Engineering Design Problems Research Articles

Enhancing Sine–Cosine mutation strategy with Lorentz distribution for solving engineering design problems

Enhancing Sine–Cosine mutation strategy with Lorentz distribution for solving engineering design problems

Hybridizing interval method with a heuristic for solving real-world constrained engineering optimization problems

Practical issues in real-world engineering problems limit the use of traditional optimization methods in finding solutions to design optimization problems. Although several advanced optimization approaches have been developed recently, a unified way to solve almost every problem effectively is yet to arise. To address this, we present a new method for optimizing several structure engineering design problems using our unique hybrid optimization approach inspired by the integration of branch and bound-like concepts of interval analysis with heuristics, and it differs from other methods in the literature. We hybridize the Split-Detect-Discard-Shrink technique and the Sophisticated ABC (SDDS-SABC) algorithm. The advantage of the SDDS process is that it shrinks the entire search region through recursive breakdown and improves computational effort to focus on subregion covering potential solutions for further decomposition. SABC plays a vital role in extracting the best solutions from subregions whose values help detect the promising subregion. SDDS and SABC are sequentially repeated until global/ close to the global solution(s) is reached. The ranking and selection rules are applied to assist in an optimistic decision-making process. In the SABC algorithm, we introduce Latin Hypercube, a new initialization scheme for food sources. This enhances the computational approach towards the optimal solution. Develop a dual strategy employed bees phase to explore their neighbourhoods better. Introduce a new Dynamic penalty method free from extra parameters/ factors. Here 57 real-world complex constrained optimization benchmark problems (CEC2020) featuring problems with Industrial Chemical Processes, Synthesis and Design, Mechanical Engineering, Power System, Power Electronics, and Livestock Feed Ration Optimization are presented, where our method is applied to optimize the decision parameters. Finally, our optimal results are compared in terms of their statistical significance using Friedman and Wilcoxon rank tests with three well-known optimizer algorithms in the literature. The results show that SDDS-SABC outperforms most tested competitors and demonstrates the practicability of SDDS-SABC in solving challenging real-world problems. Moreover, the SDDS-SABC method is numerically stable and suitable for the illustrated optimization examples. The key novelty of the presented approach is the ability to perform a static and better optimal solution in most runs despite the problem being too complex.

Hunger games pattern search with elite opposite-based solution for solving complex engineering design problems

Hunger games pattern search with elite opposite-based solution for solving complex engineering design problems

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.

Developments on metaheuristic-based optimization for numerical and engineering optimization problems: Analysis, design, validation, and applications

Optimization problems are prevalent in a variety of real-world applications, including medical, engineering, chemical, and others, and must be precisely solved to enhance the performance of these applications. Unfortunately, finding near-optimal solutions to these problems is regarded as a hard challenge due to their various characteristics. In a new attempt to solve these problems, this paper presents a new variant of the artificial gorilla troops optimizer (GTO) called ranking-based GTO (RGTO). This variant uses two strategies known as the ranking-based update strategy and the convergence acceleration strategy to improve both the classical GTO's exploitation and exploration capabilities. The first strategy is proposed to enhance each gorilla's local and global search abilities, whereas the latter is intended to enhance GTO's global search abilities to reach better solutions as quickly as possible. First, a recent and challenging benchmark, namely CEC-2017, is utilized to assess the RGTO's explorative and exploitative capabilities. After that, RGTO is used to solve three engineering optimization problems, including parameter estimation problems for both photovoltaic (PV) models and proton exchange membrane fuel cells (PEMFCs), as well as some engineering design problems, to demonstrate how well it performs for real-world optimization problems. Compared to several rival optimizers, the proposed algorithm provides outstanding outcomes for the three engineering optimization benchmark problems considered.

A multi-strategy enhanced northern goshawk optimization algorithm for global optimization and engineering design problems

Metaheuristic algorithms are widely utilized in various fields owing to their ability to produce a variety of solutions. The Northern Goshawk Optimization (NGO) is an effective optimization algorithm, however, its convergence rate is slow and it tends to fall into local optima in some cases. Therefore, this paper proposes a Multi-strategy Enhanced Northern Goshawk Optimization (MENGO) algorithm, which introduces a novel exploration strategy based on Levy flights to mitigate the risk of getting trapped in local optima. To balance exploration and exploitation, a new nonlinear reduction strategy based on the sine function is proposed. Additionally, a novel exploitation strategy is employed to accelerate the convergence speed while ensuring accuracy. The effectiveness of MENGO is demonstrated by comparing it with 13 advanced algorithms using 23 classical benchmark and 12 CEC2022 test functions in different dimensions. To evaluate the feasibility of the proposed approach in real-world applications, it is studied for nine constrained engineering problems, and the performance is compared with other contender algorithms extracted from the literature. The all experimental results show that MENGO outperforms other state-of-the-art algorithms in terms of solution quality and stability, making it a more competitive option.

Diversity-enhanced modified sine cosine algorithm and its application in solving engineering design problems

The sine cosine algorithm (SCA) is a recently developed metaheuristic algorithm that is inspired by the characteristics of sine and cosine trigonometric functions. Although it has successfully solved several benchmark and real-life problems, it experiences the issues of slow convergence rate, premature convergence and local optima stagnation. To overcome these shortcomings, this paper introduces an improved version of the SCA named ISCA, in which four different search strategies namely, population division, modification of the original SCA search scheme, hybridization of the modified SCA with differential evolution (DE), and additional mutation phase are integrated. In the first stage, the population division is performed to pass candidate solutions through different levels of exploration and exploitation. The second stage modifies the original SCA to exploit the main features of the SCA related to exploring the elite area of the search space. In the third stage, the proposed modified SCA is hybridized with DE to maintain the diversity of the population. The fourth and last stage embeds the Cauchy mutation-based search scheme for failure candidates to prevent them from stagnation at local optima. To validate the performance of the ISCA, it has been tested on 30 standard benchmark problems including unimodal, multimodal and composite problems and its comparison is performed with the original SCA and other metaheuristic algorithms. Its performance on the scalability of optimization problems is also verified by increasing the dimensions of test problems from 30 to 1000. Various performance measures such as average and standard deviation of optimization results, statistical analysis, and convergence analysis conclude the better search efficiency of the ISCA. Furthermore, six engineering application problems are solved using ISCA to demonstrate its real-world applicability. The experimental results attest that the proposed ISCA is highly competitive with the other metaheuristics.

MOIMPA: multi-objective improved marine predators algorithm for solving multi-objective optimization problems

This paper introduces a multi-objective variant of the marine predators algorithm (MPA) called the multi-objective improved marine predators algorithm (MOIMPA), which incorporates concepts from Quantum theory. By leveraging Quantum theory, the MOIMPA aims to enhance the MPA’s ability to balance between exploration and exploitation and find optimal solutions. The algorithm utilizes a concept inspired by the Schrödinger wave function to determine the position of particles in the search space. This modification improves both exploration and exploitation, resulting in enhanced performance. Additionally, the proposed MOIMPA incorporates the Pareto dominance mechanism. It stores non-dominated Pareto optimal solutions in a repository and employs a roulette wheel strategy to select solutions from the repository, considering their coverage. To evaluate the effectiveness and efficiency of MOIMPA, tests are conducted on various benchmark functions, including ZDT and DTLZ, as well as using the evolutionary computation 2009 (CEC’09) test suite. The algorithm is also evaluated on engineering design problems. A comparison is made between the proposed multi-objective approach and other well-known evolutionary optimization methods, such as MOMPA, multi-objective ant lion optimizer, and multi-objective multi-verse optimization. The statistical results demonstrate the robustness of the MOIMPA approach, as measured by metrics like inverted generational distance, generalized distance, spacing, and delta. Furthermore, qualitative experimental results confirm that MOIMPA provides highly accurate approximations of the true Pareto fronts.

Mantis Search Algorithm: A novel bio-inspired algorithm for global optimization and engineering design problems

This study presents a new nature-inspired optimization algorithm, namely the Mantis Search Algorithm (MSA), inspired by the unique hunting behavior and sexual cannibalism of praying mantises. In brief, MSA consists of three optimization stages, including the search for prey (exploration), attack prey (exploitation), and sexual cannibalism. Those operators are simulated using various mathematical models to effectively tackle optimization challenges across diverse search spaces. The performance of MSA is rigorously tested on fifty-two optimization problems and three real-world applications (five engineering design problems, and the parameter estimation problem of photovoltaic modules and fuel cells) to show its versatility and adaptability to different scenarios. To disclose the MSA’s superiority, it is compared to two categories from the rival optimizers: the first category involves well-established and highly-cited optimizers, like Differential evolution; and the second category contains recently-published algorithms, like African Vultures Optimization Algorithm. This comparison is conducted using several performance metrics, the Wilcoxon rank-sum test and the Friedman mean rank to disclose the MSA’s effectiveness and efficiency. The results of this comparison highlight the effectiveness of this new approach and its potential for future optimization applications. The source codes of the MSA algorithm are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/131833-mantis-search-algorithm-msa.

Meta-heuristic search algorithms in truss optimization: Research on stability and complexity analyses

Although they are among the most researched real world engineering design problems, it is encountered with significant problems in the optimization of structural truss bar problems (TPs). The main reasons for these problems are (i) the studies on the optimization of TPs being carried out with different experimental settings, (ii) the competitor algorithms used in the optimization process being insufficient, and (iii) the stability analysis and computational complexity information of the algorithms are not investigated. It is designed a simulation environment with defined standards and a benchmarking suite consisting of nine TPs of three different types in order to eliminate these problems. It is found optimum solutions for all problems in the benchmark suite and it is introduced feasible solutions for the first time. According to the statistical analysis results, among the seventy-seven competing algorithms, the best ten algorithms showed competitive performance in the optimization of TPs are LSHADE-EpSin, LSHADE-CnEpSin, SHADE, LSHADE, GSK, FDB-AGDE, MPA, LRFDB-COA and BES. According to the results of the stability analysis carried out on feasible solutions, it has been determined the algorithms with the best success rate for planar type; LSHADE, LSHADE-CnEpSin, LSHADE-EpSin and GSK with 100% success rate, LSHADE-CnEpSin, LSHADE-EpSin, LSHADE and SHADE for Space type with 100% success rate, and LSHADE-EpSin with 69% success rate for frequency constrained type. When the performances of the algorithms are evaluated regardless of the problem type, LSHADE-EpSin has been the most stable algorithm with an overall success rate of 90% on the TPs.Click here for the simulation environment containing the source codes of the ten competitive algorithms and the TP benchmark suite proposed in the article: https://www.mathworks.com/matlabcentral/fileexchange/131668-structural-truss-problems-benchmark-suite.

NLBBODE optimizer for accurate and fast modeling of photovoltaic module/string generator and its application to solve real-world constrained optimization problems

In this paper, a new optimizer is presented to quickly and accurately identify parameters of the photovoltaic (PV) module/string models. This optimizer is named Nested Loop Biogeography-based Optimization - Differential Evolution referred to as (NLBBODE). It has been developed to identify the PV parameters with reasonable computational effort and minimum execution time, despite the nonlinearity of the PV system dynamics and the insufficiency of data. In addition, the NLBBODE optimizer is used to solve some engineering design problems known as highly constrained, nonlinear, and non-convex. The weaknesses of the original versions of BBO and DE approaches have been overpassed. Furthermore, the proposed optimizer is compared to the state-of-the-art metaheuristic methods using performance evaluation metrics. The computational resources needed to obtain the optimum solution using NLBBODE are significantly reduced due to the nested loop design. The results obtained prove that the NLBBODE optimizer is a suitable candidate to solve the problem of the PV modeling as well as to solve various real-world constrained optimization problems, with high accuracy and low processor runtime, which is a necessary condition for online applications. For instance, the well-known Photowatt-PWP-201 module model represented in the single diode model, NLBBODE registers a standard deviation value of 1.4682E-17 within a time of 13.9 s. For the STM6-40/36 module model represented in the single diode model, the NLBBODE optimizer records a standard deviation value of 6.191583E-18 within an execution time of 6 s. For the speed reducer design problem, the standard deviation obtained by the NLBBODE is 1.515824E-13 and needs a time of less than 1 s to obtain the optimum solution.

An effective multi-objective bald eagle search algorithm for solving engineering design problems

In this paper, a multi-objective bald eagle search algorithm (MOBES) is proposed. The MOBES introduces an archive mechanism to store the non-dominated solutions obtained by the algorithm. When the archive overflows, remove the most crowded solutions by using the roulette method. The MOBES also adds elite selection strategy to guide other individuals to optimize by selecting elite individuals in the population. The efficiency of MOBES is validated on CEC 2020 benchmark functions, and the results demonstrate that the proposed algorithm is more efficient than its competitors in terms of convergence, diversity and distribution of solutions. The MOBES is also applied to two-objective, tri-objective and four-objective engineering design problems in real world. The results show its superiority in handling challenging multi-objective optimization problems with unknown true Pareto optimal solutions and fronts, and it is more competitive than other algorithms.

Sine cosine algorithm with communication and quality enhancement: Performance design for engineering problems

Abstract In recent years, the sine cosine algorithm (SCA) has become one of the popular swarm intelligence algorithms due to its simple and convenient structure. However, the standard SCA tends to fall into the local optimum when solving complex multimodal tasks, leading to unsatisfactory results. Therefore, this study presents the SCA with communication and quality enhancement, called CCEQSCA. The proposed algorithm includes two enhancement strategies: the communication and collaboration strategy (CC) and the quality enhancement strategy (EQ). In the proposed algorithm, CC strengthens the connection of SCA populations by guiding the search agents closer to the range of optimal solutions. EQ improves the quality of candidate solutions to enhance the exploitation of the algorithm. Furthermore, EQ can explore potential candidate solutions in other scopes, thus strengthening the ability of the algorithm to prevent trapping in the local optimum. To verify the capability of CCEQSCA, 30 functions from the IEEE CEC2017 are analyzed. The proposed algorithm is compared with 5 advanced original algorithms and 10 advanced variants. The outcomes indicate that it is dominant over other comparison algorithms in global optimization tasks. The work in this paper is also utilized to tackle three typical engineering design problems with excellent optimization capabilities. It has been experimentally demonstrated that CCEQSCA works as an effective tool to tackle real issues with constraints and complex search space.

Improved dwarf mongoose optimization algorithm using novel nonlinear control and exploration strategies

The Dwarf Mongoose Optimization Algorithm (DMO) is a popular metaheuristic algorithm utilized to solve real-world problems. However, DMO has limitations such as slow convergence rate, susceptibility to local optima, and poor performance in high-dimensional problems. Aiming at the defects of the DMO, we propose an improved version called IDMO. This paper introduces the best leader and proposes a novel nonlinear control strategy based on the sine function, significantly enhancing the convergence rate while ensuring accuracy. Moreover, we propose a novel exploration strategy to solve the global optimization and high-dimensional problem. To demonstrate the performance of the proposed IDMO, we test on 65 test functions, including CEC2017, CEC2020, CEC2022, and CEC2013 large-scale global optimization. IDMO is compared with three classes of widely recognized algorithms: (1) highly-cited algorithms, namely, GSA, GWO, WOA, and HHO, (2) advanced algorithms, such as CPSOGSA, CSA, AVOA, and SO, (3) high-performance optimizers including L-SHADE, AL-SHADE, LSHADE-SPACMA, and LSHADE-cnEpSin. Moreover, we also compare with the original DMO and variants such as GDNNMOA, CO-DWO, and BDMOSAO. Meanwhile, we apply IDMO to solve 19 engineering design problems from the CEC2020 real-world optimization suite. Experimental results demonstrate that IDMO outperforms other algorithms, exhibiting excellent convergence rate, stability, and accuracy. The effectiveness of IDMO is confirmed by the Friedman mean ranking, showcasing its potential for metaheuristic optimization and real-world engineering design problems.

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.

Mother optimization algorithm: a new human-based metaheuristic approach for solving engineering optimization

This article’s innovation and novelty are introducing a new metaheuristic method called mother optimization algorithm (MOA) that mimics the human interaction between a mother and her children. The real inspiration of MOA is to simulate the mother’s care of children in three phases education, advice, and upbringing. The mathematical model of MOA used in the search process and exploration is presented. The performance of MOA is assessed on a set of 52 benchmark functions, including unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, and the CEC 2017 test suite. The findings of optimizing unimodal functions indicate MOA’s high ability in local search and exploitation. The findings of optimization of high-dimensional multimodal functions indicate the high ability of MOA in global search and exploration. The findings of optimization of fixed-dimension multi-model functions and the CEC 2017 test suite show that MOA with a high ability to balance exploration and exploitation effectively supports the search process and can generate appropriate solutions for optimization problems. The outcomes quality obtained from MOA has been compared with the performance of 12 often-used metaheuristic algorithms. Upon analysis and comparison of the simulation results, it was found that the proposed MOA outperforms competing algorithms with superior and significantly more competitive performance. Precisely, the proposed MOA delivers better results in most objective functions. Furthermore, the application of MOA on four engineering design problems demonstrates the efficacy of the proposed approach in solving real-world optimization problems. The findings of the statistical analysis from the Wilcoxon signed-rank test show that MOA has a significant statistical superiority compared to the twelve well-known metaheuristic algorithms in managing the optimization problems studied in this paper.

Net-HDMR Metamodeling Method for High-Dimensional Problems

Abstract Metamodel technology provides an efficient method to approximate complex engineering design problems. However, the approximation for high-dimensional problems usually requires a large number of samples for most traditional metamodeling methods, which leads to the difficulty of “curse of dimensionality.” To address the aforementioned issue, this paper presents the Net-high dimension model representation (HDMR) method based on the Cut-HDMR framework. Compared with traditional HDMR modeling, the Net-HDMR method incorporates two novel modeling approaches that improve the modeling efficiency of high-dimensional problems. The first approach enhances the modeling accuracy of HDMR by using the net function interpolation method to decompose the component functions into a series of one-dimensional net functions. The second approach adopts the CV-Voronoi sequence sampling method to effectively represent one-dimensional net functions with limited samples. Overall, the proposed method transforms complex high-dimensional problems into fitting finite one-dimensional splines, thereby increasing the modeling efficiency while ensuring approximate accuracy. Six numerical benchmark examples with different dimensions are examined to demonstrate the accuracy and efficiency of the proposed Net-HDMR. An engineering problem of thermal stress and deformation analysis for a jet engine turbine blade was introduced to verify the engineering feasibility of the proposed Net-HDMR.

Gradient-Based Trade-Off Design for Engineering Applications

The goal of the trade-off design method presented in this study is to achieve newly targeted performance requirements by modifying the current values of the design variables. The trade-off design problem is formulated in the framework of Sequential Quadratic Programming. The method is computationally efficient as it is gradient-based, which, however, requires the performance functions to be differentiable. A new equation to calculate the scale factor to control the size of the design variables is introduced in this study, which can ensure the new design achieves the targeted performance objective. Three formal approaches are developed in this study for trade-off design to handle various design scenarios, which include one that can handle cases with linearly dependent constraints and with more constraints than the number of design variables. Three engineering design problems are presented as examples to validate and demonstrate the use of these trade-off approaches to find the best way to adjust the design variables to meet the revised performance requirements.

Enhanced opposition-based grey wolf optimizer for global optimization and engineering design problems

A recently developed swarm-based meta-heuristic algorithm namely Grey Wolf Optimization algorithm (GWO), which is based on the hunting and leadership behaviours of the grey wolves in nature, has shown superior performance when compared with existing meta-heuristic algorithms. However, like other approaches, the GWO has the limitation of poor exploitation ability and being stuck in local optima when solving challenging optimization problems. To overcome these limitations, a novel technique, namely “Enhanced Opposition-Based Learning” (EOBL), has been proposed and is implemented with the GWO algorithm. The EOBL technique is largely inspired by Opposition-Based Learning (OBL) and Random Opposition-Based Learning (ROBL) techniques to efficiently balance exploration and exploitation. As a result, the Enhanced Opposition-Based Grey Wolf Optimizer (EOBGWO), an innovative approach, is proposed to increase the effectiveness of the conventional GWO algorithm. To test the efficiency of the proposed EOBGWO method, it has been tested on the standard IEEECEC2005, IEEECEC2017, and IEEECEC2019 test functions, along with several real-life engineering design problems. Furthermore, to evaluate the effectiveness and stability of the proposed technique, it has been evaluated on the challenging IEEECEC2008 special session on large scale global optimization problems. The experimental outcomes and statistical measures such as the t-test and Wilcoxon rank-sum test demonstrate that the proposed EOBGWO method outperforms the other state-of-the-art algorithms in both global optimization and engineering design problems.

CROWDSOURCING ENGINEERING DESIGN PROBLEMS: LEARNING FROM EXPERIENCES

AbstractThe availability of digital data in the fourth industrial revolution brings different trends with new opportunities and challenges for the engineering design community. As an opportunity, these trends would impact engineering design. However, the challenge is finding applications for these trends in engineering design. Crowdsourcing is one of the trends inspired by digital data. It is outsourcing an individually performed task to be mass-performed. This paper explores the application of crowdsourcing in identifying engineering design problems. Identifying an engineering design problem is an aspect of engineering design considered challenging but necessary for inventions. Secondary data from 63 invention-related cases and an interview with a renowned UK inventor are presented. The data contains scenarios on how the engineering design problems solved to qualify for a UK patent grant or application are identified. Lessons from the case studies are presented and discussed, especially regarding crowdsourcing engineering design problems. These seem to be promising ways of supporting the identification of new engineering design problems with inventive benefits once solved.