Robust design of a robot gripper mechanism using new hybrid grasshopper optimization algorithm

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

doi:10.1111/exsy.12666

Cited by 99 publications

(21 citation statements)

References 88 publications

(97 reference statements)

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“…Chakraborty et al [29] designed a modified WOA and applied it to optimize real-world problems from civil and mechanical engineering disciplines. Yıldız et al [30] developed a new approach based on the Grasshopper Optimization Algorithm and Nelder-Mead Algorithm to optimize robot gripper problem with a fast and accurate solution. Additionally, vehicle side crash design, multi-clutch disk, and manufacturing optimization problems were also solved with the developed method.…”

Section: Few Work On Verification Of Performance and Real-world Probl...mentioning

confidence: 99%

Comparative Performance Analysis of Differential Evolution Variants on Engineering Design Problems

et al. 2022

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Because of their superior problem-solving ability, nature-inspired optimization algorithms are being regularly used in solving complex real-world optimization problems. Engineering academics have recently focused on meta-heuristic algorithms to solve various optimization challenges. Among the state-of-the-art algorithms, Differential Evolution (DE) is one of the most successful algorithms and is frequently used to solve various industrial problems. Over the previous 2 decades, DE has been heavily modified to improve its capabilities. Several DE variations secured positions in IEEE CEC competitions, establishing their efficacy. However, to our knowledge, there has never been a comparison of performance across various CEC-winning DE versions, which could aid in determining which is the most successful. In this study, the performance of DE and its eight other IEEE CEC competition-winning variants are compared. First, the algorithms have evaluated IEEE CEC 2019 and 2020 bound-constrained functions, and the performances have been compared. One unconstrained problem from IEEE CEC 2011 problem suite and five other constrained mechanical engineering design problems, out of which four issues have been taken from IEEE CEC 2020 non-convex constrained optimization suite, have been solved to compare the performances. Statistical analyses like Friedman's test and Wilcoxon's test are executed to verify the algorithm's ability statistically. Performance analysis exposes that none of the DE variants can solve all the problems efficiently. Performance of SHADE and ELSHADE-SPACMA are considerable among the methods used for comparison to solve such mechanical design problems.

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Section: Few Work On Verification Of Performance and Real-world Probl...mentioning

confidence: 99%

Comparative Performance Analysis of Differential Evolution Variants on Engineering Design Problems

et al. 2022

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“…Using innovative or hybrid techniques, researchers tried to address their problems which many of the proposed methods can be used in other scientific or engineering domains. Self‐adaptive many‐objective based on decomposition in Champasak et al (2020), Multi‐surrogate‐assisted metaheuristics for crashworthiness optimisation in Aye et al (2019), Butterfly optimization algorithm for optimum vehicle designs in Yıldız, Yıldız, Albak, et al (2020), Hybrid Taguchi‐salp swarm optimization algorithm proposed in Yıldız and Erdaş (2021), hybrid grasshopper optimization algorithm in Yildiz et al (2021), arithmetic optimization algorithm, the slime mould optimization algorithm and the marine predators algorithm which are described in Gürses et al (2021), seagull optimization algorithm (SOA) which is proposed for optimizing the shape of a vehicle bracket in Panagant et al (2020) and the Henry gas solubility optimization (HGSO) algorithm in Yıldız, Yıldız, Pholdee, et al (2020).…”

Section: Introductionmentioning

confidence: 99%

Plant competition optimization: A novel metaheuristic algorithm

Rahmani

AliAbdi

2022

Expert Systems

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Plant competition is a fundamental process in plant communities. In one neighbourhood, different plants compete with each other to access shared resources. This paper presents a novel evolutionary algorithm, plant competition optimization (PCO) algorithm, inspired by plant competition processes. In this algorithm, each feasible solution to an optimization problem is assumed to be a plant, with the underlying assumption that each plant grows in competition with its neighbours. In contrast to other techniques inspired by natural phenomena, we attempted to fit the formulation with a known plant growth model, Richards' growth model, and simulate what happens in nature. As the number of plants in a specific area increases, the available resources are decreased, and competition occurs in smaller areas. So, to use this aspect of competition, we develop some mathematical formulation to simulate the decrease of neighbouring area for each plant related to its size to compete on the share resources with the other neighbouring plants. This competition will conduct a smart local search around the most fitted solutions in the optimization context. Furthermore, the reproduction is simulated by producing some seeds in their neighbouring area, which a few of them can migrate to far distances as well. Summing up together, the most powerful plants can grow more. Under competition pressure, their neighbouring area will decrease more than the others, producing more seeds in the next generation. Just as happened in nature, the losers of this competition will die. Our algorithm's efficiency is shown by performing numerical tests on well‐known optimization problems and comparing the results with the other evolutionary algorithms. The results confirm that PCO is effective and efficient in finding sub‐optimal solutions and gives better results than genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), grasshopper optimisation algorithm (GOA), dragonfly algorithm (DA), salp swarm Algorithm (SSA), and comparable results with whale optimization algorithm (WOA) and marine predators algorithm (MPA) on multimodal optimization functions because it efficiently explores the entire search space efficiently and intelligently.

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“…Hybridization, utilising mainly analytical approaches, is a frequent solution for overcoming these restrictions. This approach has proven to be effective in a variety of applications, including internet data clustering [19], power flow [20,21], cybersecurity [22], mechanism [23], and even in the estimate of diode model parameters [24,25]. Despite providing excellent results for extracting parameters from the diode model, metaheuristic methods have several drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Harris Hawk Optimization Combined with Differential Evolution for the Estimation of Solar Cell Parameters

Ndi

Perabi

Ndjakomo

et al. 2022

International Journal of Photoenergy

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In a dynamic shift, lowering reliance on fossil fuels and greenhouse gas emissions is now a top goal. This is accomplished through expanding the usage of renewable energy. Solar photovoltaic (PV) energy is now more than ever at the heart of many cities’ policies. Improving the efficiency of PV systems is a current research goal. The key challenge in rectifying complex systems is to establish a model that correctly reproduces the system’s dynamic behaviour. The goal function and optimization method utilised are indicative of the model parameters’ correctness. This paper presents a mix of differential evolution (DE) and Harris hawk optimisation (HHO). The suggested technique estimates the parameter vector that minimises the objective function to the greatest extent possible. This is for the many diode models. The procedure is validated using experimental data acquired at a known temperature and irradiance. The root mean square error (RMSE) is used to assess the method’s effectiveness. A comparison is made between the objective function of the hybrid approach presented in this publication and previously authorised methods. The strategy utilised is as straightforward as many others stated in our predecessors’ publications, and this applies to both models analysed. When compared to the simple version of the Harris hawk optimizer, this approach allows for more experimentation.

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Robust design of a robot gripper mechanism using new hybrid grasshopper optimization algorithm

Cited by 99 publications

References 88 publications

Comparative Performance Analysis of Differential Evolution Variants on Engineering Design Problems

Comparative Performance Analysis of Differential Evolution Variants on Engineering Design Problems

Plant competition optimization: A novel metaheuristic algorithm

Harris Hawk Optimization Combined with Differential Evolution for the Estimation of Solar Cell Parameters

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