. The technique is tested on three widely studied benchmark field data including gravity anomaly [Garber Fault (USA), Gazal Fault (Egypt), Mersa Matruh Fault (Egypt)]. In previous studies, various solution techniques have been used for the inversion of these field data and the compatibility of the model parameters obtained in this study with the values obtained from previous studies was compared. The results demonstrate that the CSA is a very effective and robust approach for the optimization of gravity anomalies. [ABSTRACT FROM AUTHOR]

There are non-stop efforts being put into enhancing the performance of the available maximum power point tracking methods and proposing new tracking methods. In this paper, a novel maximum power point tracking method based on a physics-inspired metaheuristic algorithm called Electromagnetic Field Optimization algorithm is proposed. The methodology of applying the Electromagnetic Field Optimization method on the maximum power point tracking problem is explained. The proposed method is applied to control the duty cycle of a DC–DC converter in a standalone photovoltaic system. The performance of the proposed method is evaluated against the Cuckoo Search Algorithm method, the Particle Swarm Optimization method, the Perturb and Observe method, and the Incremental Conductance method. A simulation test using MATLAB/Simulink software was conducted for varied sun irradiance levels under fixed temperature and load conditions. An experimental test was also conducted under fixed load and fixed weather conditions. The proposed method achieved tracking efficiencies of 100% and 80.14% in the simulation and experimental tests, accordingly. The superiority of the proposed method over the other applied metaheuristic-based methods is highlighted as the proposed method achieved short tracking times, no steady-state oscillations, and no duty cycle oscillations in both tests. The easiness of tuning the proposed method's parameters is also an advantage of it. [ABSTRACT FROM AUTHOR]

In the present paper, a physics-inspired metaheuristic algorithm is presented to solve multi-objective optimization problems. The algorithm is developed based on the concept of Newtonian cooling law that recently has been employed by the thermal exchange optimization (TEO) algorithm to solve single-objective optimization problems efficiently. The performance of the multi-objective version of TEO (MOTEO) is examined through bi- and tri-objective mathematical and engineering problems as well as bi-objective structural design examples. According to the comparisons between the MOTEO and several well-known algorithms, the proposed algorithm can provide quality Pareto fronts with appropriate accuracy, uniformity, and coverage for multi-objective problems. [ABSTRACT FROM AUTHOR]

Motivated by the hunting strategies of cheetahs, this paper proposes a nature-inspired algorithm called the cheetah optimizer (CO). Cheetahs generally utilize three main strategies for hunting prey, i.e., searching, sitting-and-waiting, and attacking. These strategies are adopted in this work. Additionally, the leave the pray and go back home strategy is also incorporated in the hunting process to improve the proposed framework's population diversification, convergence performance, and robustness. We perform intensive testing over 14 shifted-rotated CEC-2005 benchmark functions to evaluate the performance of the proposed CO in comparison to state-of-the-art algorithms. Moreover, to test the power of the proposed CO algorithm over large-scale optimization problems, the CEC2010 and the CEC2013 benchmarks are considered. The proposed algorithm is also tested in solving one of the well-known and complex engineering problems, i.e., the economic load dispatch problem. For all considered problems, the results are shown to outperform those obtained using other conventional and improved algorithms. The simulation results demonstrate that the CO algorithm can successfully solve large-scale and challenging optimization problems and offers a significant advantage over different standards and improved and hybrid existing algorithms. Note that the source code of the CO algorithm is publicly available at https://www.optim-app.com/projects/co. [ABSTRACT FROM AUTHOR]

This work presents a new nature-inspired meta-heuristic algorithm named spider wasp optimization (SWO) algorithm, which is based on replicating the hunting, nesting, and mating behaviors of the female spider wasps in nature. This proposed algorithm has various unique updating strategies, making it applicable to a wide range of optimization problems with different exploration and exploitation requirements. The proposed SWO is compared with nine newly published and well-established metaheuristics over four different benchmarks: (1) Standard benchmark, including 23 unimodal and multimodal test functions; (2) test suite of CEC2017, (3) test suite of CEC2020, and (4) test suite of CEC2014 to validate its reliability. Moreover, two classical engineering design problems, namely, welded bean and pressure vessel designs, and parameter estimation of the single-diode, double-diode, and triple-diode photovoltaic models are used to further evaluate the performance of SWO in optimizing real-world optimization problems. Experimental findings demonstrate that SWO is more competitive compared with the state-of-art meta-heuristic methods for four validated benchmarks and superior to all observed real-world optimization problems. Specifically, SWO achieves an overall effective percentage of 78.2% on the standard benchmark, 92.31% on CEC2014, 77.78% on CEC2017, 60% on CEC2020, and 100% on real-world problems. The source code of SWO is publicly available at https://www.mathworks.com/matlabcentral/fileexchange/126010-spider-wasp-optimizer-swo. [ABSTRACT FROM AUTHOR]

In the present study, a new local grid reconstruction algorithm for automatic fracture modelling with complex morphologies evolved in rock crack propagation is developed. The algorithm is inspired by the Coulomb force equilibrium of idealized electrons in a constrained area, which leads to the relative uniform distribution of these electrons. As a physically inspired computing algorithm, nodes in the re-meshing area are equivalent to idealized electrons with different charges. This meta-heuristic algorithm includes several efficient implementation aspects, such as the treatment of initial seed, re-meshing area confirmation, Coulomb force calculation and mesh improvement. The algorithm can efficiently transform a structured background mesh with complex boundaries into a high-quality conforming mesh with low aspect ratio and high stretch value triangular elements. Combined with a discrete crack method, the proposed algorithm has been implemented in cracking simulation. Distinguishing with the previous methods, the meshing quality, efficiency, computing speed and calculating accuracy are greatly improved. Next, based on verification and validation of the methodology, the proposed algorithm is applied to the cracking simulations of compression induced wing cracks and mixed-mode cracks that evolve in three-point bending tests. The ability of the method for the simulation of cracking process is fully demonstrated. Highlights: An automatic local re-meshing algorithm for crack simulation inspired from the Coulomb force equilibrium of idealised electrons in constrained area is developed. Meshing quality, efficiency, computing speed, convergence rate and calculating accuracy are greatly improved. The ability of the algorithm for the cracking process simulation is well demonstrated. [ABSTRACT FROM AUTHOR]

A novel bio-inspired meta-heuristic algorithm, namely the American zebra optimization algorithm (AZOA), which mimics the social behaviour of American zebras in the wild, is proposed in this study. American zebras are distinguished from other mammals by their distinct and fascinating social character and leadership exercise, which navies the baby zebras to leave the herd before maturity and join a separate herd with no family ties. This departure of the baby zebra encourages diversification by preventing intra-family mating. Moreover, the convergence is assured by the leadership exercise in American zebras, which directs the speed and direction of the group. This social lifestyle behaviour of American zebras is indigenous in nature and is the main inspiration for proposing the AZOA meta-heuristic algorithm. To examine the efficiency of the AZOA algorithm, the CEC-2005, CEC-2017, and CEC-2019 benchmark functions are considered, and compared with the several state-of-the-art meta-heuristic algorithms. The experimental outcomes and statistical analysis reveal that AZOA is capable of attaining the optimal solutions for maximum benchmark functions while maintaining a good balance between exploration and exploitation. Furthermore, numerous real-world engineering problems have been employed to demonstrate the robustness of AZOA. Finally, it is anticipated that the AZOA will accomplish domineeringly for forthcoming advanced CEC benchmark functions and other complex engineering problems. [ABSTRACT FROM AUTHOR]

The firefly algorithm (FA) and the artificial bee colony (ABC) algorithm are used in this study to perform a coordinated parametrization of the proportional-integral and supplementary damping controllers, i.e., power system stabilizers (PSSs) and the unified power flow controller (UPFC)–power oscillation damping set. The parametrization obtained for the controllers should allow them to damp the low-frequency oscillatory modes in the power system for different loading scenarios. The power system dynamics is represented using a model based on current injections, known as the current sensitivity model, which implies that a formulation by current injections for the UPFC should be formulated. To validate the proposed optimization techniques and the current injection model for the UPFC for small-signal stability, simulations are carried out under two distinct perspectives, namely, static and dynamic analysis, using the New England system. The results demonstrated the effectiveness of the UPFC's current injection model. Moreover, it was possible to verify that the FA performed better than the ABC algorithm to solve the discussed problem, accrediting both the UPFC current injection model and the FA algorithm as new tools for small-signal stability analysis in electrical power systems. [ABSTRACT FROM AUTHOR]

We focus on the performances of three nature-inspired metaheuristic methods for the optimization of time-domain electromagnetic (TDEM) data: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO) and the Grey Wolf Optimizer (GWO) algorithms. While GA and PSO have been used in a plethora of geophysical applications, GWO has received little attention in the literature so far, despite promising outcomes. This study directly and quantitatively compares GA, PSO and GWO applied to TDEM data. To date, these three algorithms have only been compared in pairs. The methods were first applied to a synthetic example of noise-corrupted data and then to two field surveys carried out in Italy. Real data from the first survey refer to a TDEM sounding acquired for groundwater prospection over a known stratigraphy. The data set from the second survey deals with the characterization of a geothermal reservoir. The resulting resistivity models are quantitatively compared to provide a thorough overview of the performances of the algorithms. The comparative analysis reveals that PSO and GWO perform better than GA. GA yields the highest data misfit and an ineffective minimization of the objective function. PSO and GWO provide similar outcomes in terms of both resistivity distribution and data misfits, thus providing compelling evidence that both the emerging GWO and the established PSO are highly valid tools for stochastic inverse modeling in geophysics. [ABSTRACT FROM AUTHOR]