MOMRFO: Multi-objective Manta ray foraging optimizer for handling engineering design problems

Got, Adel; Zouache, Djaafar; Moussaoui, Abdelouahab

doi:10.1016/j.knosys.2021.107880

Cited by 28 publications

(8 citation statements)

References 26 publications

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“…Kahraman et al used the MRFO algorithm, which they developed with the crowd-distance-based Pareto archiving strategy, to solve the CEC 2020 benchmarking functions and the multi-objective optimal power flow problem [28]. Got et al proposed MRFO, which they developed with external archive and grid mechanisms, for multi-purpose problems [29]. Elaziz et al developed the algorithm by integrating the triangle mutation operator and orthogonal learning strategies into MRFO and tested it on CEC functions and engineering problems [30].…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluations of the Manta Ray Foraging Optimization Algorithm in Real-World Constrained Optimization Problems

Yıldızdan

2024

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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Metaheuristic algorithms are often preferred for solving constrained engineering design optimization problems. The most important reason for choosing these algorithms is that they guarantee a satisfactory response within a reasonable time. The swarm intelligence-based manta ray foraging optimization algorithm (MRFO) is a metaheuristic algorithm proposed to solve engineering applications. In this study, the performance of MRFO is evaluated on 19 mechanical engineering optimization problems in the CEC2020 real-world constrained optimization problem suite. In order to increase the MRFO performance, three modifications are made to the algorithm; in this way, the enhanced manta ray foraging optimization (EMRFO) algorithm is proposed. The effects of the modifications made are analyzed and interpreted separately. Its performance has been compared with the algorithms in the literature, and it has been shown that EMRFO is a successful and preferable algorithm for this problem suite.

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Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluations of the Manta Ray Foraging Optimization Algorithm in Real-World Constrained Optimization Problems

Yıldızdan

2024

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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“…In this study, a MOMRFO [36] was used to optimize the developed multi-objective cutting parameter optimization model. MOMRFO is an improved multi-objective version based on MRFO [37].…”

Section: Momrfo Algorithm and Cutting Parameter Optimization Processmentioning

confidence: 99%

Cutting Energy Consumption Modeling by Considering Tool Wear and Workpiece Material Properties for Multi-Objective Optimization of Machine Tools

Meng,

Dong,

Sun

et al. 2024

Coatings

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The increasing demand for energy is leading to global depletion of fossil fuels and growing environmental pressures, which are issues that need to be addressed. Machine tools are basic energy-consuming equipment in manufacturing systems. However, existing theoretical models ignore tool wear as well as workpiece material properties. This makes it difficult to further improve the accuracy of the model. Therefore, this study begins with the point of view of energy dissipation in the metal material removal process. A milling power model for computer numerical control (CNC) machines, considering tool wear and workpiece material properties during machining, is established. At the same time, milling is taken as the research object. A multi-objective cutting parameter optimization model is established to ensure the surface quality of the workpiece. In addition, the cutting energy consumption is taken into account in the developed models. Based on the multi-objective manta ray foraging optimization algorithm (MOMRFO), the Pareto-optimal solution set under multiple cutting conditions is solved. Finally, the experimental results of optimized parameters are compared with empirical parameters. The average prediction accuracy of the proposed energy consumption prediction model is above 91%. The experiments show that machining quality improves by optimizing the cutting parameters, with SEC, MRR, and Ra increasing by more than 44%, 53%, and 38%, respectively. The goals of reducing energy consumption and increasing productivity are achieved.

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“…The four-bar plane truss design problem has been widely used to evaluate and verify the engineering application potential of the algorithm [55][56][57] . This problem contains four joint points and four structural members, takes the cross-sectional area of the structural members as the design variables, and takes the synchronous minimization of the volume of the plane truss and the vertical displacement of the node as the optimization objective functions.…”

Section: Application Experimentsmentioning

confidence: 99%

Adaptive Sampling Immune Algorithm for Multi-Objective Probabilistic Optimization

Zhang,

Pan,

Zhang

2024

Preprint

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In this paper, for a general type of multi-objective probabilistic optimization problem without any prior noisy information, which has an extensive engineering application background and needs to be solved urgently, we propose a small-population immune algorithm with adaptive sampling to solve. In the design. First, we design a small-population immune algorithm framework inspired by the response mechanism of adaptive immunity. Second, we design an adaptive sampling scheme that adaptively allocates an appropriate number of samples for each sub-objective function of all individuals in the population to estimate the objective function value. Third, based on the objective function estimates, the dominance levels of all individuals in the population and the crowding distances of individuals in each dominance level are determined. Fourth, the clone size, mutation rate, crossover distribution index, and mutation distribution index of an individual are designed to be adaptively determined based on the number of iterations, dominance level, and crowding distance. Cloning, crossover, and mutation operators are implemented for each individual, using simulated binary crossover and polynomial mutation to enhance co-evolution and facilitate information sharing and exchange among all individuals. Fifth, based on the dominance level and crowding distance, the population update strategies are designed to adaptively update the memory set with high-quality individuals and generate a new generation population with good diversity. Finally, based on three theoretical problems and two engineering problems, as well as six representative comparative algorithms, the experimental results show that the proposed algorithm is an optimizer with good competitiveness and application potential, and has few parameters, less sample consumption, strong noise suppression ability, and high search efficiency.

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MOMRFO: Multi-objective Manta ray foraging optimizer for handling engineering design problems

Cited by 28 publications

References 26 publications

Performance Evaluations of the Manta Ray Foraging Optimization Algorithm in Real-World Constrained Optimization Problems

Performance Evaluations of the Manta Ray Foraging Optimization Algorithm in Real-World Constrained Optimization Problems

Cutting Energy Consumption Modeling by Considering Tool Wear and Workpiece Material Properties for Multi-Objective Optimization of Machine Tools

Adaptive Sampling Immune Algorithm for Multi-Objective Probabilistic Optimization

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