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The moth search algorithm (MSA) is a meta-heuristic optimization technique inspired by moth behavior, has shown remarkable efficacy in solving optimization challenges. However, its poor exploration capability results in an imbalance between exploitation and exploration. To address this issue, this research introduces a new mutation operator to enhance exploration by increasing population diversity. The proposed enhanced moth search algorithm (EMSA) aims to expedite convergence and improve overall robustness by exploring new solutions more effectively. Evaluation on ten benchmark functions demonstrates EMSA's superior exploration capabilities, efficiently tackling optimization problems and yielding more optimal solutions within the search space. Compared to conventional MSA and other established algorithms, EMSA delivers well-balanced results, showcasing its effectiveness in optimizing the search space. In the future, the EMSA could potentially find applications in addressing real-world engineering optimization challenges. [ABSTRACT FROM AUTHOR]

High-utility itemsets mining (HUIM) is an important research topic in data mining that consists of extracting efficient itemsets that generate high profit in databases. Metaheuristic algorithms are good candidates for finding some sufficiently good high-utility itemsets (HUIs) while reducing the prohibitive computational cost incurred by such mining. However, metaheuristic algorithms suffer from crucial issues: runtime and memory consumption, entailing scalability issues. To contribute in alleviating these drawbacks, we explore a hybrid approach combining metaheuristics with an optimization technique, and experimentally show the obtainment of better quality solutions. The exploration component in our approach relies on the crow search algorithm (CSA) which is a method that presents a strong capacity in the global search process. The exploitation component is based on the particle swarm optimization (PSO) which is an established stochastic optimization technique known to have a good convergence capability. This article proposes the HUIM-ICSO algorithm, coupling CSA and PSO, as a hybrid method for mining HUIs. In addition to the proposed main CSA-based algorithm, two additional novel enhanced variants are incorporated and integrated with PSO. Experiments are undertaken on 12 publicly available datasets and compared with state-of-the-art algorithms, such as mHUIMiner, HUIM-SPSO and HUIM-AF. Experiments show that, as the number of transactions or items grows, the suggested approach remains scalable and delivers favorable outcomes in terms of runtime, memory usage, and the quantity of HUIs mined, all while maintaining a good convergence rate. [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]

Many new metaheuristic algorithms prioritize their search strategy phase, often neglecting equally critical stages like initialization. Latin hypercube sampling (LHS) is one technique that stands out in this context. LHS selects representative samples through permutations in a multidimensional space, effectively preventing the clustering of points in specific areas. However, its limitations become apparent in high-dimensional problems, as it fails to provide crucial search space information and struggles to determine samples across all dimensions. Addressing these challenges, this paper introduces an innovative population initialization approach, combining the strengths of LHS with evolutionary behaviors. This technique is divided into two main sections: spatial and quality. The spatial section divides the search space into equal intervals across each dimension to establish initial solutions. Meanwhile, the quality section employs evolutionary strategies like mutation and crossover. These strategies serve a dual purpose: they explore the search space thoroughly and refine solutions, bringing them closer to the objective function. To validate the effectiveness of this method, these principles have been integrated it into the classic Differential Evolution algorithm. We conducted extensive tests using 30 representative benchmark functions to assess its performance. The experimental results are encouraging; our methodology not only speeds up convergence but also enhances solution quality, outperforming other similar techniques. [ABSTRACT FROM AUTHOR]

Several optimization-based population search methods have been proposed; they use various operators that permit exploring the search space. These methods typically suffer from local search (LS) problems and are unbalanced between exploration and exploitation. Consequently, recent researchers sought to modify the algorithms to avoid search problems using local search techniques to intensify the exploitation when is necessary. This paper proposes a novel single-based local search optimization algorithm called the non-monopolize search (NO). The NO is a single-solution metaphor-free algorithm, and its operators are designed based to explore and exploit along the iterative process. The NO works only with a candidate solution, and the operators modify the dimension to move the current solution along the search space. The NO is an effective LS method that combines the benefits of exploration with exploitation. Different from other LS, the NO can escape from suboptimal solutions thanks to the randomness incorporated into its operators. This is the main advantage of the NO. Experiments are conducted on standard benchmark functions to validate the performance of the proposed non-monopolize search optimization technique. The results are compared with other well-known methods, and the proposed NO got better results. Moreover, the proposed NO can be considered a powerful alternative to improve the optimization algorithms' performance and help avoid local search problems. Source codes of NO are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/156154-the-non-monopolize-search-no. [ABSTRACT FROM AUTHOR]

Jaya is a metaheuristic algorithm that uses a pair of random internal parameters to adjust its exploration and exploitation search behaviors. Such a random setting can negatively affect the search performance of the algorithm by causing inappropriate search behavior in some iterations. To tackle this issue, the present study deals with developing a new fuzzy decision-making mechanism for dynamic adjusting the trade-off between the exploration and exploitation search behaviors of the Jaya method. The new algorithm is named Fuzzy Reinforced Jaya (FRJ) method. The search capability of the FRJ is evaluated in solving a suite of unconstrained mathematical benchmarks and constrained mechanical and structural optimization problems with buckling and natural frequency constraints. Also, the relevant decision variables are selected from both continuous and discrete domains. To provide a deeper insight into the effect of the defined auxiliary fuzzy module, the performance of the algorithm is evaluated and discussed using normalized diversity concept and behavioral diagrams. Also, employing different statistical analyses (e.g., Q–Q diagrams, Wilcoxson and Friedman tests), the significance of the outcomes is evaluated. Also, the numeric achievements are compared with six other well-stablished techniques. Attained outcomes indicate that the proposed FRJ, as a self-adaptive and parameter-free method, provides superior and promising results in the terms of stability, accuracy, and computational cost in solving mathematical and structural optimization problems. [ABSTRACT FROM AUTHOR]

Use Case Points estimation framework relies on the complexity weight parameters to estimate software development projects. However, due to the discontinue parameters, it lead to abrupt weight classification and results in inaccurate estimation. Several research studies have addressed these weaknesses by employing various approaches, including fuzzy logic, regression analysis, and optimization techniques. Nevertheless, the utilization of optimization techniques to determine use case weight parameter values has yet to be extensively explored, with the potential to enhance accuracy further. Motivated by this, the current research delves into various metaheuristic search-based algorithms, such as genetic algorithms, Firefly algorithms, Reptile search algorithms, Particle swarm optimization, and Grey Wolf optimizers. The experimental investigation was carried out using a Silhavy UCP estimation dataset, which contains 71 project data from three software houses and is publicly available. Furthermore, we compared the performance between models based on metaheuristic algorithms. The findings indicate that the performance of the Firefly algorithm outperforms the others based on five accuracy metrics: mean absolute error, mean balance relative error, mean inverted relative error, standardized accuracy, and effect size. This research could be useful for software project managers to leverage the practical implications of this study by utilizing the UCP estimation method, which is optimized using the Firefly algorithm. [ABSTRACT FROM AUTHOR]

As science and technology advance, the need for novel optimization techniques has led to an increase. The recently proposed metaheuristic algorithm, Gradient-based optimizer (GBO), is rooted in the gradient-based Newton's method. GBO has a more concrete theoretical foundation. However, gradient search rule (GSR) and local escaping operator (LEO) operators in GBO still have some shortcomings. The insufficient updating method and the simple selection process limit the search performance of the algorithm. In this paper, an improved version is proposed to compensate for the above shortcomings, called RL-SDOGBO. First, during the GSR phase, the Spearman rank correlation coefficient is used to determine weak solutions on which to perform dynamic opposite learning. This operation assists the algorithm to escape from local optima and enhance exploration capability. Secondly, to optimize the exploitation capability, reinforcement learning is used to guide the selection of solution update modes in the LEO operator. RL-SDOGBO is tested on 12 classical benchmark functions and 12 CEC2022 benchmark functions with seven representative metaheuristics, respectively. The impact of the improvements, the scalability and running time of the algorithm, and the balance of exploration and exploitation are analyzed and discussed. Combining the experimental results and some statistical results, RL-SDOGBO exhibits excellent numerical optimization performance and provides high-quality solutions in most cases. In addition, RL-SDOGBO is also used to solve the anchor clustering problem for small target detection, making it a more potential and competitive option. [ABSTRACT FROM AUTHOR]

The reptile search algorithm (RSA) is a dynamic and effective meta-heuristic algorithm inspired by the behavior of crocodiles in nature and the way of hunting prey. Unlike other crawler search algorithms, it uses four novel mechanisms to update the location of the solutions, such as walking at high or on the belly, and hunting in a coordinated or cooperative manner. In this algorithm, the total number of iterations is divided into four intervals, and different position-updating strategies are used to make the algorithm easily fall into the local optimum. Therefore, an improved reptile search algorithm based on a mathematical optimization accelerator (MOA) and elementary functions is proposed to improve its search efficiency and make it not easily fall into local optimum. MOA was used to realize the switching of RSA's four searching modes by introducing random perturbations of six elementary functions (sine function, cosine function, tangent function, arccosine function, hyperbolic secant function and hyperbolic cosecant function), four mechanisms are distinguished by random number instead of the original RSA algorithm's inherent four mechanisms by iteration number, which increases the randomness of the algorithm and avoids falling into local optimum. The random perturbations generated by elementary functions are added to the variation trend of parameter MOA to improve the optimization accuracy of the algorithm. To verify the effectiveness of the proposed algorithm, 30 benchmark functions in CEC2017 were used for carrying out simulation experiments, and the optimization performance was compared with BAT, PSO, ChOA, MRA and SSA. Finally, two practical engineering design problems are optimized. Simulation results show that the proposed sechRSA has strong global optimization ability. [ABSTRACT FROM AUTHOR]

The next step in mobile communication technology, known as 5G, is set to go live in a number of countries in the near future. New wireless applications have high data rates and mobility requirements, which have posed a challenge to mobile communication technology researchers and designers. 5G systems could benefit from the Universal Filtered Multicarrier (UFMC). UFMC is an alternate waveform to orthogonal frequency-division multiplexing (OFDM), in filtering process is performed for a sub-band of subcarriers rather than the entire band of subcarriers Inter Carrier Interference (ICI) between neighbouring users is reduced via the sub-band filtering process, which reduces out-of-band emissions. However, the UFMC system has a high Peak-to-Average Power Ratio (PAPR), which limits its capabilities. Metaheuristic optimization based Selective mapping (SLM) is used in this paper to optimise the UFMC-PAPR. Based on the cognitive behaviour of crows, the research study suggests an innovative metaheuristic optimization known as Crow Search Algorithm (CSA) for SLM optimization. Compared to the standard UFMC, SLM-UFMC system, and SLM-UFMC with conventional metaheuristic optimization techniques, the suggested technique significantly reduces PAPR. For the UFMC system, the suggested approach has a very low Bit Error Rate (BER). [ABSTRACT FROM AUTHOR]

The Travelling Salesman Problem (TSP) is a well-known combinatorial optimization problem that belongs to a class of problems known as NP-hard, which is an exceptional case of a travelling salesman problem (TSP), which determines a set of routes enabling multiple salesmen to start at and return to home cities (depots). The penguins search optimization algorithm (PeSOA) is a new metaheuristic optimization algorithm. This paper presents a discrete penguins search optimization algorithm (PeSOA) for solving the multiple traveling salesman problem (MTSP). A set of benchmarks of TSP instances from TSPLIB library evaluates the PeSOA. The experimental results show that PeSOA is very efficient in finding the right solutions in a reasonable time. [ABSTRACT FROM AUTHOR]

Controlled thermonuclear reactors require consistent monitoring of plasma in the toroidal chamber. Better working conditions of such machines can be monitored by analyzing its radiations. Various wavelengths such as 656.3, 486.1, 464.7 nm are quite significant which are used for health monitoring of thermonuclear machines. The optical thin film filters which work on constructive and destructive interference are the ideal choices. These filters are multilayered with a pair of high and low refractive index dielectric materials. Significantly high transmission index at the desired wavelength and relatively low transmission at the other wavelengths are desired. With this as the objective, it is necessary to design the filter. Various optimization techniques are used for identifying the suitable design of the filters. To choose the parameter combination that provides the most excellent performance, optimization of the design parameters is entailed. The goal of this work is to improve the optical band filter using the Bald eagle search optimization (BES) method. The ideal design is determined by assessing several characteristics such as thickness, refractive index, Full-Width at Half-Maximum (FWHM), and the impact of choosing optical properties, which increases transmission potential. Initially, an alternate multi-layer stack with 28, 30, and 32 layers is created by altering the thickness while keeping the dielectric substances high and low refractive indices constant. By adjusting the thickness of each layer, the BES algorithm achieves the best practical solution. The proposed method is implemented using MATLAB and the outcomes show the efficacy of the proposed technique. The transmittance, reflectance, and FWHM using the proposed BES are found to be 99.9356%, 0.065%, and 1.2 nm respectively. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of the seagull optimization algorithm (SOA), which has low solution accuracy, and rela-tively poor population diversity, and is easy to trap in local optimal, this paper proposed an enhanced seagull optimization algorithm (ESOA) that integrated multiple improvement strategies. Firstly, this paper used an improved self-adaptive differential mutation strategy to perform differential mutation operation on a single seagull individual in each iteration process which could expand the global search range of seagulls and improve the diversity of the population.Secondly, it established a processing mechanism for the worst position of individual seagulls based on particle swarm algorithm.Finally, for the optimal position of the seagull, it adopted a dynamic lens mapping strategy to jump out of the local optimum for the optimal position of the seagull. This paper used 14 functions in the CEC2017 test suite as the benchmark function to compare the performance of ESOA, algorithm with sparrow search algorithm (SSA), moth-flame optimization algorithm (MFO), grey wolf optimizer (GWO), and the improved GSCSOA and CCSOA. The experimental results show that ESOA has a statistically significant performance advantage. [ABSTRACT FROM AUTHOR]

In this article, the exploration and stochastic property of elite opposition‐based learning and chaotic maps are utilized to introduce a hybridized metaheuristic optimization technique. Both the techniques are combined with state of matter search optimization to enhance its capability of locating global minima. The proposed hybrid algorithm is tested on various benchmark functions and compared with the state of mater search optimization to verify its efficiency. The results show that the proposed hybrid algorithm gives better convergence for various benchmark functions. [ABSTRACT FROM AUTHOR]

Clustering is a technique of grouping the data objects into clusters. Many metaheuristic algorithms based on swarm intelligence, physic laws, and chemical reactions, among others, have been developed for clustering. In this study, an enhanced whale optimization algorithm (EWOA) is introduced to solve clustering problems. The whale optimization algorithm (WOA) is adapted and enhanced with two additional operational procedures. The position update equations from the water wave optimization algorithm are incorporated into the algorithm to improve the search space and accelerate the convergence rate. The tabu and neighbourhood search mechanisms were added to handle the local optima situation. The efficiency of the proposed EWOA is measured using a simulation-based experiment conducted on eight benchmark datasets, and the results obtained are then compared to seven existing clustering algorithms/techniques. The performance of each algorithm is compared and analyzed using the average intra-cluster distance and f-measure parameters. The experimental results demonstrated the applicability and feasibility of the enhancements that were made and proved the superiority of the proposed EWOA clustering algorithm. [ABSTRACT FROM AUTHOR]

Arithmetic optimization algorithm (AOA) is a recent population-based metaheuristic widely used for solving optimization problems. However, the emerging large-scale optimization problems pose a great challenge for AOA due to its prohibitive computational cost to traverse the huge solution space effectively. This article proposes a parallel Spark-AOA using Scala on Apache Spark computing platform. Spark-AOA leverages the intrinsic parallel nature of the population-based AOA and the native iterative in-memory computation support of Spark through resilient distributed datasets (RDD) to accelerate the optimization process. Spark-AOA divides the solutions population into several subpopulations that are distributed into multiple RDD partitions and manipulated concurrently. Simulation experiments on different benchmark functions with up to 1,000-dimension and three engineering design problems demonstrate that Spark-AOA outperforms considerably standard AOA and Spark-based implementations of two recent metaheuristics both in terms of run-time and solution quality. [ABSTRACT FROM AUTHOR]

In small workshops, a more number of holes on aero engine parts are drilled successively in one process to confirm positional accuracy. Because of the fast time-varying drill wear, the surface roughness of the holes is unstable and difficult to be satisfied. To the present end, this paper presents a varying-parameter drilling (VPD) method to enhance machining efficiency and hole surface roughness for multi-hole parts manufactured from Al 7075 alloys. This method uses varying cutting parameters for every hole to adapt to the varying drill wear. The major problem of this method deceit in an optimization issue during which the optimal combination of setting of cutting parameters have to be found, with the target of the interval and also the constaint of the opening surface roughness, because sequence of cutting parameter encompasses a important aspect and therefore the surface roughness of all the holes must be guaranteed, the challenge of this optimization issue is that the strict constraint with a sophisticated non-linear boundary of the feasible zone. To cope with the convergence complexity of the searching algorithm, a metaheuristic method supported particle swarm optimization (PSO) algorithm with a self-adaptive penalty method (SAPM) is applied. The drilled hole surface roughness is predicted with a radial basis function (RBF) neural network. The various types of drill wear comprising flank wear, crater wear, chisel wear and outer corner wear are considered and the grey relational analysis (GRA) is deployed to pick the input drill wear parameters to the network. The PSO algorithms integrate with the SAPM is used to search the overall optimal solution of the optimization problem. It is found that the satisfied solutions can be searched in all the trials with the proposed metaheuristic algorithms, even though the proportion of feasible solutions is severely fluctuant during the searching process. The drilling experiment confirm that, when compared with the fixed-parameter drilling, the proposed VPD and the metaheuristic algorithm method for solving the optimization problem can effectively improve machining efficiency and surface quality for drilling Al 7075 alloy multi-hole parts. [ABSTRACT FROM AUTHOR]

Software testing is considered as the basic procedure and it is genuinely supportive for several software developers. Developing an automatic test case generation helps the software professionals to conserve more time. In recent years, with the aim of saving cost and time, majority of the software is delivered without sufficient testing resulting in revenue loss. Simultaneously, the testing cost minimizes with the diminution of testing time. To overcome the above-mentioned limitations, this paper proposes a shuffled shepherd flamingo search (S2FS) approach for an optimized and automatic test case generation with minimum execution time. The S2FS approach is an integration of two different metaheuristic algorithms namely the shuffled shepherd optimization algorithm and flamingo search optimization (FSO) algorithm. The significant intention of the proposed approach is to evaluate the efficiency and effectiveness thereby enhancing the generation of test cases. In addition to this, this paper also determines the efficiency of the proposed technique based on ATM operation with respect to total number of test case generations. The test cases of ATM are provided as an input to the proposed S2FS approach to determine an optimal test case. Finally, the experimental evaluations and comparative analysis are performed to determine the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

In recent years, a large number of meta-heuristic algorithms have been proposed to efficiently solve various complex optimization problems in reality. Most of these algorithms are based on the intelligent behavior of swarms in the natural world. In this article, we take Hooke's law of elasticity and Newton's second law of motion as the information interaction tools and innovatively propose a new meta-heuristic algorithm that is based on the laws of physics, called the elastic deformation optimization algorithm (EDOA). A new parameter adaptive adjustment mechanism is designed in the EDOA to better explore and exploit the search space. At the same time, we compare the proposed EDOA with six well-known search algorithms and conduct simulation experiments on 23 classical benchmark functions and IEEE CEC 2020 benchmark functions respectively. We have further analyzed the experimental results, used two nonparametric statistical test methods, and drawn iterative curves of the algorithms to prove the powerful comprehensive performance of the proposed EDOA. [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 hybrid optimization of modern cementitious materials requires concrete to meet many competing objectives (e.g., mechanical properties, cost, workability, environmental requirements, and durability). This paper reviews the current literature on optimizing mixing ratios using machine learning and metaheuristic optimization algorithms based on past studies on varying methods. In this review, we first discuss the conventional methods for mixing optimization of cementitious materials. Then, the problem expression of hybrid optimization is discussed, including decision variables, constraints, machine learning algorithms for modeling objectives, and metaheuristic optimization algorithms for searching the best mixture ratio. Finally, we explore the development prospects of this field, including, expanding the database by combining field data, considering more influencing variables, and considering more competitive targets in the production of functional cemented materials. In addition, to overcome the limitation of the swarm intelligence-based multi-objective optimization (MOO) algorithm in hybrid optimization, this paper proposes a new MOO algorithm based on individual intelligence (multi-objective beetle antenna search algorithm). The development of computationally efficient robust MOO models will continue to make progress in the field of hybrid optimization. This review is adapted for engineers and researchers who want to optimize the mixture proportions of cementitious materials using machine learning and metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Water-cycle algorithm based on evaporation rate (ErWCA) is a powerful enhanced version of the water-cycle algorithm (WCA) metaheuristics algorithm. ErWCA, like other algorithms, may still fall in the sub-optimal region and have a slow convergence, especially in high-dimensional tasks problems. This paper suggests an enhanced ErWCA (EErWCA) version, which embeds local escaping operator (LEO) as an internal operator in the updating process. ErWCA also uses a control-randomization operator. To verify this version, a comparison between EErWCA and other algorithms, namely, classical ErWCA, water cycle algorithm (WCA), butterfly optimization algorithm (BOA), bird swarm algorithm (BSA), crow search algorithm (CSA), grasshopper optimization algorithm (GOA), Harris Hawks Optimization (HHO), whale optimization algorithm (WOA), dandelion optimizer (DO) and fire hawks optimization (FHO) using IEEE CEC 2017, was performed. The experimental and analytical results show the adequate performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithm is a generalization of heuristic algorithm that can be applied to almost all optimization problems. For optimization problems, metaheuristic algorithm is one of the methods to find its optimal solution or approximate solution under limited conditions. Most of the existing metaheuristic algorithms are designed for serial systems. Meanwhile, existing algorithms still have a lot of room for improvement in convergence speed, robustness, and performance. To address these issues, this paper proposes an easily parallelizable metaheuristic optimization algorithm called team competition and cooperation optimization (TCCO) inspired by the process of human team cooperation and competition. The proposed algorithm attempts to mathematically model human team cooperation and competition to promote the optimization process and find an approximate solution as close as possible to the optimal solution under limited conditions. In order to evaluate the performance of the proposed algorithm, this paper compares the solution accuracy and convergence speed of the TCCO algorithm with the Grasshopper Optimization Algorithm (GOA), Seagull Optimization Algorithm (SOA), Whale Optimization Algorithm (WOA) and Sparrow Search Algorithm (SSA). Experiment results of 30 test functions commonly used in the optimization field indicate that, compared with these current advanced metaheuristic algorithms, TCCO has strong competitiveness in both solution accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

Nature-inspired metaheuristic algorithms have gained great attention over the last decade due to their potential for finding optimal solutions to different optimization problems. In this study, a metaheuristic based on the dwarf mongoose optimization algorithm (DMOA) is presented for the parameter estimation of an autoregressive exogenous (ARX) model. In the DMOA, the set of candidate solutions were stochastically created and improved using only one tuning parameter. The performance of the DMOA for ARX identification was deeply investigated in terms of its convergence speed, estimation accuracy, robustness and reliability. Furthermore, comparative analyses with other recent state-of-the-art metaheuristics based on Aquila Optimizer, the Sine Cosine Algorithm, the Arithmetic Optimization Algorithm and the Reptile Search algorithm—using a nonparametric Kruskal–Wallis test—endorsed the consistent, accurate performance of the proposed metaheuristic for ARX identification. [ABSTRACT FROM AUTHOR]

Accurate and effective wind power prediction plays an important role in wind power generation, distribution, and management. Inthis paper, a hybrid algorithm based on gradient descent and meta-heuristic optimization is designed to improve the accuracy of prediction and reduce the computational burden. The hybrid algorithm includes three steps: in the first step, we use the gradient descent algorithm to get the initial parameters. Secondly, we input the initial parameters into the meta-heuristic optimization algorithm to search for the "best parameters" (high-quality inferior solutions). Finally, we input optimized parameters into the RMSProp optimization algorithm and conduct gradient descent again to find a better solution. We used 2021 wind power data from Guangxi, China for the experiment. The results show that the hybrid prediction algorithm has better performance than the traditional Back Propagation (BP) in accuracy, stability, and efficiency. [ABSTRACT FROM AUTHOR]

In this research study, a new combination search algorithm, based on indexing its constituent processes, is proposed to solve global optimisation problems. As optimisation problems become more complex, especially real‐world problems, the use of higher‐performance algorithms has become essential. One of the techniques that lead to design of an algorithm with stronger strategies in exploration and exploitation, as well as a better balance between these two strategies, is the appropriate combination of parent algorithm processes. The proposed algorithm was developed using a new innovation with the help of the supply‐demand‐based optimisation and the Harris hawks optimisation algorithms processes as parent algorithms. In this algorithm, the local and global search sections of its parent algorithms, are separated, and then based on a new indexing method in each iteration, according to the current population indexing, a global search and a local search are selected from the processes of its parent algorithms, and then the current population is updated with two selected sections. The performance and effectiveness of the proposed algorithm in solving well‐known standard benchmark problems and in solving real‐world engineering problems have been tested and validated by statistical tools. The results of the research study show that the proposed algorithm can provide very effective results compared to other competing algorithms as well as its parent algorithms in many tests. The results show that the proper combination of optimisation algorithm processes can be used as a technique to design more powerful algorithms to solve global optimisation problems, especially complex real‐world problems. [ABSTRACT FROM AUTHOR]

This paper introduces a novel physical-inspired metaheuristic algorithm called "Light Spectrum Optimizer (LSO)" for continuous optimization problems. The inspiration for the proposed algorithm is the light dispersions with different angles while passing through rain droplets, causing the meteorological phenomenon of the colorful rainbow spectrum. In order to validate the proposed algorithm, three different experiments are conducted. First, LSO is tested on solving CEC 2005, and the obtained results are compared with a wide range of well-regarded metaheuristics. In the second experiment, LSO is used for solving four CEC competitions in single objective optimization benchmarks (CEC2014, CEC2017, CEC2020, and CEC2022), and its results are compared with eleven well-established and recently-published optimizers, named grey wolf optimizer (GWO), whale optimization algorithm (WOA), and salp swarm algorithm (SSA), evolutionary algorithms like differential evolution (DE), and recently-published optimizers including gradient-based optimizer (GBO), artificial gorilla troops optimizer (GTO), Runge–Kutta method (RUN) beyond the metaphor, African vultures optimization algorithm (AVOA), equilibrium optimizer (EO), grey wolf optimizer (GWO), Reptile Search Algorithm (RSA), and slime mold algorithm (SMA). In addition, several engineering design problems are solved, and the results are compared with many algorithms from the literature. The experimental results with the statistical analysis demonstrate the merits and highly superior performance of the proposed LSO algorithm. [ABSTRACT FROM AUTHOR]

Harmonics worsen the quality of electrical signals, hence, there is a need to eliminate them. The test objects under discussion are single-phase versions of cascaded H-bridge (CHB) multilevel inverters (MLIs) whose switching angles are optimized to eliminate specific harmonics. The Dragonfly Algorithm (DA) is used to eradicate low-order harmonics, and its statistical performance is compared to that of many other optimization techniques, including Particle Swarm Optimization (PSO), Accelerated Particle Swarm Optimization (APSO), Differential Evolution (DE), and Grey Wolf Optimization (GWO). Various scenarios of the algorithms' search agent population for inverters with seven, nine, and eleven levels of output voltages are comprehensively addressed in this research. No algorithm shows total dominance in every scenario. The DA is least impacted by the change in dimensions of the narrated problem. [ABSTRACT FROM AUTHOR]

The rapid growing number of web services has posed new challenges for service composition computing. How to combine services that meet the needs of users in the least amount of time from a huge number of candidate services is a hot topic of research today. As a meta-heuristic algorithm for solving optimization problems, salp swarm algorithm (SSA) has been widely applied to case scenarios in different fields due to its simple structure and high performance. However, QoS-aware service composition is a discrete problem and existing methods are not suitable for it. Therefore, in this paper, we propose an improved SSA integrating chaotic mapping method for QoS service composition selection, named CSSA. Through the randomness and ergodicity of chaos, reducing the possibility of falling into local optimum and strengthening the exploitation capability of the algorithm. In addition, a fuzzy continuous neighborhood search method is used to enhance the local search capability of the algorithm which makes the discrete space of service composition in a way similar to continuous space. Finally, two well-known datasets are used to verify the effectiveness of CSSA compared to three advanced algorithms and original SSA. The test results demonstrate that CSSA has significant advantages and it also has satisfactory performance in large scale scenarios. [ABSTRACT FROM AUTHOR]

The development of a novel hybrid algorithm by modifying the arithmetic optimization algorithm (AOA) with the aid of simulated annealing technique is discussed in this paper. The novel algorithm, named modified arithmetic optimization algorithm (mAOA), is proposed as an effective tool for optimizing power system stabilizer (PSS) adopted in a single-machine infinite-bus power system. To perform the assessments, MATLAB/Simulink software was used. The evaluations on the proposed algorithm are initially performed using several benchmark functions that have unimodal and multimodal natures. The results are then compared with five of the other competitive approaches (arithmetic optimization algorithm, simulated annealing algorithm, genetic algorithm, particle swarm optimization and gravitational search algorithm). The comparisons with respect to those algorithms demonstrate the great promise of the constructed hybrid mAOA algorithm. This shows the greater balance between global and local search stages achieved by the mAOA algorithm. The performance of the developed mAOA algorithm is also assessed through designing an optimally performing PSS for further evaluation which allows the observation of its capability for complex real-world engineering problems. To do so, PSS damping controller is formulated as an optimization problem and the constructed mAOA algorithm is used to search for optimal controller parameters to demonstrate the applicability and the greater performance of the proposed hybrid algorithm for such a complex real-world engineering problem. The obtained results for the latter case are compared with the sine-cosine and symbiotic organisms search algorithms as they are the best performing reported algorithms. The comparisons have demonstrated the superiority of the mAOA algorithm over reported best performing algorithms in terms of PSS design, as well. [ABSTRACT FROM AUTHOR]

This paper presents a new method relied on improved coyote optimization algorithm (ICOA) for traveling salesman problem (TSP). COA is a recent recently metaheuristic algorithm that is inspired from the social life of coyote. To improve the performance of ICOA, the 2-opt algorithm is applied to adjust created new solutions. In addition, the swapping technique with varying exchange city number is also equipped to enhance the exploration and exploitation ability of ICOA. The efficiency of the ICOA is compared with COA on the 14-, 30-, 48-, 52-, 76- and 100-city instances. The simulated results show that ICOA reaches the better results than COA for the most instances. The average error of ICOA is 0.0057%, 0.2937%, 0.0613%, 5.8431% and 49.3940% lower than that of COA for the 30-, 48-, 52-, 76- and 100-city instances, respectively. Furthermore, the maximum, average and standard deviation objective values as well as the number of convergence generations of ICOA are also lower than those of COA. Moreover, ICOA has also achieved the optimal solution with higher quality compared to the previous approaches in literature. Consequently, ICOA is one of the methods worth considering for the TSP problem. [ABSTRACT FROM AUTHOR]

This paper proposes a new solution methodology based on a mixed-integer conic formulation to locate and size photovoltaic (PV) generation units in AC distribution networks with a radial structure. The objective function comprises the annual expected energy costs of the conventional substation in addition to the investment and operating costs of PV sources. The original optimization model that represents this problem belongs to the family of mixed-integer nonlinear programming (MINLP); however, the complexity of the power balance constraints make it difficult to find the global optimum. In order to improve the quality of the optimization model, a mixed-integer conic (MIC) formulation is proposed in this research in order to represent the studied problem. Numerical results in two test feeders composed of 33 and 69 nodes demonstrate the effectiveness of the proposed MIC model when compared to multiple metaheuristic optimizers such as the Chu and Beasley Genetic Algorithm, the Newton Metaheuristic Algorithm, the Vortex Search Algorithm, the Gradient-Based Metaheuristic Optimization Algorithm, and the Arithmetic Optimization Algorithm, among others. The final results obtained with the MIC model show improvements greater than USD 100,000 per year of operation. All simulations were run in the MATLAB programming environment, using its own scripts for all the metaheuristic algorithms and the disciplined convex tool known as CVX with the Gurobi solver in order to solve the proposed MIC model. [ABSTRACT FROM AUTHOR]

Feature selection problem is concerned with searching in a dataset for a set of features aiming to reduce the training time and enhance the accuracy of a classification method. Therefore, feature selection algorithms are proposed to choose important features from large and complex datasets. The cuckoo search (CS) algorithm is a type of natural-inspired optimization algorithms and is widely implemented to find the optimum solution for a specified problem. In this work, the cuckoo search algorithm is hybridized with a local search algorithm to find a satisfactory solution for the problem of feature selection. The great deluge (GD) algorithm is an iterative search procedure, that can accept some worse moves to find better solutions for the problem, also to increase the exploitation ability of CS. The comparison is also provided to examine the performance of the proposed method and the original CS algorithm. As result, using the UCI datasets the proposed algorithm outperforms the original algorithm and produces comparable results compared with some of the results from the literature. [ABSTRACT FROM AUTHOR]

A fundamental part of the self‐healing grid is reducing disturbances and automatically responding to problems. Toward this goal, smart grid operators would be of great use to ensure stability after a potential overload for a planning horizon, as the electrical values are unknown. To evaluate the robustness of the topology reconfiguration after a disturbance, like an overload, reliability analysis through simulation can be employed. However, the simulation approach we propose in this paper, except for the excessive time it consumes, cannot assure the quality of its solutions. To achieve a more robust configuration, we, additionally, propose a single‐stage stochastic program to optimize the grid topology configuration after a potential overload to ensure stability for the next day. We suggest a simheuristic approach based on a Variable Neighborhood Search metaheuristic to solve the above stochastic optimization problem. We evaluate the two approaches for this real‐world problem, together with Creos Luxembourg S.A., the leading grid operator in Luxembourg. We show that our method can quickly suggest countermeasures to operators facing potential overloading incidents, ensuring the smart grid's stability for the next day. [ABSTRACT FROM AUTHOR]

Over the last decade, the size and complexity of real-world problems have grown dramatically, necessitating more effective tools. Nature-inspired metaheuristic algorithms have proven to be a promising tool for solving such problems due to their performance in a variety of fields. JAYA algorithm is a novel population-based algorithm which could have been able to present reliable results. This is because it does not need any parameters to be set other than the population size and the maximum number of iteration. Despite its positive feedbacks, this algorithm should be modified to witness more efficiency. This paper aims to amend the original version of Jaya to present a high-efficiency version named Powerful Enhanced Jaya (PEJAYA). In other words, the methodology of updating position in Jaya is modified to enhance the convergence and search capabilities. This approach is assessed according to solve 20 well-known benchmark functions, feature selection, and statistical tests. The output results of proposed optimization algorithm are then evaluated by comparing it with other recent algorithms including crow search algorithm (CSA), standard version of JAYA, particle swarm optimization (PSO), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), moth-flame optimization (MFO) and sine–cosine algorithm (SCA). Solving a real-world problem is another way of checking the efficiency of this approach with other published works. Prompt escape from local minima, superior convergence, and stability demonstrate that the suggested approach is a very powerful instrument that may be employed in a variety of optimization situations. [ABSTRACT FROM AUTHOR]

Medical technological advancements have led to the creation of various large datasets with numerous attributes. The presence of redundant and irrelevant features in datasets negatively influences algorithms and leads to decreases in the performance of the algorithms. Using effective features in data mining and analyzing tasks such as classification can increase the accuracy of the results and relevant decisions made by decision-makers using them. This increase can become more acute when dealing with challenging, large-scale problems in medical applications. Nature-inspired metaheuristics show superior performance in finding optimal feature subsets in the literature. As a seminal attempt, a wrapper feature selection approach is presented on the basis of the newly proposed Aquila optimizer (AO) in this work. In this regard, the wrapper approach uses AO as a search algorithm in order to discover the most effective feature subset. S-shaped binary Aquila optimizer (SBAO) and V-shaped binary Aquila optimizer (VBAO) are two binary algorithms suggested for feature selection in medical datasets. Binary position vectors are generated utilizing S- and V-shaped transfer functions while the search space stays continuous. The suggested algorithms are compared to six recent binary optimization algorithms on seven benchmark medical datasets. In comparison to the comparative algorithms, the gained results demonstrate that using both proposed BAO variants can improve the classification accuracy on these medical datasets. The proposed algorithm is also tested on the real-dataset COVID-19. The findings testified that SBAO outperforms comparative algorithms regarding the least number of selected features with the highest accuracy. [ABSTRACT FROM AUTHOR]

Surrogate-based optimization, nature-inspired metaheuristics, and hybrid combinations have become state of the art in algorithm design for solving real-world optimization problems. Still, it is difficult for practitioners to get an overview that explains their advantages in comparison to a large number of available methods in the scope of optimization. Available taxonomies lack the embedding of current approaches in the larger context of this broad field. This article presents a taxonomy of the field, which explores and matches algorithm strategies by extracting similarities and differences in their search strategies. A particular focus lies on algorithms using surrogates, nature-inspired designs, and those created by automatic algorithm generation. The extracted features of algorithms, their main concepts, and search operators, allow us to create a set of classification indicators to distinguish between a small number of classes. The features allow a deeper understanding of components of the search strategies and further indicate the close connections between the different algorithm designs. We present intuitive analogies to explain the basic principles of the search algorithms, particularly useful for novices in this research field. Furthermore, this taxonomy allows recommendations for the applicability of the corresponding algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a novel metaheuristic optimization algorithm inspired by the geometrical features of circles, called the circle search algorithm (CSA). The circle is the most well-known geometric object, with various features including diameter, center, perimeter, and tangent lines. The ratio between the radius and the tangent line segment is the orthogonal function of the angle opposite to the orthogonal radius. This angle plays an important role in the exploration and exploitation behavior of the CSA. To evaluate the robustness of the CSA in comparison to other algorithms, many independent experiments employing 23 famous functions and 3 real engineering problems were carried out. The statistical results revealed that the CSA succeeded in achieving the minimum fitness values for 21 out of the tested 23 functions, and the p-value was less than 0.05. The results evidence that the CSA converged to the minimum results faster than the comparative algorithms. Furthermore, high-dimensional functions were used to assess the CSA's robustness, with statistical results revealing that the CSA is robust to high-dimensional problems. As a result, the proposed CSA is a promising algorithm that can be used to easily handle a wide range of optimization problems. [ABSTRACT FROM AUTHOR]

The goal of this research is to develop a high-performance fractionalized proportional--integral--derivative (FPID) controller based on Henry Gas Solubility Optimization (HGSO) for controlling the speed of a direct current (DC) motor. The suggested HGSOA-based Fractionalized PID technique with Matsuda approximation method was used to obtain the optimal FPID controller by minimising the integral of time multiplied absolute error (ITAE) as the objective function. Index of performance and disturbance rejection analyses, as well as transient and frequency responses, were all employed to validate the suggested approach's effectiveness. The proposed HGSO-FPID controller with Matsuda approximation was then compared not only to the original HGSO algorithm-tuned PID controller, but also to other controllers tuned by cutting-edge meta-heuristic algorithms such as Atom Search Optimization algorithm (ASO), Grey Wolf Optimization algorithm (GWO), Particle Swarm Optimisation (PSO), Invasive Weed Optimisation (IWO), and stochastic fractal search (SFS). The results showed that the proposed HGSOA-FPID controller has better performance with lower settling time, Ts which 0.1003 s, with lower rise time, Tr which is 0.0579 s, negligible overshoot, D which is 0.0052% and strong output disturbance rejection when compared to the performance of the other controllers. [ABSTRACT FROM AUTHOR]

Crow search algorithm (CSA) is a novel meta-heuristic optimization algorithm based on the intelligent behavior of the crow population. Although the algorithm has the characteristics of few parameters, simple structure, and easy application, it has the shortcomings of low convergence accuracy and imbalance between exploration and exploitation capabilities. The occurrence of these issues is originated from crow learning from only one goal. In this paper, an improved crow search algorithm based on oppositional forgetting learning (OFLCSA) is proposed. In order to solve the shortcomings of CSA, the forgetting mechanism is introduced to help the algorithm jump out of the local optimum. Moreover, the opposition-based learning (OBL) strategy is combined with the forgetting mechanism to increase the probability of approaching the optimal solution. In addition, the elite crow and adaptive flight length are used to improve the convergence accuracy. To verify the performance of OFLCSA, experiments were conducted on the Congress on Evolutionary Computation (CEC) 2014 and CEC 2019 benchmark functions. OFLCSA is compared with the ten state-of-the-art meta-heuristic optimization algorithms. Moreover, OFLCSA is evaluated by four real-world engineering applications. Experimental results and analysis show that OFLCSA is a competitive meta-heuristic optimization algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a novel metaheuristic optimizer, named Colony Search Optimization Algorithm (CSOA). The algorithm mimics the social behavior of early humans. Early humans expanded their settlements in search of more livable places to live. In CSOA, the worst solution is used to escape from local optima. And the number of these redundant solutions' updates is reduced to improve the performance of the algorithm. CSOA is tested with 26 mathematical optimization problems and 4 classical engineering optimization problems. The optimization results are compared with those of various optimization algorithms. The experimental results show that the CSOA is able to provide very competitive results on most of the tested problems. Then, a new effective method is provided for solving optimization problems. [ABSTRACT FROM AUTHOR]

The relevance of the optimal design of water distribution networks lies in its applicability. Thus, the design must be as efficient and affordable as possible, meaning that it achieves a minimum level of serviceability using an appropriate amount of resources. In this context, an ideal water network requires the use of minimum-size elements to reach the minimum head pressure required for each node of the system with the lowest energy consumption. For this purpose, a relatively new meta-heuristic algorithm, called Harmony Search, was used in this study to optimize the water distribution network of Cheongna International City, Korea, in terms of the pipe diameter. Furthermore, using the same algorithm under a different approach, an assessment of the deterioration of conduits over time was conducted by analyzing the optimal roughness coefficient for the original pipe material, which provides a useful tool for decision making. Other heuristic and meta-heuristic algorithms have been applied to this type of problem; however, Harmony Search provides a convenient implementation at a reasonable computational cost. In this study, Harmony Search is demonstrated to be a valuable tool for water distribution network optimization as well as for pipe aging assessment. [ABSTRACT FROM AUTHOR]

The hypercube optimization search (HOS) approach is a new efficient and robust metaheuristic algorithm that simulates the dove's movement in quest of new food sites in nature, utilizing hypercubes to depict the search zones. In medical informatics, the classification of medical data is one of the most challenging tasks because of the uncertainty and nature of healthcare data. This paper proposes the use of the HOS algorithm for training multilayer perceptrons (MLP), one of the most extensively used neural networks (NNs), to enhance its efficacy as a decision support tool for medical data classification. The proposed HOS-MLP model is tested on four significant medical datasets: orthopedic patients, diabetes, coronary heart disease, and breast cancer, to assess HOS's success in training MLP. For verification, the results are compared with eleven different classifiers and eight well-regarded MLP trainer metaheuristic algorithms: particle swarm optimization (PSO), biogeography-based optimizer (BBO), the firefly algorithm (FFA), artificial bee colony (ABC), genetic algorithm (GA), bat algorithm (BAT), monarch butterfly optimizer (MBO), and the flower pollination algorithm (FPA). The experimental results demonstrate that the MLP trained by HOS outperforms the other comparative models regarding mean square error (MSE), classification accuracy, and convergence rate. The findings also reveal that the HOS help the MLP to produce more accurate results than other classification algorithms for the prediction of diseases. [ABSTRACT FROM AUTHOR]

In recent times, the Internet of Medical Things (IoMT) is a new loomed technology, which has been deliberated as a promising technology designed for various and broadly connected networks. In an intelligent healthcare system, the framework of IoMT observes the health circumstances of the patients dynamically and responds to backings their needs, which helps detect the symptoms of critical rare body conditions based on the data collected. Metaheuristic algorithms have proven effective, robust, and efficient in deciphering real-world optimization, clustering, forecasting, classification, and other engineering problems. The emergence of extraordinary, very large-scale data being generated from various sources such as the web, sensors, and social media has led the world to the era of big data. Big data poses a new contest to metaheuristic algorithms. So, this research work presents the metaheuristic optimization algorithm for big data analysis in the IoMT using gravitational search optimization algorithm (GSOA) and reflective belief network with convolutional neural networks (DBN-CNNs). Here the data optimization has been carried out using GSOA for the collected input data. The input data were collected for the diabetes prediction with cardiac risk prediction based on the damage in blood vessels and cardiac nerves. Collected data have been classified to predict abnormal and normal diabetes range, and based on this range, the risk for a cardiac attack has been predicted using SVM. The performance analysis is made to reveal that GSOA-DBN_CNN performs well in predicting diseases. The simulation results illustrate that the GSOA-DBN_CNN model used for prediction improves accuracy, precision, recall, F1-score, and PSNR. [ABSTRACT FROM AUTHOR]

This article has four main objectives. These are: to develop the dynamic fitness-distance balance (dFDB) selection method for meta-heuristic search algorithms, to develop a strong optimization algorithm using the dFDB method, to create an optimization model of the coordination of directional overcurrent relays (DOCRs) problem, and to optimize the DOCRs problem using the developed algorithm, respectively. A comprehensive experimental study was conducted to analyze the performance of the developed dFDB selection method and to evaluate the optimization results of the DOCRs problem. Experimental studies were carried out in two steps. In the first step, to test the performance of the developed dFDB method and optimization algorithm, studies were conducted on three different benchmark test suites consisting of different problem types and dimensions. The data obtained from the experimental studies were analyzed using non-parametric statistical methods and the most effective among the developed optimization algorithms was determined. In the second step, the DOCRs problem was optimized using the developed algorithm. The performance of the proposed method for the solution to the DOCRs coordination problem was evaluated on five test systems including the IEEE 3-bus, the IEEE 4-bus, the 8-bus, the 9-bus, and the IEEE 30-bus test systems. The numerical results of the developed algorithm were compared with previously proposed algorithms available in the literature. Simulation results showed the effectiveness of the proposed method in minimizing the relay operating time for the optimal coordination of DOCRs. [ABSTRACT FROM AUTHOR]

Optimization techniques are widely used to solve variety of problems related to the fields of engineering, statistics, finance, etc. In this article, a new optimization algorithm named criminal search optimization algorithm (CSOA) has been proposed. This proposed algorithm is inspired by policemen and replicates the strategies and intelligence used by a team of the policemen to catch a criminal for a crime. The performance of CSOA is validated using two suites of benchmark functions (CEC-2005 and CEC-2020). Further, the proposed method is used to solve a multi-objective real-world optimization problem, i.e. a combined emission economic dispatch problem. To evaluate the performance of the proposed method, five test cases have been considered in this study. The results obtained are compared with other existing well-known optimization methods to show the superiority of the proposed CSOA method. [ABSTRACT FROM AUTHOR]

The optimization of industrial processes is a critical task for leveraging profitability and sustainability. To ensure the selection of optimum process parameter levels in any industrial process, numerous metaheuristic algorithms have been proposed so far. However, many algorithms are either computationally too expensive or become trapped in the pit of local optima. To counter these challenges, in this paper, a hybrid metaheuristic called PSO-GSA is employed that works by combining the iterative improvement capability of particle swarm optimization (PSO) and gravitational search algorithm (GSA). A binary PSO is also fused with GSA to develop a BPSO-GSA algorithm. Both the hybrid algorithms i.e., PSO-GSA and BPSO-GSA, are compared against traditional algorithms, such as tabu search (TS), genetic algorithm (GA), differential evolution (DE), GSA and PSO algorithms. Moreover, another popular hybrid algorithm DE-GA is also used for comparison. Since earlier works have already studied the performance of these algorithms on mathematical benchmark functions, in this paper, two real-world-applicable independent case studies on biodiesel production are considered. Based on the extensive comparisons, significantly better solutions are observed in the PSO-GSA algorithm as compared to the traditional algorithms. The outcomes of this work will be beneficial to similar studies that rely on polynomial models. [ABSTRACT FROM AUTHOR]