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

Eesa, Adel Sabry; Hassan, Masoud Muhammed; Arabo, Wahab Khalaf

doi:10.1016/j.asoc.2023.110252

Cited by 7 publications

(3 citation statements)

References 78 publications

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“…Several approaches and optimization techniques have been employed to address the Welded Beam Design Problem. These include mathematical programming techniques, such as linear programming, nonlinear programming, or mixed-integer programming, as well as metaheuristic algorithms like genetic algorithms, particle swarm optimization, and simulated annealing (Eesa et al, 2023). Previous approaches have provided valuable insights into the problem and have demonstrated varying degrees of success in finding optimal or near-optimal solutions.…”

Section: Welded Beam Design (Wbd)mentioning

confidence: 99%

Exploring the Impact of Big Bang-Big Crunch Algorithm Parameters on Welded Beam Design Problem Resolution

Almufti

2023

ACAD J NAWROZ UNIV

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A Metaheuristic Optimization is a group of algorithms that are widely studied and employed in the scientific literature. Typically, metaheuristics algorithms utilize stochastic operators that make each iteration unique, and they frequently contain controlling parameters that have an impact on the convergence process since their impacts are mostly neglected in most optimization literature, making it difficult to draw conclusions. This paper introduced the Big Bang-Big Crunch (BB-BC) metaheuristic algorithm to evaluate the performance of a metaheuristic algorithm in relation to its control parameter. It also demonstrates the effects of varying the values of BB-BC in solving. The "Welded Beam Design problem" is a well-known engineering optimization problem that is classified as a Single-Objective Constrained Optimization issue. Multiple starting parameter values for the BB-BC are evaluated as part of the experimental findings. This is done in an attempt to find the algorithm's optimal starting settings. The lowest, maximum, and mean values of the penalized objective functions are then computed. Finally, the BB-BC results are compared with various metaheuristics algorithms.

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Section: Welded Beam Design (Wbd)mentioning

confidence: 99%

Exploring the Impact of Big Bang-Big Crunch Algorithm Parameters on Welded Beam Design Problem Resolution

Almufti

2023

ACAD J NAWROZ UNIV

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“…These include many mathematical programming techniques, such as linear programming, nonlinear programming, and mixed-integer programming. Additionally, metaheuristic algorithms like genetic algorithms, simulated annealing, and particle swarm optimization are widely used [15].…”

Section: Welded Beam Design Problem Formulationmentioning

confidence: 99%

Optimization of Welded Beam Design Problem Using Water Evaporation Optimization Algorithm

A. H. Alkurdi

2023

ACAD J NAWROZ UNIV

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This paper introduces a novel approach to tackle the Welded Beam Design Problem through the application of the Water Evaporation Optimization Algorithm (WEOA), a nature-inspired metaheuristic. The problem involves finding the optimal dimensions of a welded beam that can support a given load while minimizing its weight. The Water Evaporation Optimization Algorithm draws inspiration from the evaporation process and water droplet movement in nature. The design is formulated as an optimization challenge with beam dimensions as variables and incorporate constraints such as allowable stress and geometric limitations. The fitness function is tailored to evaluate each candidate solution based on load-bearing capacity and weight. To demonstrate the efficacy of the proposed method, extensive experimental evaluations are conducted. Comparisons with traditional optimization techniques highlight the WEOA's superior convergence and global search capabilities. Real-world case studies further illustrate the practical applicability of the optimized welded beam designs, showcasing their cost-effectiveness and high-performance characteristics. The results underscore the potential of the Water Evaporation Optimization Algorithm as a robust and efficient tool for tackling the welded beam design problem. The approach provides engineers with valuable support in achieving optimized beam designs, leading to improved structural performance and material utilization.

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“…Traditional optimization algorithms, such as the steepest descent method and variable metric method, are gradually unable to meet the needs of engineering optimization as the problems faced by the engineering field are becoming increasingly complex [1]. Recently, intelligent optimization algorithms, as a new evolutionary computational tool, have been widely used in engineering optimization problems [2][3][4]. As one of the heuristic search algorithms, the Beetle Antennae Search (BAS) algorithm enjoys the advantages of fast convergence, simple model, low computational complexity, and robust searching ability [5,6], and is widely employed in several engineering optimization problems with high research value [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Strategy Improved Beetle Antennae Search Algorithm and Application

Shan,

Lu,

et al. 2024

Applied Sciences

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The multi-dimensional optimization of mechanisms is a typical optimization problem encountered in mechanical design. Herein, the Hybrid strategy improved Beetle Antennae Search (HSBAS) algorithm is proposed to solve the multi-dimensional optimization problems encountered in structural design. To solve the problems of local optimization and low accuracy of the high-dimensional solution of the Beetle Antennae Search (BAS) algorithm, the algorithm adopts the adaptive step strategy, multi-directional exploration strategy, and Lens Opposition-Based Learning strategy, significantly reducing the probability of the algorithm falling into the local optimum and improving its global search capability. Comparative experiments of the improved algorithm are carried out by selecting eleven benchmark test functions. HSBAS can reach 1 × 10−22 accuracy from the optimal value when dealing with low-dimensional functions. It can also obtain 1 × 10−2 accuracy when dealing with high-dimensional functions, significantly improving the algorithm’s capability. According to Friedman’s ranking test result, HSBAS ranks first, which proves that HSBAS is superior to the other three algorithms. The HSBAS algorithm is further used to optimize the design of the altitude compensation module of the gravity compensation device for solar wings, controlling the fluctuation of bearing capacity within 0.25%, which shows that the algorithm can be used as an effective tool for engineering structural optimization problems.

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

Cited by 7 publications

References 78 publications

Exploring the Impact of Big Bang-Big Crunch Algorithm Parameters on Welded Beam Design Problem Resolution

Exploring the Impact of Big Bang-Big Crunch Algorithm Parameters on Welded Beam Design Problem Resolution

Optimization of Welded Beam Design Problem Using Water Evaporation Optimization Algorithm

Hybrid Strategy Improved Beetle Antennae Search Algorithm and Application

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