Enhanced grasshopper optimization algorithm using elite opposition-based learning for solving real-world engineering problems

Yıldız, Betül Sultan; Pholdee, Nantiwat; Bureerat, Sujin; Yıldız, Ali Rıza; Sait, Sadiq M.

doi:10.1007/s00366-021-01368-w

Cited by 115 publications

(36 citation statements)

References 54 publications

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“…The car crashworthiness design problem aims to minimize the weight by optimizing eleven influence variables [ 58 ], including the thickness of B-Pillar inner (x1), B-pillar reinforcement (x2), floor side inner (x3), cross members (x4), door beam (x5), door beltline reinforcement (x6) and roof rail (x7), materials of B-Pillar inner (x8) and floor side inner (x9), barrier height (x10), and barrier hitting position (x11). This problem can be formulated as follows.…”

Section: Experiments On Industrial Engineering Design Problemsmentioning

confidence: 99%

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An Improved Teaching-Learning-Based Optimization Algorithm with Reinforcement Learning Strategy for Solving Optimization Problems

Wang

Liu

et al. 2022

Computational Intelligence and Neuroscience

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This paper presents an improved teaching-learning-based optimization (TLBO) algorithm for solving optimization problems, called RLTLBO. First, a new learning mode considering the effect of the teacher is presented. Second, the Q-Learning method in reinforcement learning (RL) is introduced to build a switching mechanism between two different learning modes in the learner phase. Finally, ROBL is adopted after both the teacher and learner phases to improve the local optima avoidance ability of RLTLBO. These two strategies effectively enhance the convergence speed and accuracy of the proposed algorithm. RLTLBO is analyzed on 23 standard benchmark functions and eight CEC2017 test functions to verify the optimization performance. The results reveal that proposed algorithm provides effective and efficient performance in solving benchmark test functions. Moreover, RLTLBO is also applied to solve eight industrial engineering design problems. Compared with the basic TLBO and seven state-of-the-art algorithms, the results illustrate that RLTLBO has superior performance and promising prospects for dealing with real-world optimization problems. The source codes of the RLTLBO are publicly available at https://github.com/WangShuang92/RLTLBO.

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Section: Experiments On Industrial Engineering Design Problemsmentioning

confidence: 99%

“…The RLTLBO and DE, GA, FA, CS [ 59 ], GOA, and EOBL-GOA [ 58 ] are applied to solve the car crashworthiness problem. As shown in Table 14 , compared to other methods, the proposed RLTLBO achieves the best result than others.…”

Section: Experiments On Industrial Engineering Design Problemsmentioning

confidence: 99%

An Improved Teaching-Learning-Based Optimization Algorithm with Reinforcement Learning Strategy for Solving Optimization Problems

Wang

Liu

et al. 2022

Computational Intelligence and Neuroscience

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“…Elite opposition-based learning (EOBL) is an innovative approach in the field of computational intelligence, and it has been applied in many metaheuristic algorithms [31][32][33]. An EOBL strategy is mainly used to improve the diversity of population by evaluating the fitness function of the current and opposition solutions, and then the better solutions are selected as the initial individual.…”

Section: Elite Opposition-based Learningmentioning

confidence: 99%

Indoor High-Precision 3D Positioning System Based on Visible-Light Communication Using Improved Whale Optimization Algorithm

et al. 2022

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Visible-light communication (VLC) is a promising method for indoor positioning. The received signal strength algorithm is the most widely used localization algorithm in visible-light positioning (VLP) systems. However, in a VLP system, the photodiode (PD) will have a small rotation angle during movement, which will result in a massive positioning error ignoring the angle. In this study, a three-dimensional (3D) indoor VLP system using an improved whale optimization algorithm (IWOA) is proposed to reduce the error caused by the PD rotation. Firstly, the model of the VLC system with the PD rotation angles is introduced. Secondly, a novel IWOA with an elite opposition-based learning strategy and Lévy flight strategy is proposed. The convergence speed and accuracy of the WOA are improved. Lastly, the IWOA algorithm is efficiently utilized to address the problem with the PD rotation in the indoor VLP system. Simulation results show that the average error of 3D positioning is 2.14 cm with no PD rotation. When the PD has a rotation angle, the average positioning error estimated by ignoring the rotation angle is 27.14 cm, while that estimated by considering the rotation angle is 7.85 cm. In the VLP system, the positioning error with the PD rotation angle is effectively reduced by the proposed algorithm, which can be applied in a variety of indoor location scenes.

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“…The three-bar truss design is a well-known optimization problem in the realm of civil engineering (see Figure 12). The problem is very popular and has been used to benchmark optimization methods in numerous papers, most recently in [65][66][67][68]. The main goal of this problem is to decrease the weight of a three-bar truss by taking two structural characteristics into account.…”

Section: Three-bar Truss Design Problemmentioning

confidence: 99%

Agent State Flipping Based Hybridization of Heuristic Optimization Algorithms: A Case of Bat Algorithm and Krill Herd Hybrid Algorithm

Damaševičius

Maskeliūnas

2021

Algorithms

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This paper describes a unique meta-heuristic technique for hybridizing bio-inspired heuristic algorithms. The technique is based on altering the state of agents using a logistic probability function that is dependent on an agent’s fitness rank. An evaluation using two bio-inspired algorithms (bat algorithm (BA) and krill herd (KH)) and 12 optimization problems (cross-in-tray, rotated hyper-ellipsoid (RHE), sphere, sum of squares, sum of different powers, McCormick, Zakharov, Rosenbrock, De Jong No. 5, Easom, Branin, and Styblinski–Tang) is presented. Furthermore, an experimental evaluation of the proposed scheme using the industrial three-bar truss design problem is presented. The experimental results demonstrate that the hybrid scheme outperformed the baseline algorithms (mean rank for the hybrid BA-KH algorithm is 1.279 vs. 1.958 for KH and 2.763 for BA).

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Enhanced grasshopper optimization algorithm using elite opposition-based learning for solving real-world engineering problems

Cited by 115 publications

References 54 publications

An Improved Teaching-Learning-Based Optimization Algorithm with Reinforcement Learning Strategy for Solving Optimization Problems

An Improved Teaching-Learning-Based Optimization Algorithm with Reinforcement Learning Strategy for Solving Optimization Problems

Indoor High-Precision 3D Positioning System Based on Visible-Light Communication Using Improved Whale Optimization Algorithm

Agent State Flipping Based Hybridization of Heuristic Optimization Algorithms: A Case of Bat Algorithm and Krill Herd Hybrid Algorithm

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