Cantilever Beam Design Research Articles

Performance Assessment of Natural Survivor Method-Based Metaheuristic Optimizers in Global Optimization and Engineering Design Problems

This study presents the comparative performance analysis of Natural Survivor Method (NSM)-based algorithms in solving the IEEE CEC 2022 test suite benchmark problems and four real-world engineering design problems. Three different variants (Case1, Case2, Case3) of the NSM-TLABC, NSM-SFS and NSM-LSHADE-SPACMA algorithms were used in the study. The data obtained from the experimental studies were statistically analyzed using Friedman and Wilcoxon signed-rank tests. Based on the Friedman test results, NSM-LSHADE-SPACMA_Case2 showed the best performance with an average Friedman score of 3.96. The Wilcoxon signed-rank test showed that NSM-LSHADE-SPACMA_Case2 outperformed its competitors in 13 out of 16 experiments, achieving a success rate of 81.25%. NSM-LSHADE-SPACMA_Case2, which was found to be the most powerful of the NSM-based algorithms, is used to solve cantilever beam design, tension/compression spring design, pressure vessel design and gear train design problems. The optimization results are also compared with eight state-of-the-art metaheuristics, including Rime Optimization Algorithm (RIME), Nonlinear Marine Predator Algorithm (NMPA), Northern Goshawk Optimization (NGO), Kepler Optimization Algorithm (KOA), Honey Badger Algorithm (HBA), Artificial Gorilla Troops Optimizer (GTO), Exponential Distribution Optimization (EDO) and Hunger Games Search (HGS). Given that all results are together, it is seen that NSM-LSHADE-SPACMA_Case2 algorithm consistently produced the best results for the global and engineering design problems studied.

Solution of engineering design and truss topology problems with improved forensic-based investigation algorithm based on dynamic oppositional based learning

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.

Enhanced gorilla troops optimizer powered by marine predator algorithm: global optimization and engineering design

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.

Multi-scale design of composite material structures for maximizing fundamental natural frequency

This study deals with the topology optimization (TO) of linear elastic composite structures considering fundamental natural frequency maximization. The concept of functionally graded structures (FGS) is used for anisotropic composite material structure design to improve the structural dynamic performance. The rational approximation of material properties (RAMP) method was employed as an alternative to the solid isotropic material with penalization (SIMP) method to improve the convergence stability for eigen-frequency problem. In the SIMP method, the occurrence of local eigen-modes at low frequencies in the void region causes ill-convergence, whereas the RAMP method prevents local eigen-modes from occurring. The method of moving asymptotes is used as an optimizer. Numerical verification was performed for three structural types: cantilever beam, double-clamped beam, and four-pinned beam structure. In addition, for cantilever beam design, bi-objective optimization for maximizing fundamental natural frequency as well as minimizing structural compliance was performed using the adaptive weighting method that quantitatively reflects the designer's preference by adjusting the weight for each objective function at each iteration.

Frequency Design of a Vertical Cantilever Beam Carrying Tip Mass

Previous research has revealed that vertical cantilever beam structures, after considering the effects of gravity, can exhibit a 1:3 internal resonance between two transverse modes. However, whether a similar phenomenon would still occur in the system when a proof body is attached to the free end of cantilever beams, and how to perform parameter design to deliberately induce internal resonance are still open issues. The present study conducts research on the natural frequencies of a vertical cantilever beam with a tip mass. Within the framework of the Euler–Bernoulli beam theory, Hamilton’s principle is utilized to derive the integral-partial differential equation of free vibration of the system. The corresponding frequency equation is then solved using the Chebyshev pseudospectral method, with an algebraic equation of the natural frequency obtained. Moreover, an explicit analytical expression for the fundamental frequency is obtained using parameter fitting, which is then used to reveal the relationship among parameters under the critical state of buckling and evaluate the extent of gravity’s impact on the fundamental frequency. The study finally investigates the ratio of the first two natural frequencies. A parameter design method is proposed, which derives a parametric curve in terms of the fundamental frequency, ensuring a 1:3 ratio between the first two natural frequencies. The free vibration time history at the midpoint of the beam is acquired by directly solving the system’s partial differential equation through the finite element method. Fourier transformation of this time history verifies that the fundamental frequency aligns with a predetermined value, and the ratio between the first two natural frequencies is 1:3, thus confirming the efficacy of the proposed method. It is found that the addition of a tip mass provides more flexibility in adjusting parameters to achieve internal resonance, which facilitates the miniaturization of actual structures.

The optimal design of wind-induced vibration energy harvester with microscale and high efficiency

Abstract In order to improve the performance of wind-induced vibration energy harvester (WIV-EH), the bluff body and cantilever beam play a significant role when the energy harvester works. In this paper, a WIV-EH model is designed to explore the factors affecting energy capture efficiency. In the Optimal design of bluff body, five kinds of bluff body is set to analyse the influence of the shape on the lift and lift coefficient by COMSOL Multiphysics 6.0. According to the simulation results, the D-shaped bluff body is optimized by changing the scale ratio and windward surface shape. In the Optimal design of cantilever beam, the result shows that under the same bluff body and wind speed, the shape of cantilever beam will affect the magnitude and distribution of strain. we also designed cantilever beams with different materials and splicing materials, and studied the response voltage output at different wind speeds. According to the above results, a wind tunnel test was designed for verification. The research results may provide new ideas for improving the energy harvesting efficiency of energy harvesters.

Improved honey badger algorithm based on elementary function density factors and mathematical spirals in polar coordinate systema

The Honey Badger Algorithm (HBA) is a new swarm intelligence optimization algorithm by simulating the foraging behavior of honey badgers in nature. To further improve its convergence speed and convergence accuracy, an improved HBA based on the density factors with the elementary functions and the mathematical spirals in the polar coordinate system was proposed. The algorithm proposes six density factors for attenuation states based on elementary functions, and introduces mathematical expressions of the polar diameters and angles of seven mathematical spirals (Fibonacci spiral, Butterfly curve, Rose spiral, Cycloid, Archimedean spiral, Hypotrochoid and Cardioid) in the polar coordinate system based on the density factors with the best synthesized effect to replace the foraging strategy of honey badger digging pattern in HBA. By using 23 benchmark test functions, the above improvements are sequentially compared with the original HBA, and the optimization algorithm with the best improvement, α4CycρHBA, is selected to be compared with SOA, MVO, DOA, CDO, MFO, SCA, BA, GWO and FFA. Finally, four engineering design problems (pressure vessel design, three-bar truss design, cantilever beam design and slotted bulkhead design) were solved. The simulation experiments results show that the proposed improved HBA based on the density factors with the elementary functions and the mathematical spirals of the polar coordinate system has the characteristics of balanced exploration and expiration, fast convergence and high accuracy, and is able to solve the function optimization and engineering optimization problems in a better way.

Piezoelectric based V-shape cantilever beam design of energy harvester for biomedical applications

AbstractThis research paper exhibits the design of a V-shaped cantilever beam as a micro Energy Harvester (EH) having Piezoelectric (PZT) as its energy source for biomedical applications. PZT source based materials have the ability to convert the mechanical energy into electrical energy. Low-power biomedical devices mostly operate using electrical energy (i.e. batteries). But batteries are treated as a bio-hazard due to the massive use of biomedical applications. To overcome this toxic bio-hazard, the proposed PZT based V-shaped cantilever beam of micro EH can solve the limitations. To perform the experimental work, the cantilever beam design parameters - length, width and thickness have been considered and simulated using COMSOL Multiphysics to get the resonant frequency of 156.19 Hz which is lower than previous research work. It was observed that the obtained lower resonant frequency can be converted into AC voltage (mV) using PZT material. To convert the output AC voltage (mV) into DC voltage, a circuit of an Ultra-Low-Power (ULP) EH will be designed in LTSPICE software. Finally, the integration of the both V-shape cantilever beam and the ULP EH circuit will be implemented in PCB hardware to generate the output power (10 µW), will be stored in super-capacitor for biomedical devices-pacemaker.

Design analysis and simulation of serpentine-shaped piezoelectric cantilever beam for pipeline vibration-based energy harvester

<abstract> <p>This study investigated the design and simulation of a novel serpentine-shaped piezoelectric cantilever beam to harness pipeline vibration energy. As the demand for sustainable energy sources increases, harvesting piezoelectric energy from environmental vibrations offers an attractive way to use low-power devices. The purpose of the proposed serpentine configuration is to improve energy dissipation efficiency by maximizing the piezoelectric material exposure to dynamic mechanical stress caused by pipeline vibration. The design process included finite element analysis simulations performed using COMSOL Multiphysics software to optimize the geometry of the cantilever beam. The serpentine structure was strategically designed to take advantage of the flexural vibration caused by the pipeline and its operating dynamics. Extensive simulations evaluated the piezoelectric cantilever beam, taking into account various parameters such as beam size, shape and material properties. From the analysis conducted in COMSOL Multiphysics software, the model was able to produce up to 14.38 V at the resonant frequency of 263 Hz. The simulation results show the effectiveness of the serpentine-shaped piezoelectric cantilever in generating electrical energy from the pipeline vibrations within the safe vibration region of the pipeline from 10 to 300 Hz.</p> </abstract>

OCRUN: An oppositional Runge Kutta optimizer with cuckoo search for global optimization and feature selection

The recently proposed swarm intelligence algorithm, Runge–Kutta Optimization (RUN), is rooted in the fourth-order Runge–Kutta method. Compared with its counterparts, RUN boasts an advantage of having a more concrete theoretical foundation embodying a more powerful optimization efficacy, free from any metaphor. However, RUN still has its shortcomings. The compelling enhanced solution function leads to insufficient exploration ability of the algorithm, and resulting in an imbalance between exploration and exploitation that cannot be mitigated. An improved version based on opposition-based learning and cuckoo search is proposed to compensate for the above deficiencies, called OCRUN. OCRUN is tested on 30 test functions of CEC2014 with 10 classical metaheuristics and 9 advanced metaheuristics, respectively. Combining the experimental results and the Wilcoxon signed-rank test, OCRUN exhibits excellent performance. At the same time, parameter sensitivity analysis experiments are also carried out on this test set. Furthermore, a binary implementation of the algorithm was constructed specifically for feature selection cases, labeled as BOCRUN. BOCRUN is compared with 5 existing binary metaheuristics on 15 public datasets. The experimental results show that the improved algorithm performs well in feature selection. Therefore, OCRUN is an effectively improved optimizer. Finally, the OCRUN method offers high-quality solutions to engineering problems and contributes significantly to engineering design under practical constraints. The method has been successfully applied to various design scenarios, such as reducer design, cantilever beam design, and tension/compression spring design. The OCRUN method outperforms other similar products in terms of performance and effectiveness.

A modified smell agent optimization for global optimization and industrial engineering design problems

Abstract This paper introduces an Improved Smell Agent Optimization Algorithm (mSAO), a new and enhanced metaheuristic designed to tackle complex engineering optimization issues by overcoming the shortcomings of the recently introduced Smell Agent Optimization Algorithm. The proposed mSAO incorporates the jellyfish swarm active–passive mechanism and novel random operator in the elementary SAO. The objective of modification is to improve the global convergence speed, exploration–exploitation behaviour, and performance of SAO, as well as provide a problem-free method of global optimization. For numerical validation, the mSAO is examined using 29 IEEE benchmarks with varying degrees of dimensionality, and the findings are contrasted with those of its basic version and numerous renowned recently developed metaheuristics. To measure the viability of the mSAO algorithm for real-world applications, the algorithm was employed to solve to resolve eight challenges drawn from real-world scenarios including cantilever beam design, multi-product batch plant, industrial refrigeration system, pressure vessel design, speed reducer design, tension/compression spring, and three-bar truss problem. The computational analysis demonstrates the robustness of mSAO relatively in finding optimal solutions for mechanical, civil, and industrial design problems. Experimental results show that the suggested modifications lead to an improvement in solution quality by 10–20% of basic SAO while solving constraint benchmarks and engineering problems. Additionally, it contributes to avoiding local optimal stuck, and premature convergence limitations of SAO and simultaneously.

A comparative study on transient vibration suppression of magnetic nonlinear vibration absorbers with different arrangements

Nonlinear vibration absorbers (NVA) using magnets feature ease of implementation and outstanding tunability and have attracted much interest in vibration suppression in recent years. Through configuration design and optimization, magnetic NVA can exhibit different nonlinear characteristics such as monostability, bistability and tristability. This work performs a comprehensive comparative study on the transient vibration suppression performances of the NVAs with four different magnet arrangements based on the cantilever beam design, including the NVA with two repulsive magnets (2RMNVA), two attractive magnets (2AMNVA), three repulsive magnets (3RMNVA) and three attractive magnets (3AMNVA). Based on the dipole–dipole model of magnets, the dynamic model of the system containing different magnetic NVAs is established for numerical simulation and the key parameters of each NVA are optimized under different excitation levels. The influence of parameters on different magnetic NVAs is investigated. Based on the optimization configurations, the rate of the energy dissipation from the primary structure by different magnetic NVAs is compared by wavelet transform analysis. The results show that due to the largest oscillations of crossing outmost wells, the optimized 3RMNVA and 3AMNVA are the preferred configurations for magnetic NVA under different excitation levels. Meanwhile, through the verification and comparison with reported designs, the effectiveness of the optimization method and the optimized NVAs in this paper are confirmed. This work provides the approach and guidelines to design the effective NVA to suppress transient vibrations.

Enhanced marine predator algorithm for global optimization and engineering design problems

Marine predator algorithm adopts the policy of optimal encounter rate in biological interaction between predator and prey, inspired by the Lévy and Brownian motions commonly used in oceanic predators. However, the Marine predator algorithm faces problems of premature convergence, poor search capability, and stagnation in a local optimum. In order to tolerate these shortcomings, two different strategies are applied to Marine predator algorithm in this study. (1) Taylor-based optimal neighborhood strategy (TNS) provides a more effective update in obtaining optimal individuals, thus reducing the probability of local optimum falling and attempting to overcome premature convergence, (2) Asymmetric search space with dynamic option-based learning significantly increases the probability of the population achieving the global optimum, thus improving the exploitation ability of Marine predator algorithm. The new method introduced with these two strategies added to the Marine predator algorithm is called Marine Predator Algorithm Dynamic Opposition and Taylor neighborhood search (DOTMPA). DOTMPA’s convergence performance is compared with current metaheuristic optimization and different versions of MPA in the literature on CEC2017, CEC2019, and CEC2022 benchmark suites. The results are discussed with convergence curves and box-plot analyses. DOTMPA is tested in Cantilever beam design, Tension/compression spring design, Speed reducer, Car crashworthiness, and Industrial refrigeration system engineering problems. The results are compared with the studies in the literature. Experimental results show that the proposed algorithm has strong competitiveness in terms of convergence accuracy. In addition, the stability of DOTMPA in convergence to the global optimum with trajectory analysis is also mentioned. Finally, the proposed method is applied to three truss topology optimization problems (20-truss, 24-truss, and 72-truss). The experimental results, the applicability of the proposed algorithm in practical applications, and the benchmark problem make it a promising and competitive optimization algorithm for optimization problems.

Novel memetic of beluga whale optimization with self-adaptive exploration–exploitation balance for global optimization and engineering problems

A population-based optimizer called beluga whale optimization (BWO) depicts behavioral patterns of water aerobics, foraging, and diving whales. BWO runs effectively, nevertheless it retains numerous of deficiencies that has to be strengthened. Premature convergence and a disparity between exploitation and exploration are some of these challenges. Furthermore, the absence of a transfer parameter in the typical BWO when moving from the exploration phase to the exploitation phase has a direct impact on the algorithm’s performance. This work proposes a novel modified BWO (mBWO) optimizer that incorporates an elite evolution strategy, a randomization control factor, and a transition factor between exploitation and exploitation. The elite strategy preserves the top candidates for the subsequent generation so it helps generate effective solutions with meaningful differences between them to prevent settling into local maxima. The elite random mutation improves the search strategy and offers a more crucial exploration ability that prevents stagnation in the local optimum. The mBWO incorporates a controlling factor to direct the algorithm away from the local optima region during the randomization phase of the BWO. Gaussian local mutation (GM) acts on the initial position vector to produce a new location. Because of this, the majority of altered operators are scattered close to the original position, which is comparable to carrying out a local search in a small region. The original method can now depart the local optimal zone because to this modification, which also increases the optimizer’s optimization precision control randomization traverses the search space using random placements, which can lead to stagnation in the local optimal zone. Transition factor (TF) phase are used to make the transitions of the agents from exploration to exploitation gradually concerning the amount of time required. The mBWO undergoes comparison to the original BWO and 10 additional optimizers using 29 CEC2017 functions. Eight engineering problems are addressed by mBWO, involving the design of welded beams, three-bar trusses, tension/compression springs, speed reducers, the best design of industrial refrigeration systems, pressure vessel design challenges, cantilever beam designs, and multi-product batch plants. In both constrained and unconstrained settings, the results of mBWO preformed superior to those of other methods.

Study on ZnO and BaTiO3 for Low Frequency Applications

In this paper, we have discussed about piezo electric materials and its properties which are suitable for design and fabrication of low frequency applications. In this we have multiple of piezoelectric materials for the design of low frequency cantilever beam among them some of are here like, ceramic, lead zirconate titanate, composite material, barium titanate, quartz, pvdf, lithium niobate, lithium tantalate, zinc oxide, piezoelectric ceramics, among this here we compared two piezo electric materials they are P(VDF-TrFE) and copolymer such as ZnO and BaTiO3. This process has been done through polarization and annealing. Initially the process the process starts by simulation which includes calculation of polarization frequency of an compared elements, further the characterization of an BaTiO3 and ZnO will be done by oscilloscope and the fabrication of this materials will done through electrospinning.

Letter: Application of optimization algorithms to engineering design problems and discrepancies in mathematical formulas

Engineering design optimization problems have attracted the attention of researchers since they appeared. Those who work on developing optimization algorithms, in particular, apply their developed algorithms to these problems in order to test their new algorithms’ capabilities. The mathematical discrepancy emerges during the implementation of equations and constraints related to these engineering problems. This is due to an error occurring in writing or transmitting these equations from one paper to another. Maintaining these discrepancies will have a negative impact on the assessment and model performance verification of the newly developed algorithms, as well as the decision-making process. To address this issue, this study investigates the mathematical discrepancies occurred by researchers in four well-known engineering design optimization problems (Welded Beam Design WBD, Speed Reducer Design SRD, Cantilever Beam Design CBD, and Multiple Disk Clutch Brake Design MDCBD). We have investigated some of the recently published papers in the literature, identifying discrepancies in their mathematical formulas, and fixing them appropriately by referring and comparing them to the original problem. Furthermore, all mathematical discrepancies , references, parameters, cost functions, constraints, and constraint errors are highlighted, arranged and organized in tables. As a result, this work can help readers and researchers avoid being confused and wasting time when working on these engineering design optimization problems.

An Efficient Approach Based on Remora Optimization Algorithm and Levy Flight for Intrusion Detection

With the recent increase in network attacks by threats, malware, and other sources, machine learning techniques have gained special attention for intrusion detection due to their ability to classify hundreds of features into normal system behavior or an attack attempt. However, feature selection is a vital preprocessing stage in machine learning approaches. This paper presents a novel feature selection-based approach, Remora Optimization Algorithm-Levy Flight (ROA-LF), to improve intrusion detection by boosting the ROA performance with LF. The developed ROA-LF is assessed using several evaluation measures on five publicly available datasets for intrusion detection: Knowledge discovery and data mining tools competition, network security laboratory knowledge discovery and data mining, intrusion detection evaluation dataset, block out traffic network, Canadian institute of cybersecurity and three engineering problems: Cantilever beam design, three-bar truss design, and pressure vessel design. A comparative analysis between developed ROA-LF, particle swarm optimization, salp swarm algorithm, snake optimizer, and the original ROA methods is also presented. The results show that the developed ROA-LF is more efficient and superior to other feature selection methods and the three tested engineering problems for intrusion detection.

An Improved Elite Slime Mould Algorithm for Engineering Design

The Swarm intelligence algorithm is a very prevalent field in which some scholars have made outstanding achievements. As a representative, Slime mould algorithm (SMA) is widely used because of its superior initial performance. Therefore, this paper focuses on the improvement of the SMA and the mitigation of its stagnation problems. For this aim, the structure of SMA is adjusted to develop the efficiency of the original method. As a stochastic optimizer, SMA mainly stimulates the behavior of slime mold in nature. For the harmony of the exploration and exploitation of SMA, the paper proposed an enhanced algorithm of SMA called ECSMA, in which two mechanisms are embedded into the structure: elite strategy, and chaotic stochastic strategy. The details of the original SMA and the two introduced strategies are given in this paper. Then, the advantages of the improved SMA through mechanism comparison, balance-diversity analysis, and contrasts with other counterparts are validated. The experimental results demonstrate that both mechanisms have a significant enhancing effect on SMA. Also, SMA is applied to four structural design issues of the welded beam design problem, PV design problem, I-beam design problem, and cantilever beam design problem with excellent results.

Mechanical modeling, numerical investigation and design of cantilever beam for low pull-in MEMS switch

Mechanical modeling, numerical investigation and design of cantilever beam for low pull-in MEMS switch

An opposition learning and spiral modelling based arithmetic optimization algorithm for global continuous optimization problems

In engineering applications, many real-world optimization problems are nonlinear with multiple local optimums. Traditional algorithms that require gradients are not suitable for these problems. Meta-heuristic algorithms are popularly employed to deal with these problems because they can promisingly jump out of local optima and do not need any gradient information. The arithmetic optimization algorithm (AOA), a recently developed meta-heuristic algorithm, uses arithmetic operators (multiplication, division, subtraction, and addition) to solve optimization problems including nonlinear ones. However, the exploration and exploitation of AOA are not effective to handle some complex optimization problems. In this paper, an opposition learning and spiral modelling based AOA, namely OSAOA, is proposed for enhancing the optimization performance. It improves AOA from two perspectives. In the first perspective, the opposition-based learning (OBL) is committed to taking both candidate solutions and their opposite solutions into consideration for improving the global search with a high probability of jumping out of local minima. Then, the spiral modelling is introduced as the second perspective, which is particularly useful in getting the solutions gathering faster and accelerating the convergence speed in the later stage. In addition, OSAOA is compared with other existing advanced meta-heuristic algorithms based on 23 benchmark functions and four engineering problems: the three-bar truss design, the cantilever beam design, the pressure vessel design, and the tubular column design. From our simulations and engineering applications, the proposed OSAOA can provide better optimization results in dealing with these real-world optimization problems.