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This paper presents a novel bio-inspired metaheuristic algorithm termed the Fossa Optimization Algorithm (FOA), which emulates the natural hunting behavior of the fossa in its habitat. FOA draws its core inspiration from the two-stage hunting technique of the fossa, involving an initial attack on a spotted lemur followed by a pursuit through the trees. The theoretical framework of FOA is elaborated, and its implementation is mathematically modeled in two distinct phases: (i) exploration, which simulates the fossa's positional adjustments during the initial attack on the lemur, and (ii) exploitation, which models the positional changes of the fossa during the chase. The efficacy of FOA is tested against twenty-two constrained optimization problems from the CEC 2011 test suite, as well as four engineering design challenges. The optimization results demonstrate FOA's strong capabilities in both exploration and exploitation, maintaining a balance that facilitates convergence to optimal solutions. FOA's performance is benchmarked against twelve established algorithms, showing that it consistently outperforms its competitors by delivering superior results and ranking as the top optimizer in most of the evaluated functions. These findings indicate that FOA is highly effective in addressing optimization tasks in real-world scenarios. [ABSTRACT FROM AUTHOR]

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]

This study introduces the CP-EODE algorithm, a novel hybrid of the Equilibrium Optimizer (EO), and the Differential Evolution (DE) algorithm. It addresses EO's tendency toward premature convergence by enhancing its exploration capabilities. The motivation for this research stems from the need for more efficient and economically viable designs in engineering, particularly in the optimization of shell-and-tube heat exchangers (STHEs) and photovoltaic module parameters. The CP-EODE algorithm leverages DE's exploration strengths to enhance EO's performance adopting a cooperative parallel approach that divides the population into EO and DE sub-populations for a more effective search space exploration. The validation of CP-EODE's effectiveness commenced with its application to 29 benchmark functions, where a Friedman test yielded an F r value of 22.19, significantly surpassing the critical value of 7.81, and a Wilcoxon signed rank test confirmed statistical improvements with a z-score beyond − 1.96. Four quality metrics were discussed to provide a detailed assessment of CP-EODE's performance highlighting its robust exploration and exploitation capabilities. In practical applications, CP-EODE demonstrated significant advancements in STHE design achieving up to 19.706% reduction in heat exchanger surface area compared to traditional and other algorithmic designs, showcasing its potential to enhance engineering design efficiency and cost-effectiveness. A critical examination of the algorithm's convergence rate revealed its swift optimization capability, with rapid attainment of optimal solutions in various test cases, further establishing CP-EODE's advantage in handling complex optimization challenges. Through rigorous statistical analysis and performance metrics, CP-EODE emerges as a superior solution for complex optimization challenges, underscoring the value of hybrid algorithmic strategies in engineering optimizations. [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]

The forensic-based investigation (FBI) is a metaheuristic algorithm inspired by the criminal investigation process. The collaborative efforts of the investigation and pursuit teams demonstrate the FBI's involvement during the exploitation and exploration phases. When choosing the promising population, the FBI algorithm's population selection technique focuses on the same region. This research aims to propose a dynamic population selection method for the original FBI and thereby enhance its convergence performance. To achieve this objective, the FBI may employ dynamic oppositional learning (DOL), a dynamic version of the oppositional learning methodology, to dynamically navigate to local minima in various locations. Therefore, the proposed advanced method is named DOLFBI. The performance of DOLFBI on the CEC2019 and CEC2022 benchmark functions is evaluated by comparing it with several other popular metaheuristics in the literature. As a result, DOLFBI yielded the lowest fitness value in 18 of 22 benchmark problems. Furthermore, DOLFBI has shown promising results in solving real-world engineering problems. It can be argued that DOLFBI exhibits the best convergence performance in cantilever beam design, speed reducer, and tension/compression problems. DOLFBI is often utilized in truss engineering difficulties to determine the minimal weight. Its success is comparable to other competitive MAs in the literature. The Wilcoxon signed-rank and Friedman rank tests further confirmed the study's stability. Convergence and trajectory analyses validate the superior convergence concept of the proposed method. When the proposed study is compared to essential and enhanced MAs, the results show that DOLFBI has a competitive framework for addressing complex optimization problems due to its robust convergence ability compared to other optimization techniques. As a result, DOLFBI is expected to achieve significant success in various optimization challenges, feature selection, and other complex engineering or real-world problems. [ABSTRACT FROM AUTHOR]

This study presents the Hybrid COASaDE Optimizer, a novel combination of the Crayfish Optimization Algorithm (COA) and Self-adaptive Differential Evolution (SaDE), designed to address complex optimization challenges and solve engineering design problems. The hybrid approach leverages COA's efficient exploration mechanisms, inspired by crayfish behaviour, with the symmetry of SaDE's adaptive exploitation capabilities, characterized by its dynamic parameter adjustment. The balance between these two phases represents a symmetrical relationship wherein both components contribute equally and complementary to the algorithm's overall performance. This symmetry in design enables the Hybrid COASaDE to maintain consistent and robust performance across a diverse range of optimization problems. Experimental evaluations were conducted using CEC2022 and CEC2017 benchmark functions, demonstrating COASaDE's superior performance compared to state-of-the-art optimization algorithms. The results and statistical analyses confirm the robustness and efficiency of the Hybrid COASaDE in finding optimal solutions. Furthermore, the applicability of the Hybrid COASaDE was validated through several engineering design problems, where COASaDE outperformed other optimizers in achieving the optimal solution. [ABSTRACT FROM AUTHOR]

Swarm intelligence (SI) algorithms represent a class of Artificial Intelligence (AI) optimization metaheuristics used for solving complex optimization problems. However, a key challenge in solving complex problems is maintaining the balance between exploration and exploitation to find the optimal global solution and avoid local minima. This paper proposes an innovative Swarm Intelligence (SI) algorithm called the Four Vector Intelligent Metaheuristic (FVIM) to address the aforementioned problem. FVIM's search strategy is guided by four top-performing leaders within a swarm, ensuring a balanced exploration-exploitation trade-off in the search space, avoiding local minima, and mitigating low convergence issues. The efficacy of FVIM is evaluated through extensive experiments conducted over two datasets, incorporating both qualitative and quantitative statistical measurements. One dataset contains twenty-three well-known single-objective optimization functions, such as fixed-dimensional and multi-modal functions, while the other dataset comprises the CEC2017 functions. Additionally, the Wilcoxon test was computed to validate the result's significance. The results illustrate FVIM's effectiveness in addressing diverse optimization challenges. Moreover, FVIM has been successfully applied to tackle engineering design problems, such as weld beam and truss engineering design. [ABSTRACT FROM AUTHOR]

The base connection is flexible, not fully pinned/fixed, implying a nonlinear moment–rotation relationship. This deviates from a linear response, where rotation is not directly proportional to the applied moment. Numerical investigations using the commercial software ABAQUS were conducted to analyze the steel base plate connections. The finite element (FE) models were verified against previous experimental results. Moreover, numerical findings of a comprehensive parametric investigation were conducted. The studied connections were examined with different configurations, including variations in the diameter, spacing, and number of the anchor bolts; the thickness of the base plate; and the applied axial force. The current study aims to use numerical results combined with the whale optimization algorithm (WOA) and classical genetic algorithm (GA) to derive a formulation for the moment–rotation (M-θr) relationship. The distinctive aspect of this formulation is that it aims to simulate the nonlinear rotational behavior exhibited by flexible base connections under combined moment and tension loads, while also considering various parameters such as bolt number/diameter and plate thickness. The findings indicate that the WOA is capable of obtaining an optimal equation for accurately simulating the M-Ɵr relationship. This underscores the ability of the WOA to effectively address the complexity of the problem and provide a reliable equation for predicting the rotational behavior of such connections. Consequently, the WOA method can be utilized to calculate the rotational stiffness at H/150, offering valuable support for engineering design processes. [ABSTRACT FROM AUTHOR]

In the recent years, there are a lot of swarm-based stochastic optimization techniques i.e., metaheuristics were introduced. Most of these techniques were tested to solve the sets of theoretical functions. Some of them were enriched with practical tests where the common use cases are the mechanical engineering designs. On the other hand, the similar studies that utilized the optimization in power system are difficult to find. Moreover, the environmental issues become major considerations in engineering field. Based on this evidence, this paper constructs a new swarmbased optimization technique called stay-jump optimizer (SJO). The equal size swarm split is applied in the beginning of the process. Then, two directed searches toward the highest quality sub-swam members and two randomly selected higher quality sub swarm members are employed. The performance investigation is performed by employing SJO to find the optimal solution of 23 classic functions and the economic emission dispatch (EED) problem. The use-case for EED is the Java-Bali power grid system in Indonesia that consists of six steam power plants and two hydro-electric power plants. Five new optimization techniques including addax optimization algorithm (AOA), dollmaker optimization algorithm (DOA), giant armadillo optimization algorithm (GAO), zebra optimization algorithm (ZOA), and total interaction algorithm (TIA). The result shows that SJO is superior to its opponents as it is better than AOA, DOA, GAO, ZOA, and TIA in 17, 17, 16, 19, and 14 functions out of 23 functions respectively. SJO also becomes the best in the EED problem. [ABSTRACT FROM AUTHOR]

In this paper, a new human-based metaheuristic algorithm called Carpet Weaving Optimization (CWO) is introduced, which is inspired by human skills when weaving a carpet. The main source of inspiration in the design of CWO is taken from the communication between the carpet weaver and the map reader who try to weave a carpet according to the given pattern. The theory of CWO is stated and then mathematically modeled based on the simulation of the carpet weaving process. The effectiveness of CWO in optimization has been assessed across twenty-three standard benchmark functions encompassing unimodal, high-dimensional multimodal, and fixed-dimensional multimodal categories. The optimization outcomes underscore CWO's capability to yield favorable results across various optimization challenges, adeptly navigating between exploration, exploitation, and achieving a balanced search process. Comparative analysis against twelve rival algorithms demonstrates CWO's superior performance, consistently outshining competitors across all twenty-three benchmark functions and securing the top rank as the premier optimizer. Moreover, the efficacy of CWO in real-world applications has been scrutinized through its optimization of four engineering design quandaries. Simulation findings corroborate CWO's commendable performance in real-world and engineering contexts, as evidenced by its capacity to deliver superior values for design variables and objective functions compared to competing algorithms. [ABSTRACT FROM AUTHOR]

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems. This approach aims to leverage the strengths of multiple algorithms, enhancing solution quality, convergence speed, and robustness, thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks. In this paper, we introduce a hybrid algorithm that amalgamates three distinct metaheuristics: the Beluga Whale Optimization (BWO), the Honey Badger Algorithm (HBA), and the Jellyfish Search (JS) optimizer. The proposed hybrid algorithm will be referred to as BHJO. Through this fusion, the BHJO algorithm aims to leverage the strengths of each optimizer. Before this hybridization, we thoroughly examined the exploration and exploitation capabilities of the BWO, HBA, and JS metaheuristics, as well as their ability to strike a balance between exploration and exploitation. This meticulous analysis allowed us to identify the pros and cons of each algorithm, enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance. In addition, the BHJO algorithm incorporates Opposition-Based Learning (OBL) to harness the advantages offered by this technique, leveraging its diverse exploration, accelerated convergence, and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm. Moreover, the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems, providing a comprehensive assessment of its efficacy and applicability in diverse problem domains. Similarly, the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms, where mean and standard deviation values were utilized as evaluation metrics. This rigorous comparison aimed to assess the performance of the BHJO algorithm about its counterparts, shedding light on its effectiveness and reliability in solving optimization problems. Finally, the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn's test. The resulting numerical values revealed the BHJO algorithm's competitiveness in tackling intricate optimization problems, affirming its capability to deliver favorable outcomes in challenging scenarios. [ABSTRACT FROM AUTHOR]

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

This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the unique hunting behavior of frilled lizards in their natural habitat. FLO draws its inspiration from the sit-and-wait hunting strategy of these lizards. The algorithm's core principles are meticulously detailed and mathematically structured into two distinct phases: (i) an exploration phase, which mimics the lizard's sudden attack on its prey, and (ii) an exploitation phase, which simulates the lizard's retreat to the treetops after feeding. To assess FLO's efficacy in addressing optimization problems, its performance is rigorously tested on fifty-two standard benchmark functions. These functions include unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions, as well as the challenging CEC 2017 test suite. FLO's performance is benchmarked against twelve established metaheuristic algorithms, providing a comprehensive comparative analysis. The simulation results demonstrate that FLO excels in both exploration and exploitation, effectively balancing these two critical aspects throughout the search process. This balanced approach enables FLO to outperform several competing algorithms in numerous test cases. Additionally, FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems, further validating its robustness and versatility in solving real-world optimization challenges. Overall, the study highlights FLO's superior performance and its potential as a powerful tool for tackling a wide range of optimization problems. [ABSTRACT FROM AUTHOR]

The hunger games search algorithm (HGS) is a newly proposed metaheuristic algorithm that emulates hunger-driven foraging behaviors in a population. It combines fitness values to determine individual weights and updates them based on fitness value size, resulting in high adaptability and effective optimization. However, HGS faces issues like low convergence accuracy and susceptibility to local optima in complex optimization problems. To address these problems, an improved version called BDFXHGS is introduced. BDFXHGS incorporates a collaborative feeding strategy based on HGS's design advantages. Individuals approach others based on hunger degree, facilitating information exchange and resolving convergence and accuracy issues. BDFXHGS combines a disperse foraging strategy and a directional crossover strategy to enhance exploration and convergence speed. The paper conducts qualitative analysis and ablation experiments to examine the effectiveness of the strategies. Comparative experiments are performed using IEEE CEC 2017 benchmark functions to compare BDFXHGS with competitive algorithms, including previous champion algorithms in different dimensions. Additionally, BDFXHGS is evaluated on 25 constrained optimization problems from the IEEE CEC 2020 competition and five real engineering optimization problems. Experimental results show that BDFXHGS performs well on benchmarks and outperforms other algorithms in real-world applications. [ABSTRACT FROM AUTHOR]

This paper introduces the election optimization algorithm (EOA), a meta-heuristic approach for engineering optimization problems. Inspired by the democratic electoral system, focusing on the presidential election, EOA emulates the complete election process to optimize solutions. By simulating the presidential election, EOA introduces a novel position-tracking strategy that expands the scope of effectively solvable problems, surpassing conventional human-based algorithms, specifically, the political optimizer. EOA incorporates explicit behaviors observed during elections, including the party nomination and presidential election. During the party nomination, the search space is broadened to avoid local optima by integrating diverse strategies and suggestions from within the party. In the presidential election, adequate population diversity is maintained in later stages through further campaigning between elite candidates elected within the party. To establish a benchmark for comparison, EOA is rigorously assessed against several renowned and widely recognized algorithms in the field of optimization. EOA demonstrates superior performance in terms of average values and standard deviations across the twenty-three standard test functions and CEC2019. Through rigorous statistical analysis using the Wilcoxon rank-sum test at a significance level of 0.05, experimental results indicate that EOA consistently delivers high-quality solutions compared to the other benchmark algorithms. Moreover, the practical applicability of EOA is assessed by solving six complex engineering design problems, demonstrating its effectiveness in real-world scenarios. [ABSTRACT FROM AUTHOR]

The Fennec Fox algorithm (FFA) is a new meta-heuristic algorithm that is primarily inspired by the Fennec fox's ability to dig and escape from wild predators. Compared with other classical algorithms, FFA shows strong competitiveness. The "No free lunch" theorem shows that an algorithm has different effects in the face of different problems, such as: when solving high-dimensional or more complex applications, there are challenges such as easily falling into local optimal and slow convergence speed. To solve this problem with FFA, in this paper, an improved Fenna fox algorithm DEMFFA is proposed by adding sin chaotic mapping, formula factor adjustment, Cauchy operator mutation, and differential evolution mutation strategies. Firstly, a sin chaotic mapping strategy is added in the initialization stage to make the population distribution more uniform, thus speeding up the algorithm convergence speed. Secondly, in order to expedite the convergence speed of the algorithm, adjustments are made to the factors of the formula whose position is updated in the first stage, resulting in faster convergence. Finally, in order to prevent the algorithm from getting into the local optimal too early and expand the search space of the population, the Cauchy operator mutation strategy and differential evolution mutation strategy are added after the first and second stages of the original algorithm update. In order to verify the performance of the proposed DEMFFA, qualitative analysis is carried out on different test sets, and the proposed algorithm is tested with the original FFA, other classical algorithms, improved algorithms, and newly proposed algorithms on three different test sets. And we also carried out a qualitative analysis of the CEC2020. In addition, DEMFFA is applied to 10 practical engineering design problems and a complex 24-bar truss topology optimization problem, and the results show that the DEMFFA algorithm has the potential to solve complex problems. [ABSTRACT FROM AUTHOR]

In this article, a new human-based metaheuristic algorithm named Dollmaker Optimization Algorithm (DOA) is introduced, which imitates the strategy and skill of the dollmaker when making dolls. The basic inspiration of DOA is derived from two natural behaviors in the doll making process (i) making general changes to the doll-making materials and (ii) making precise small changes on the appearance characteristics of the dolls. The theory of DOA is proposed and then modeled mathematically in two phases (i) exploration based on the simulation of large changes made on doll-making materials and (ii) exploitation based on the simulation of small changes on the made dolls. The performance of DOA in optimization is evaluated on twenty-three standard benchmark functions of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types. The optimization results show that DOA has achieved suitable results for optimization problems with its ability in exploration, exploitation, and balance them during the search process. Comparison of DOA results with twelve competing algorithms shows that the proposed algorithm has superior performance compared to competing algorithms by providing better results in all twenty-three benchmark functions and getting the rank of the first best optimizer. In addition, the efficiency of DOA in handling real-world applications is evaluated in the optimization of four engineering design problems. Simulation results show that DOA has acceptable performance in real world and engineering applications by providing better values for design variables and objective functions compared to competing algorithms. [ABSTRACT FROM AUTHOR]

One of the most important tasks in handling real-world global optimization problems is to achieve a balance between exploration and exploitation in any nature-inspired optimization method. As a result, the search agents of an algorithm constantly strive to investigate the unexplored regions of a search space. Aquila Optimizer (AO) is a recent addition to the field of metaheuristics that finds the solution to an optimization problem using the hunting behavior of Aquila. However, in some cases, AO skips the true solutions and is trapped at sub-optimal solutions. These problems lead to premature convergence (stagnation), which is harmful in determining the global optima. Therefore, to solve the above-mentioned problem, the present study aims to establish comparatively better synergy between exploration and exploitation and to escape from local stagnation in AO. In this direction, firstly, the exploration ability of AO is improved by integrating Dynamic Random Walk (DRW), and, secondly, the balance between exploration and exploitation is maintained through Dynamic Oppositional Learning (DOL). Due to its dynamic search space and low complexity, the DOL-inspired DRW technique is more computationally efficient and has higher exploration potential for convergence to the best optimum. This allows the algorithm to be improved even further and prevents premature convergence. The proposed algorithm is named DAO. A well-known set of CEC2017 and CEC2019 benchmark functions as well as three engineering problems are used for the performance evaluation. The superior ability of the proposed DAO is demonstrated by the examination of the numerical data produced and its comparison with existing metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

As a swarm-based metaheuristic algorithm, the crow search algorithm (CSA) has attracted a lot of attention owing to its simplicity and flexibility. However, CSA tends to have low efficiency. To improve the optimization efficiency, this article proposes a modified version of CSA based on group strategy with an adaptive mechanism (GCSA). On this basis, crows are divided into multiple competing groups, and are assigned different roles and statuses. Then, the group strategy including different search modes is implemented to increase the solution diversity and search efficiency. Moreover, benefiting from the adaptive mechanism, the search range of crows changes in different stages to balance exploration and exploitation capabilities. To evaluate the performance of the proposed algorithm, 35 benchmark test functions (including 10 CEC2020 functions) and three engineering design problems are solved by GCSA and 11 other algorithms. The results prove that GCSA generally provides more competitive results than other metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Harris Hawks Optimization (HHO) is a novel meta-heuristic optimization algorithm. The algorithm is inspired by the behavior of Harris Hawks collaborating with each other to pursue prey in nature. The algorithm has the advantages of simple structure, fewer parameters, easy implementation, and excellent performance on high-dimensional problems. However, the algorithm also suffers from the inability to strike a good balance between exploration and exploitation, low convergence accuracy, and slow convergence speed in the early stage. In response to these defects, this paper will introduce three strategies to the HHO: a non-negative stochastic shrinkage exponential energy function, a Cauchy-Gaussian-based dynamic variance reduction selection strategy, and a greedy-difference-based selection strategy. The improved algorithm TSHHO is analyzed on the well-established 28 benchmark test functions, and four industrial engineering design problems. The experimental results show that the TSHHO algorithm proposed in this paper can achieve a better balance in the exploration and development stages,the strategies significantly improve the search efficiency, convergence accuracy, and robustness of the algorithm. [ABSTRACT FROM AUTHOR]

Structural engineering is a field with several optimization approaches. Concordantly, the goal in optimum design is the generation of the lightest, cheapest, or most sustainable engineering design for any type of structure or structural member. In this study, some optimization approaches were presented for structures including 25 and 72-bar space trusses. To find the smallest weights were ensured for both structures. Optimization approaches related to metaheuristics and modifications of them were developed by inspired by features of nature, abilities of lives and mechanisms of chemical/physical processes. The employed three techniques that are flower pollination algorithm, Jaya algorithm and the hybrid algorithm developed via them were compared to each other through some parameters by calculating different statistical expressions containing the best, average and standard deviation of the objective function. Also, the performance of whole metaheuristic versions is evaluated with some literature researches to find the most effective one to minimize the structural weights. These results have observed that the all versions of proposed metaheuristic algorithms are generally successful, and effective to minimize the objective function comparison to algorithms used in previous studies. Moreover, the best values of objective functions can be decreased at the 0.02–0.06 rates as minimum and maximum compared to the previous studies and algorithms, respectively. [ABSTRACT FROM AUTHOR]

Supply Chain (SC) Optimization is a key activity in today's industry with the goal of increasing operational efficiency, reducing costs, and improving customer satisfaction. Traditional optimization methods often struggle to effectively use resources while handling complex and dynamic Supply chain networks. This paper introduces a novel biomimetic metaheuristic algorithm called the Wombat Optimization Algorithm (WOA) for supply chain optimization. This algorithm replicates the natural behaviors observed in wombats living in the wild, particularly focusing on their foraging tactics and evasive maneuvers towards predators. The theory of WOA is described and then mathematically modeled in two phases: (i) exploration based on the simulation of wombat movements during foraging and trying to find food and (ii) exploitation based on simulating wombat movements when diving towards nearby tunnels to defend against its predators. The effectiveness of WOA in addressing optimization challenges is assessed by handling the CEC 2017 test suite across various problem dimensions, including 10, 30, 50, and 100. The findings of the optimization indicate that WOA demonstrates a strong ability to effectively manage exploration and exploitation, and maintains a balance between them throughout the search phase to deliver optimal solutions for optimization problems. A total of twelve well-known metaheuristic algorithms are called upon to test their performance against WOA in the optimization process. The outcomes of the simulations reveal that WOA outperforms the other algorithms, achieving superior results across most benchmark functions and securing the top ranking as the most efficient optimizer. Using a Wilcoxon rank sum test statistical analysis, it has been proven that WOA outperforms other algorithms significantly. WOA is put to the test with twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems to showcase its ability to solve real-world optimization problems. The results of the simulations demonstrate that WOA excels in real-world applications by delivering superior solutions and outperforming its competitors. [ABSTRACT FROM AUTHOR]

This study presents an advanced metaheuristic approach termed the Enhanced Gorilla Troops Optimizer (EGTO), which builds upon the Marine Predators Algorithm (MPA) to enhance the search capabilities of the Gorilla Troops Optimizer (GTO). Like numerous other metaheuristic algorithms, the GTO encounters difficulties in preserving convergence accuracy and stability, notably when tackling intricate and adaptable optimization problems, especially when compared to more advanced optimization techniques. Addressing these challenges and aiming for improved performance, this paper proposes the EGTO, integrating high and low-velocity ratios inspired by the MPA. The EGTO technique effectively balances exploration and exploitation phases, achieving impressive results by utilizing fewer parameters and operations. Evaluation on a diverse array of benchmark functions, comprising 23 established functions and ten complex ones from the CEC2019 benchmark, highlights its performance. Comparative analysis against established optimization techniques reveals EGTO's superiority, consistently outperforming its counterparts such as tuna swarm optimization, grey wolf optimizer, gradient based optimizer, artificial rabbits optimization algorithm, pelican optimization algorithm, Runge Kutta optimization algorithm (RUN), and original GTO algorithms across various test functions. Furthermore, EGTO's efficacy extends to addressing seven challenging engineering design problems, encompassing three-bar truss design, compression spring design, pressure vessel design, cantilever beam design, welded beam design, speed reducer design, and gear train design. The results showcase EGTO's robust convergence rate, its adeptness in locating local/global optima, and its supremacy over alternative methodologies explored. [ABSTRACT FROM AUTHOR]

The present work proposes a comprehensive metaheuristic methodology for the development of a medical robot for the upper limb rehabilitation, which includes the topological optimization of the device, kinematic models (5 DOF), human–robot interface, control and experimental tests. This methodology applies two cutting-edge triads: (1) the three points of view in engineering design (client, designer and community) and (2) the triad formed by three pillars of Industry 4.0 (autonomous machines and systems, additive manufacturing and simulation of virtual environments). By applying the proposed procedure, a robotic mechanism was obtained with a reduction of more than 40% of its initial weight and a human–robot interface with three modes of operation and a biomechanically viable kinematic model for humans. The digital twin instance and its evaluation through therapeutic routines with and without disturbances was assessed; the average RMSEs obtained were 0.08 rad and 0.11 rad, respectively. The proposed methodology is applicable to any medical robot, providing a versatile and effective solution for optimizing the design and development of healthcare devices. It adopts an innovative and scalable approach to enhance their processes. [ABSTRACT FROM AUTHOR]

Honey badger algorithm (HBA) is a recent swarm-based metaheuristic algorithm that excels in simplicity and high exploitation capability. However, it suffers from some limitations including weak exploration capacity and an imbalance between exploration and exploitation. In this paper, an improved honey badger algorithm called ODEHBA is proposed to improve the performance of basic HBA. Firstly, an improved orthogonal opposition-based learning technique is employed to assist population in escaping local optimum. Secondly, differential evolution is utilized to ensure the enrichment of population diversity and to enhance convergence speed. Finally, the exploration capability of ODEHBA is boosted by an equilibrium pool strategy. To validate the efficacy of proposed ODEHBA, it is compared with 13 well-known metaheuristic algorithms on CEC2022 benchmark test sets. Friedman test and Wilcoxon rank-sum test are utilized to assess the performance of ODEHBA. Furthermore, three engineering design problems and Internet of Vehicles (IoV) routing problem are applied to validate the capability of ODEHBA. The simulation results demonstrate that ODEHBA excels in solving complex numerical problems, engineering design, and IoV routing problems. This holds significant practical implications for cost reduction and improved resource utilization. [ABSTRACT FROM AUTHOR]

This paper proposes a novel nature-inspired swarm-based optimization algorithm called elk herd optimizer (EHO). It is inspired by the breeding process of the elk herd. Elks have two main breeding seasons: rutting and calving. In the rutting season, the elk herd splits into different families of various sizes. This division is based on fighting for dominance between bulls, where the stronger bull can form a family with large numbers of harems. In the calving season, each family breeds new calves from its bull and harems. This inspiration is set in an optimization context where the optimization loop consists of three operators: rutting season, calving season, and selection season. During the selection season, all families are merged, including bulls, harems, and calves. The fittest elk herd will be selected for use in the upcoming rutting and calving seasons. In simple words, EHO divides the population into a set of groups, each with one leader and several followers in the rutting season. The number of followers is determined based on the fitness value of its leader group. Each group will generate new solutions based on its leader and followers in the calving season. The members of all groups including leaders, followers, and new solutions are combined and the fittest population is selected in the selection season. The performance of EHO is assessed using 29 benchmark optimization problems utilized in the CEC-2017 special sessions on real-parameter optimization and four traditional real-world engineering design problems. The comparative results were conducted against ten well-established metaheuristic algorithms and showed that the proposed EHO yielded the best results for almost all the benchmark functions used. Statistical testing using Friedman's test post-hocked by Holm's test function confirms the superiority of the proposed EHO when compared to other methods. In a nutshell, EHO is an efficient nature-inspired swarm-based optimization algorithm that can be used to tackle several optimization problems. [ABSTRACT FROM AUTHOR]

As a nature-inspired metaheuristic method, the firefly algorithm (FA) arises more attentions in academic and engineering fields. However, too much attraction in FA's global attraction model leads to low computational efficiency, and the stochastic model with fixed randomization parameter is hard to balance the exploitation and exploration of the algorithm. Thus, FA still needs improvement to deal with complex engineering problems. An integrated firefly algorithm (IFA) that combines two novel attractive models with a new stochastic model is proposed to improve the standard FA. Firstly, the attractive model and stochastic model of standard FA are investigated through theoretical analysis and numerical experiments. And the factors that affect the computational efficiency and accuracy of FA are revealed. Based on the analysis results, two new fitness-based update formulas for attractiveness parameter are constructed to avoid the invalidation. The proposed virtual attractive model and global best attractive model can reduce the computation complexity and enhance the exploitation ability. Moreover, an adaptive strategy is presented for the stochastic model to achieve a better balance between exploitation and exploration. The nonlinearly decreased model for the update of parameter α can adjust the population diversity through the iteration and ensure the convergence. Additionally, an adaptive penalty function method is developed to handle the constraints effectively. Then, the initial parameters are tested, and the best initial parameters corresponding to the optimal performance of IFA are obtained. The proposed algorithm is evaluated by CEC2015 hybrid composition and a set of classical functions. The numerical experimental results show that the proposed techniques can enhance the solution accuracy and accelerate the convergence speed. Finally, IFA and other metaheuristic algorithms are applied to solve five engineering design optimization problems with mixed variables and multiple constraint conditions. The results indicate that IFA with adaptive penalty function needs fewer fitness evaluations and costs less computational time to obtain the optimal solutions. Furthermore, it exhibits better accuracy and robustness than other algorithms. [ABSTRACT FROM AUTHOR]

This review aims to exploit a study on different benchmark test functions used to evaluate the performance of Meta-Heuristic (MH) optimization techniques. The performance of the MH optimization techniques is evaluated with the different sets of mathematical benchmark test functions and various real-world engineering design problems. These benchmark test functions can help to identify the strengths and weaknesses of newly proposed MH optimization techniques. This review paper presents 215 mathematical test functions, including mathematical equations, characteristics, search space and global minima of the objective function and 57 real-world engineering design problems, including mathematical equations, constraints, and boundary conditions of the objective functions carried out from the literature. The MATLAB code references for mathematical benchmark test functions and real-world design problems, including the Congress of Evolutionary Computation (CEC) and Genetic and Evolutionary Computation Conference (GECCO) test suite, are presented in this paper. Also, the winners of CEC are highlighted with their reference papers. This paper also comprehensively reviews the literature related to benchmark test functions and real-world engineering design challenges using a bibliometric approach. This bibliometric analysis aims to analyze the number of publications, prolific authors, academic institutions, and country contributions to assess the field's growth and development. This paper will inspire researchers to innovate effective approaches for handling inequality and equality constraints. [ABSTRACT FROM AUTHOR]

A new bio-inspired metaheuristic algorithm named the Pufferfish Optimization Algorithm (POA), that imitates the natural behavior of pufferfish in nature, is introduced in this paper. The fundamental inspiration of POA is adapted from the defense mechanism of pufferfish against predators. In this defense mechanism, by filling its elastic stomach with water, the pufferfish becomes a spherical ball with pointed spines, and as a result, the hungry predator escapes from this threat. The POA theory is stated and then mathematically modeled in two phases: (i) exploration based on the simulation of a predator's attack on a pufferfish and (ii) exploitation based on the simulation of a predator's escape from spiny spherical pufferfish. The performance of POA is evaluated in handling the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that POA has achieved an effective solution with the appropriate ability in exploration, exploitation, and the balance between them during the search process. The quality of POA in the optimization process is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that POA provides superior performance by achieving better results in most of the benchmark functions in order to solve the CEC 2017 test suite compared to competitor algorithms. Also, the effectiveness of POA to handle optimization tasks in real-world applications is evaluated on twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems. Simulation results show that POA provides effective performance in handling real-world applications by achieving better solutions compared to competitor algorithms. [ABSTRACT FROM AUTHOR]

This paper presents the Multiscale Medalist Learning Algorithm (MMLA) as a novel heuristic approach for complex engineering optimization problems. By extending the Medalist Learning Algorithm, MMLA offers enhanced solution efficiency. The algorithm divides the learning process into successive periods with reduced search spaces, enabling focused search efforts in promising areas. Within each period, predefined learning stages are implemented. Top performers, known as medalists, engage in self-improvement operations through neighborhood searches, while common learners either learn from medalists or adapt based on the current state using neighborhood fluctuation. The MMLA balances exploration and exploitation capabilities through a natural growth curve that determines the learning efficiency. The MMLA's effectiveness and robustness are illustrated through the solution of a two-dimensional benchmark optimization problem and the successful resolution of ten well-known engineering design optimization problems. Comparative analysis demonstrates that the MMLA consistently outperforms other algorithms, providing competitive solutions with strict feasibility and minimal variation. [ABSTRACT FROM AUTHOR]

The biogeography-based optimization (BBO) algorithm is known for its simplicity and low computational overhead, but it often struggles with falling into local optima and slow convergence speed. Against this background, this work presents a multi-strategy enhanced BBO variant, named MSBBO. Firstly, the example chasing strategy is proposed to eliminate the destruction of the inferior solutions to superior solutions. Secondly, the heuristic crossover strategy is designed to enhance the search ability of the population. Finally, the prey search–attack strategy is used to balance the exploration and exploitation. To verify the performance of MSBBO, we compare it with standard BBO, seven BBO variants (PRBBO, BBOSB, HGBBO, FABBO, BLEHO, MPBBO and BBOIMAM) and seven meta-heuristic algorithms (GWO, WOA, SSA, ChOA, MPA, GJO and BWO) on multiple dimensions of 24 benchmark functions. It concludes that MSBBO significantly outperforms all competitors both on convergence accuracy, speed and stability, and MSBBO basically converges to the same results on 10,000 dimensions as on 1000 dimensions. Further, MSBBO is applied to six real-world engineering design problems. The experimental results show that our work is still more competitive than other latest optimization techniques (COA, EDO, OMA, SHO and SCSO) on constrained optimization problems. [ABSTRACT FROM AUTHOR]

Sea horse optimizer (SHO) is a noteworthy metaheuristic algorithm that emulates various intelligent behaviors exhibited by sea horses, encompassing feeding patterns, male reproductive strategies, and intricate movement patterns. To mimic the nuanced locomotion of sea horses, SHO integrates the logarithmic helical equation and Levy flight, effectively incorporating both random movements with substantial step sizes and refined local exploitation. Additionally, the utilization of Brownian motion facilitates a more comprehensive exploration of the search space. This study introduces a robust and high-performance variant of the SHO algorithm named modified sea horse optimizer (mSHO). The enhancement primarily focuses on bolstering SHO's exploitation capabilities by replacing its original method with an innovative local search strategy encompassing three distinct steps: a neighborhood-based local search, a global non-neighbor-based search, and a method involving circumnavigation of the existing search region. These techniques improve mSHO algorithm's search capabilities, allowing it to navigate the search space and converge toward optimal solutions efficiently. To evaluate the efficacy of the mSHO algorithm, comprehensive assessments are conducted across both the CEC2020 benchmark functions and nine distinct engineering problems. A meticulous comparison is drawn against nine metaheuristic algorithms to validate the achieved outcomes. Statistical tests, including Wilcoxon's rank-sum and Friedman's tests, are aptly applied to discern noteworthy differences among the compared algorithms. Empirical findings consistently underscore the exceptional performance of mSHO across diverse benchmark functions, reinforcing its prowess in solving complex optimization problems. Furthermore, the robustness of mSHO endures even as the dimensions of optimization challenges expand, signifying its unwavering efficacy in navigating complex search spaces. The comprehensive results distinctly establish the supremacy and efficiency of the mSHO method as an exemplary tool for tackling an array of optimization quandaries. The results show that the proposed mSHO algorithm has a total rank of 1 for CEC2020 test functions. In contrast, the mSHO achieved the best value for the engineering problems, recording a value of 0.012 665, 2993.634, 0.01 266, 1.724 967, 263.8915, 0.032 255, 58 507.14, 1.339 956, and 0.23 524 for the pressure vessel design, speed reducer design, tension/compression spring, welded beam design, three-bar truss engineering design, industrial refrigeration system, multi-product batch plant, cantilever beam problem, and multiple disc clutch brake problems, respectively. Source codes of mSHO are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/135882-improved-sea-horse-algorithm. [ABSTRACT FROM AUTHOR]

This paper introduces the Golf Sport Inspired Search (GSIS) algorithm as an evolutionary search method for numerical optimization. Each solution is generated with the aid of the step-length and search direction. The step-length is determined with the aid of the Tait's model of the trajectory of the golf ball, which is a physical model. The search direction is from the current position in the search space toward the position of a different individual or its reflected position. Such a direction determines the movement direction in the optimization process. A crossover operator is introduced to increase exploration at the starting and exploitation at the ending stages of the search. Performance of the GSIS is compared with many algorithms on 23 + 14 unconstrained classic functions, 29 functions of CEC 2017 benchmark suite and six constrained engineering design problems. Experiments indicate that with the aid of its cleverly designed operators, GSIS is able to produce promising results. Besides a cooperative game theoretic approach is introduced, which is able to measure the effectiveness of different operators in reducing the search cost. Such an approach can be used to measure the effectiveness of different operators that an evolutionary or swarm-intelligence algorithm owns. [ABSTRACT FROM AUTHOR]

The butterfly optimization algorithm (BOA) is a meta-heuristic algorithm that mimics foraging and mating behavior of butterflies. In order to alleviate the problems of slow convergence, local optimum and lack of population diversity of BOA, an enhanced adaptive butterfly optimization algorithm (EABOA) is proposed in this paper. First, a new adaptive fragrance model is designed, which provided a finer fragrance perception way and effectively enhanced the convergence speed and accuracy. Second, Lévy flight with high-frequency short-step jumping and low-frequency long-step walking is adopted to help the algorithm jump out of the local optimum. Third, the dimension learning-based hunting is employed to enhance information exchange by creating neighbors for each butterfly, thus improving the balance between local and global search and maintaining population diversity. In addition, the Fitness-Distance-Constraint (FDC) method is introduced to enhance constraint handling in EABOA (named FDC-EABOA). The proposed EABOA is compared with 8 well-known algorithms and 8 BOA variants in CEC 2022 test suite and the results were statistically analyzed using Friedman, Friedman aligned rank, Wilcoxon signed rank, Quade rank and multiple comparisons, analysis of variance (ANOVA) and range analysis. Finally, EABOA and FDC-EABOA are applied to seven engineering problems (parameter identification of photovoltaic module model, speed reducer design, tension/compression spring design, pressure vessel design, gear train design, welded beam design, SOPWM for 3-level inverters), and metrics such as Improvement Index (IF) and Mean Constraint Violation (MV) confirm that the proposed algorithms are satisfactory. Experimental results and statistical analysis show that the proposed algorithms outperform the comparison algorithms and demonstrate the strong potential for solving numerical optimization and engineering design problems. [ABSTRACT FROM AUTHOR]

The whale optimization algorithm has received much attention since its introduction due to its outstanding performance. However, like other algorithms, the whale optimization algorithm still suffers from some classical problems. To address the issues of slow convergence, low optimization precision, and susceptibility to local convergence in the whale optimization algorithm (WOA). Defining the optimization behavior of whale individuals as quantum mechanical behavior, a whale optimization algorithm based on atom-like structure differential evolution (WOAAD) is proposed. Enhancing the spiral update mechanism by introducing a sine strategy guided by the electron orbital center. Improving the random-walk foraging mechanism by applying mutation operations to both the electron orbital center and random individuals. Performing crossover operations between the newly generated individuals from the improved mechanisms and random dimensions, followed by a selection process to retain superior individuals. This accelerates algorithm convergence, enhances optimization precision, and prevents the algorithm from falling into local convergence. Finally, implementing a scouting bee strategy, where whale individuals progressively increase the number of optimization failures within a limited parameter L. When a threshold is reached, random initialization is carried out to enhance population diversity. Conducting simulation experiments to compare the improved algorithm with the whale optimization algorithm, other optimization algorithms, and other enhanced whale optimization algorithms. The experimental results indicate that the improved algorithm significantly accelerates convergence, enhances optimization precision, and prevents the algorithm from falling into local convergence. Applying the improved algorithm to five engineering design problems, the experimental results demonstrate that the improved algorithm exhibits good applicability. [ABSTRACT FROM AUTHOR]

Beluga Whale Optimization (BWO) is a new metaheuristic algorithm that simulates the social behaviors of beluga whales swimming, foraging, and whale falling. Compared with other optimization algorithms, BWO shows certain advantages in solving unimodal and multimodal optimization problems. However, the convergence speed and optimization performance of BWO still have some performance deficiencies when solving complex multidimensional problems. Therefore, this paper proposes a hybrid BWO method called HBWO combining Quasi-oppositional based learning (QOBL), adaptive and spiral predation strategy, and Nelder-Mead simplex search method (NM). Firstly, in the initialization phase, the QOBL strategy is introduced. This strategy reconstructs the initial spatial position of the population by pairwise comparisons to obtain a more prosperous and higher quality initial population. Subsequently, an adaptive and spiral predation strategy is designed in the exploration and exploitation phases. The strategy first learns the optimal individual positions in some dimensions through adaptive learning to avoid the loss of local optimality. At the same time, a spiral movement method motivated by a cosine factor is introduced to maintain some balance between exploration and exploitation. Finally, the NM simplex search method is added. It corrects individual positions through multiple scaling methods to improve the optimal search speed more accurately and efficiently. The performance of HBWO is verified utilizing the CEC2017 and CEC2019 test functions. Meanwhile, the superiority of HBWO is verified by utilizing six engineering design examples. The experimental results show that HBWO has higher feasibility and effectiveness in solving practical problems than BWO and other optimization methods. [ABSTRACT FROM AUTHOR]

The original whale optimization algorithm (WOA) has a low initial population quality and tends to converge to local optimal solutions. To address these challenges, this paper introduces an improved whale optimization algorithm called OLCHWOA, incorporating a chaos mechanism and an opposition-based learning strategy. This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase, thereby enhancing the quality of the initial whale population. Additionally, including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations. The work and contributions of this paper are primarily reflected in two aspects. Firstly, an improved whale algorithm with enhanced development capabilities and a wide range of application scenarios is proposed. Secondly, the proposed OLCHWOA is used to optimize the hyperparameters of the Long Short-Term Memory (LSTM) networks. Subsequently, a prediction model for Realized Volatility (RV) based on OLCHWOA-LSTM is proposed to optimize hyperparameters automatically. To evaluate the performance of OLCHWOA, a series of comparative experiments were conducted using a variety of advanced algorithms. These experiments included 38 standard test functions from CEC2013 and CEC2019 and three constrained engineering design problems. The experimental results show that OLCHWOA ranks first in accuracy and stability under the same maximum fitness function calls budget. Additionally, the China Securities Index 300 (CSI 300) dataset is used to evaluate the effectiveness of the proposed OLCHWOA-LSTM model in predicting RV. The comparison results with the other eight models show that the proposed model has the highest accuracy and goodness of fit in predicting RV. This further confirms that OLCHWOA effectively addresses real-world optimization problems. [ABSTRACT FROM AUTHOR]

In this paper, a new bio-inspired metaheuristic algorithm called Giant Armadillo Optimization (GAO) is introduced, which imitates the natural behavior of giant armadillo in the wild. The fundamental inspiration in the design of GAO is derived from the hunting strategy of giant armadillos in moving towards prey positions and digging termite mounds. The theory of GAO is expressed and mathematically modeled in two phases: (i) exploration based on simulating the movement of giant armadillos towards termite mounds, and (ii) exploitation based on simulating giant armadillos' digging skills in order to prey on and rip open termite mounds. The performance of GAO in handling optimization tasks is evaluated in order to solve the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that GAO is able to achieve effective solutions for optimization problems by benefiting from its high abilities in exploration, exploitation, and balancing them during the search process. The quality of the results obtained from GAO is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that GAO presents superior performance compared to competitor algorithms by providing better results for most of the benchmark functions. The statistical analysis of the Wilcoxon rank sum test confirms that GAO has a significant statistical superiority over competitor algorithms. The implementation of GAO on the CEC 2011 test suite and four engineering design problems show that the proposed approach has effective performance in dealing with real-world applications. [ABSTRACT FROM AUTHOR]

The Pelican Optimization Algorithm (POA) is a meta-heuristic algorithm that emulates the behavior and hunting strategy of pelicans. This paper presents three modifications that enhance the algorithmic traversal of POA by incorporating ten chaotic mapping factors. These modifications aim to improve the efficiency and accuracy of the traversal process. Firstly, the pelican optimization algorithm with chaotic mapping factors added to population generation but random prey generation (COPOA); secondly, the pelican optimization algorithm with chaotic mapping factors applied to prey generation but random population generation (OCPOA); finally, the pelican optimization algorithm with the same chaotic mapping factor, different sequences generated by different random initial values are mapped to the population and prey generation (CCPOA). The results of simulations on 12 benchmark test functions demonstrate that the algorithm (COPOA) adding the chaotic mapping factor to the population generation alone has higher generalizability, convergence speed and finding accuracy compared with the standard POA algorithm. Among these modifications, the Sine-COPOA method performed the best in ten chaotic mapping factor comparison tests. Furthermore, a comparison is made between the Sine-COPOA and typical intelligent optimization algorithms to assess its competence in optimization. The results show that Sine-COPOA performs better in most of the 12 benchmark test functions. Finally, the engineering design problems (pressure vessel problem and 10-bar truss design) are optimized. The experimental results demonstrate that Sine-COPOA effectively solves both function optimization and engineering optimization problems. [ABSTRACT FROM AUTHOR]

In this paper, an ensemble metaheuristic algorithm (denoted as LX-BBSCA) is introduced. It combines the strengths of Laplacian biogeography-based optimization (LX-BBO) and the sine cosine algorithm (SCA) to address structural engineering design optimization problems. Our primary objective is to mitigate the risk of getting stuck in local minima and accelerate the algorithm's convergence rate. We evaluate the proposed LX-BBSCA algorithm on a set of 23 benchmark functions, including both unimodal and multimodal problems of varying complexity and dimensions. Additionally, we apply LX-BBSCA to tackle five real-world structural engineering design problems, comparing the results with those obtained using other metaheuristics in terms of objective function values and convergence behavior. To ensure the statistical validity of our findings, we employ rigorous tests such as the t-test and the Wilcoxon rank test. The experimental outcomes consistently demonstrate that the ensemble LX-BBSCA algorithm outperforms not only the basic versions of BBO, SCA and LX-BBO but also other state-of-the-art metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Sand cat swarm optimization (SCSO) is a recently introduced popular swarm intelligence metaheuristic algorithm, which has two significant limitations -low convergence accuracy and the tendency to get stuck in local optima. To alleviate these issues, this paper proposes an improved SCSO based on the arithmetic optimization algorithm (AOA), the refracted opposition-based learning and crisscross strategy, called the sand cat arithmetic optimization algorithm (SC-AOA), which introduced AOA to balance the exploration and exploitation and reduce the possibility of falling into the local optimum, used crisscross strategy to enhance convergence accuracy. The effectiveness of SC-AOA is benchmarked on 10 benchmark functions, CEC 2014, CEC 2017, CEC 2022, and eight engineering problems. The results show that the SC-AOA has a competitive performance. [ABSTRACT FROM AUTHOR]

Over recent decades, research in Artificial Intelligence (AI) has developed a broad range of approaches and methods that can be utilized or adapted to address complex optimization problems. As real-world problems get increasingly complicated, this requires an effective optimization method. Various meta-heuristic algorithms have been developed and applied in the optimization domain. This paper used and ameliorated a promising meta-heuristic approach named Crow Search Algorithm (CSA) to address numerical optimization problems. Although CSA can efficiently optimize many problems, it needs more searchability and early convergence. Its positioning updating process was improved by supporting two adaptive parameters: flight length (fl) and awareness probability (AP) to tackle these curbs. This is to manage the exploration and exploitation conducts of CSA in the search space. This process takes advantage of the randomization of crows in CSA and the adoption of well-known growth functions. These functions were recognized as exponential, power, and S-shaped functions to develop three different improved versions of CSA, referred to as Exponential CSA (ECSA), Power CSA (PCSA), and S-shaped CSA (SCSA). In each of these variants, two different functions were used to amend the values of fl and AP. A new dominant parameter was added to the positioning updating process of these algorithms to enhance exploration and exploitation behaviors further. The reliability of the proposed algorithms was evaluated on 67 benchmark functions, and their performance was quantified using relevant assessment criteria. The functionality of these algorithms was illustrated by tackling four engineering design problems. A comparative study was made to explore the efficacy of the proposed algorithms over the standard one and other methods. Overall results showed that ECSA, PCSA, and SCSA have convincing merits with superior performance compared to the others. [ABSTRACT FROM AUTHOR]

In order to solve optimization problems, this paper introduces a new metaheuristic algorithm called the Numeric Crunch Algorithm (NCA), which employs the distribution behaviour of the population members and a novel hyperbolic acceleration function for convergence. Each generation's search space exploration and exploitation are ensured by the population's distribution behaviour around its members and their adaptively diversified boundaries, respectively. The convergence of the search solutions in the NCA was also accelerated by the random, adaptive parameters and hyperbolic function. A set of 68 test benchmark functions with (30, 100, 500, and 1000) dimensions was used to examine the NCA's effectiveness in terms of exploration, exploitation, local optimality avoidance, population fitness enhancement, and convergence rate. Firstly, the proposed NCA's behaviour is examined using a collection of 23 standard well-known benchmark functions, including unimodal, multimodal, and fixed-dimensional functions. Secondly, the proposed NCA's superiority is examined using the IEEE CEC-2015 and IEEE CEC-2017 benchmark suites. In addition to qualitatively examine NCA's superiority over other metaheuristics, Friedman and Wilcoxon rank-sum tests are performed. In terms of performance metrics, NCA ranked first. For application perspective, the NCA is tested on eight real-world constrained and unconstrained engineering design problems from IEEE CEC-2020 real-world optimization benchmark suits. The NCA algorithm's performance on benchmark functions and engineering design problems indicates that it can handle constrained and uncertain search spaces in real-world scenarios. The source code of the NCA algorithm is publicly available at https://github.com/Shivankur07/Numeric-Crunch-Algorithm.git. [ABSTRACT FROM AUTHOR]

The Mayfly Algorithm (MA) is a widely used metaheuristic algorithm characterized by a simple structure with simple parameters. However, MA may have problems such as poor global search ability and tend to fall into local optima. To overcome these limitations, this paper presents a chaos-based mayfly algorithm with opposition-based learning and Levy flight (COLMA) to boost the global search and local exploitation performance. In COLMA, we first introduced tent chaos to optimize the initialization process of the mayfly population, as random initialization processes may result in low diversity of the mayfly population. In addition, the gravity coefficient has been replaced with an adaptive gravity coefficient to balance the global search ability and local exploitation ability during the iterative process of the algorithm. In the process of updating the position of the male mayfly population, an opposition-based learning strategy based on an iterative chaotic map with infinite collapses is adopted to prevent the male mayfly population from falling into local optima. At the same time, in order to solve the problem of small search range of female mayfly population, Levy flight strategy was introduced to replace random walk strategy. Finally, an offspring optimization strategy was proposed to increase the probability of the offspring mayfly population approaching the optimal solution. To verify the effectiveness and superiority of COLMA and the adopted strategy, experiments were conducted on the classical benchmark functions, CEC 2017 benchmark suite and CEC 2020 real-world constraint optimization problems, and the results were statistically tested using the Wilcoxon signed rank test and Friedman test. The analysis results show that the proposed COLMA has statistical validity and reliability and has great advantages compared with MA, variant MA in terms of optimization accuracy, stability and convergence speed. [ABSTRACT FROM AUTHOR]

The prairie dog optimization (PDO) algorithm is a metaheuristic optimization algorithm that simulates the daily behavior of prairie dogs. The prairie dog groups have a unique mode of information exchange. They divide into several small groups to search for food based on special signals and build caves around the food sources. When encountering natural enemies, they emit different sound signals to remind their companions of the dangers. According to this unique information exchange mode, we propose a randomized audio signal factor to simulate the specific sounds of prairie dogs when encountering different foods or natural enemies. This strategy restores the prairie dog habitat and improves the algorithm's merit-seeking ability. In the initial stage of the algorithm, chaotic tent mapping is also added to initialize the population of prairie dogs and increase population diversity, even use lens opposition-based learning strategy to enhance the algorithm's global exploration ability. To verify the optimization performance of the modified prairie dog optimization algorithm, we applied it to 23 benchmark test functions, IEEE CEC2014 test functions, and six engineering design problems for testing. The experimental results illustrated that the modified prairie dog optimization algorithm has good optimization performance. [ABSTRACT FROM AUTHOR]

Wild Horse Optimizer (WHO) is a population-based metaheuristic algorithm inspired by animal behavior, which mainly imitates the decent behavior, grazing behavior, mating behavior and leadership dominance behavior of wild horses in nature to find the optimal. The initialization of the population by imitating the behavior of wild horses is prone to uneven distribution of population positions, and its position updating method is prone to local optimal problems while improving the efficiency of the search. In order to enhance the population diversity and to break out of the local optimum, an adaptive weighted wild horse optimizer based on backward learning and small-hole imaging strategy is proposed. The backward learning strategy is used to enhance the population diversity and improve the uneven distribution of individuals; The adaptive weight and small-hole imaging strategy are added to the local search strategy to improve the global search ability and jump out of the local optimum. To verify the effectiveness of the proposed algorithm, simulation experiments were conducted by using 23 benchmark test functions to test the search ability and Whale Optimization Algorithm (WOA), Moth-Flame Optimization (MFO), Rat Swarm Optimizer (RSO) and Multi-Verse Optimizer (MVO) algorithms are compared in terms of their search performance, and finally four real engineering design problems are solved. The simulation results indicate that the proposed FHPWHO has excellent merit-seeking capability. [ABSTRACT FROM AUTHOR]

The whale optimization algorithm (WOA) is a widely used metaheuristic optimization approach with applications in various scientific and industrial domains. However, WOA has a limitation of relying solely on the best solution to guide the population in subsequent iterations, overlooking the valuable information embedded in other candidate solutions. To address this limitation, we propose a novel and improved variant called Pbest-guided differential WOA (PDWOA). PDWOA combines the strengths of WOA, particle swarm optimizer (PSO), and differential evolution (DE) algorithms to overcome these shortcomings. In this study, we conduct a comprehensive evaluation of the proposed PDWOA algorithm on both benchmark and real-world optimization problems. The benchmark tests comprise 30-dimensional functions from CEC 2014 Test Functions, while the real-world problems include pressure vessel optimal design, tension/compression spring optimal design, and welded beam optimal design. We present the simulation results, including the outcomes of non-parametric statistical tests including the Wilcoxon signed-rank test and the Friedman test, which validate the performance improvements achieved by PDWOA over other algorithms. The results of our evaluation demonstrate the superiority of PDWOA compared to recent methods, including the original WOA. These findings provide valuable insights into the effectiveness of the proposed hybrid WOA algorithm. Furthermore, we offer recommendations for future research to further enhance its performance and open new avenues for exploration in the field of optimization algorithms. The MATLAB Codes of FISA are publicly available at https://github.com/ebrahimakbary/PDWOA. [ABSTRACT FROM AUTHOR]

This paper introduces a new metaheuristic algorithm called Migration Algorithm (MA), which is helpful in solving optimization problems. The fundamental inspiration of MA is the process of human migration, which aims to improve job, educational, economic, and living conditions, and so on. The mathematical modeling of the proposed MA is presented in two phases to empower the proposed approach in exploration and exploitation during the search process. In the exploration phase, the algorithm population is updated based on the simulation of choosing the migration destination among the available options. In the exploitation phase, the algorithm population is updated based on the efforts of individuals in the migration destination to adapt to the new environment and improve their conditions. MA’s performance is evaluated on fifty-two standard benchmark functions consisting of unimodal and multimodal types and the CEC 2017 test suite. In addition, MA’s results are compared with the performance of twelve well-known metaheuristic algorithms. The optimization results show the proposed MA approach’s high ability to balance exploration and exploitation to achieve suitable solutions for optimization problems. The analysis and comparison of the simulation results show that MA has provided superior performance against competitor algorithms in most benchmark functions. Also, the implementation of MA on four engineering design problems indicates the effective capability of the proposed approach in handling optimization tasks in real-world applications. [ABSTRACT FROM AUTHOR]

In this paper, a new bio-inspired metaheuristic algorithm named the Kookaburra Optimization Algorithm (KOA) is introduced, which imitates the natural behavior of kookaburras in nature. The fundamental inspiration of KOA is the strategy of kookaburras when hunting and killing prey. The KOA theory is stated, and its mathematical modeling is presented in the following two phases: (i) exploration based on the simulation of prey hunting and (ii) exploitation based on the simulation of kookaburras' behavior in ensuring that their prey is killed. The performance of KOA has been evaluated on 29 standard benchmark functions from the CEC 2017 test suite for the different problem dimensions of 10, 30, 50, and 100. The optimization results show that the proposed KOA approach, by establishing a balance between exploration and exploitation, has good efficiency in managing the effective search process and providing suitable solutions for optimization problems. The results obtained using KOA have been compared with the performance of 12 well-known metaheuristic algorithms. The analysis of the simulation results shows that KOA, by providing better results in most of the benchmark functions, has provided superior performance in competition with the compared algorithms. In addition, the implementation of KOA on 22 constrained optimization problems from the CEC 2011 test suite, as well as 4 engineering design problems, shows that the proposed approach has acceptable and superior performance compared to competitor algorithms in handling real-world applications. [ABSTRACT FROM AUTHOR]