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Relevant bibliographies by topics / MULTI-OBJECTIVE OPTIMIZATION (MOPSO)

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Journal articles

Academic literature on the topic 'MULTI-OBJECTIVE OPTIMIZATION (MOPSO)'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "MULTI-OBJECTIVE OPTIMIZATION (MOPSO)"

1

Trưởng, Nguyễn Huy, and Dinh-Nam Dao. "New hybrid between NSGA-III with multi-objective particle swarm optimization to multi-objective robust optimization design for Powertrain mount system of electric vehicles." Advances in Mechanical Engineering 12, no. 2 (2020): 168781402090425. http://dx.doi.org/10.1177/1687814020904253.

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In this study, a new methodology, hybrid NSGA-III with multi-objective particle swarm optimization (HNSGA-III&MOPSO), has been developed to design and achieve cost optimization of Powertrain mount system stiffness parameters. This problem is formalized as a multi-objective optimization problem involving six optimization objectives: mean square acceleration and mean square displacement of the Powertrain mount system. A hybrid HNSGA-III&MOPSO is proposed with the integration of multi-objective particle swarm optimization and a genetic algorithm (NSGA-III). Several benchmark functions are

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Zeltni, Kamel, and Souham Meshoul. "Multi-Objective Cuckoo Search Under Multiple Archiving Strategies." International Journal of Computational Intelligence and Applications 15, no. 04 (2016): 1650020. http://dx.doi.org/10.1142/s1469026816500206.

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Cuckoo Search (CS) is a recent addition to the field of swarm-based metaheuristics. It has been shown to be an efficient approach for global optimization. Moreover, its application for solving Multi-objective Optimization (MOO) shows very promising results as well. In multi-objective context, a bounded archive is required to store the set of nondominated solutions. But, what is the best archiving strategy to use in order to maintain a bounded set with good characteristics is a critical issue that may lead to a questionable choice. In this work, the behavior of the developed multi-objective CS

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Alabbadi, Afra A., and Maysoon F. Abulkhair. "Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing." Algorithms 14, no. 3 (2021): 77. http://dx.doi.org/10.3390/a14030077.

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Recently, with the development of mobile devices and the crowdsourcing platform, spatial crowdsourcing (SC) has become more widespread. In SC, workers need to physically travel to complete spatial–temporal tasks during a certain period of time. The main problem in SC platforms is scheduling a set of proper workers to achieve a set of spatial tasks based on different objectives. In actuality, real-world applications of SC need to optimize multiple objectives together, and these objectives may sometimes conflict with one another. Furthermore, there is a lack of research dealing with the multi-ob

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Anh, Ho Pham Huy, and Cao Van Kien. "Optimal energy management of microgrid using advanced multi-objective particle swarm optimization." Engineering Computations 37, no. 6 (2020): 2085–110. http://dx.doi.org/10.1108/ec-05-2019-0194.

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Purpose The purpose of this paper is to propose an optimal energy management (OEM) method using intelligent optimization techniques applied to implement an optimally hybrid heat and power isolated microgrid. The microgrid investigated combines renewable and conventional power generation. Design/methodology/approach Five bio-inspired optimization methods include an advanced proposed multi-objective particle swarm optimization (MOPSO) approach which is comparatively applied for OEM of the implemented microgrid with other bio-inspired optimization approaches via their comparative simulation resul

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Wang, Yule, Wanliang Wang, Ijaz Ahmad, and Elsayed Tag-Eldin. "Multi-Objective Quantum-Inspired Seagull Optimization Algorithm." Electronics 11, no. 12 (2022): 1834. http://dx.doi.org/10.3390/electronics11121834.

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Objective solutions of multi-objective optimization problems (MOPs) are required to balance convergence and distribution to the Pareto front. This paper proposes a multi-objective quantum-inspired seagull optimization algorithm (MOQSOA) to optimize the convergence and distribution of solutions in multi-objective optimization problems. The proposed algorithm adopts opposite-based learning, the migration and attacking behavior of seagulls, grid ranking, and the superposition principles of quantum computing. To obtain a better initialized population in the absence of a priori knowledge, an opposi

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Thawkar, Shankar, Law Kumar Singh, and Munish Khanna. "Multi-objective techniques for feature selection and classification in digital mammography." Intelligent Decision Technologies 15, no. 1 (2021): 115–25. http://dx.doi.org/10.3233/idt-200049.

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Feature selection is a crucial stage in the design of a computer-aided classification system for breast cancer diagnosis. The main objective of the proposed research design is to discover the use of multi-objective particle swarm optimization (MOPSO) and Nondominated sorting genetic algorithm-III (NSGA-III) for feature selection in digital mammography. The Pareto-optimal fronts generated by MOPSO and NSGA-III for two conflicting objective functions are used to select optimal features. An artificial neural network (ANN) is used to compute the fitness of objective functions. The importance of fe

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Chen, Shuming, Wenbo Zhu, and Yabing Cheng. "Multi-Objective Optimization of Acoustic Performances of Polyurethane Foam Composites." Polymers 10, no. 7 (2018): 788. http://dx.doi.org/10.3390/polym10070788.

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Polyurethane (PU) foams are widely used as acoustic package materials to eliminate vehicle interior noise. Therefore, it is important to improve the acoustic performances of PU foams. In this paper, the grey relational analysis (GRA) method and multi-objective particle swarm optimization (MOPSO) algorithm are applied to improve the acoustic performances of PU foam composites. The average sound absorption coefficient and average transmission loss are set as optimization objectives. The hardness and content of Ethylene Propylene Diene Monomer (EPDM) and the content of deionized water and modifie

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Mahmoud, Ali, and Xiaohui Yuan. "SHAPE OPTIMIZATION OF ROCKFILL DAM WITH RUBIK CUBE REPRODUCTION BASED MULTI-OBJECTIVE PARTICLE SWARM ALGORITHM." ASEAN Engineering Journal 11, no. 4 (2021): 204–31. http://dx.doi.org/10.11113/aej.v11.18021.

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A rockfill dam's quality and its economic aspects are inextricably interwoven with each other. Approaching the optimal design of a rockfill dam paves the path to achieve the best quality with the fewest expenses. Choosing the Sardasht rockfill dam as a case study, two semi-empirical models are presented for seepage and safety factor. These two models, together with construction costs, were employed as three objective functions for the Sardasht rockfill dam's shape optimization. Optimization was handled using a robust multi-objective particle swarm optimization algorithm (RCR-MOPSO). A new repr

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Mouassa, Souhil, and Tarek Bouktir. "Multi-objective ant lion optimization algorithm to solve large-scale multi-objective optimal reactive power dispatch problem." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 1 (2019): 304–24. http://dx.doi.org/10.1108/compel-05-2018-0208.

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Purpose In the vast majority of published papers, the optimal reactive power dispatch (ORPD) problem is dealt as a single-objective optimization; however, optimization with a single objective is insufficient to achieve better operation performance of power systems. Multi-objective ORPD (MOORPD) aims to minimize simultaneously either the active power losses and voltage stability index, or the active power losses and the voltage deviation. The purpose of this paper is to propose multi-objective ant lion optimization (MOALO) algorithm to solve multi-objective ORPD problem considering large-scale

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Majumder, Arindam. "Optimization of Modern Manufacturing Processes Using Three Multi-Objective Evolutionary Algorithms." International Journal of Swarm Intelligence Research 12, no. 3 (2021): 96–124. http://dx.doi.org/10.4018/ijsir.2021070105.

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The optimization in manufacturing processes refers to the investigation of multiple responses simultaneously. Therefore, it becomes very necessary to introduce a technique that can solve the multiple response optimization problem efficiently. In this study, an attempt has been taken to find the application of three newly introduced multi-objective evolutionary algorithms, namely multi-objective dragonfly algorithm (MODA), multi-objective particle swarm optimization algorithm (MOPSO), and multi-objective teaching-learning-based optimization (MOTLBO), in the modern manufacturing processes. For t

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