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Academic literature on the topic 'MULTI-OBJECTIVE OPTIMIZATION (MOPSO)'
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Journal articles on the topic "MULTI-OBJECTIVE OPTIMIZATION (MOPSO)"
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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 tested, and results reveal that the HNSGA-III&MOPSO is more efficient than the typical multi-objective particle swarm optimization, NSGA-III. Powertrain mount system stiffness parameter optimization with HNSGA-III&MOPSO is simulated, respectively. It proved the potential of the HNSGA-III&MOPSO for Powertrain mount system stiffness parameter optimization problem. The amplitude of the acceleration of the vehicle frame decreased by 22.8%, and the amplitude of the displacement of the vehicle frame reduced by 12.4% compared to the normal design case. The calculation time of the algorithm HNSGA-III&MOPSO is less than the algorithm NSGA-III, that is, 5 and 6 h, respectively, compared to the algorithm multi-objective particle swarm optimization.
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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 is studied under several archiving strategies. An extensive experimental study has been conducted using several test problems and two performance metrics related to convergence and diversity. A nonparametric test for statistical analysis is performed. In addition, we used a Multi-Objective Particle Swarm Optimization (MOPSO) for further analysis and comparison. The results revealed that archiving strategies play an important role as they can impact differently on the quality of obtained fronts depending on the problem’s characteristics. Also, this study confirms that the proposed MOCS algorithm is a very promising approach for MOPs compared to the widely used MOPSO.
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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-objective optimization (MOO) problem within an SC environment. Thus, in this work we focused on task scheduling based on multi-objective optimization (TS-MOO) in SC, which is based on maximizing the number of completed tasks, minimizing the total travel costs, and ensuring the balance of the workload between workers. To solve the previous problem, we developed a new method, i.e., the multi-objective task scheduling optimization (MOTSO) model that consists of two algorithms, namely, the multi-objective particle swarm optimization (MOPSO) algorithm with our fitness function Alabbadi, et al. and the ranking strategy algorithm based on the task entropy concept and task execution duration. The main purpose of our ranking strategy is to improve and enhance the performance of our MOPSO. The primary goal of the proposed MOTSO model is to find an optimal solution based on the multiple objectives that conflict with one another. We conducted our experiment with both synthetic and real datasets; the experimental results and statistical analysis showed that our proposed model is effective in terms of maximizing the number of completed tasks, minimizing the total travel costs, and balancing the workload between workers.
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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 results. Findings Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization methods. Moreover, the proposed MOPSO is successfully applied to perform 24-h OEM microgrid. The simulation results also display the merits of the real time optimization along with the arbitrary of users’ selection as to satisfy their power requirement. Originality/value This paper focuses on the OEM of a designed microgrid using a newly proposed modified MOPSO algorithm. Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization approaches.
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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 opposite-based learning mechanism is used for initialization. The proposed algorithm uses nonlinear migration and attacking operation, simulating the behavior of seagulls for exploration and exploitation. Moreover, the real-coded quantum representation of the current optimal solution and quantum rotation gate are adopted to update the seagull population. In addition, a grid mechanism including global grid ranking and grid density ranking provides a criterion for leader selection and archive control. The experimental results of the IGD and Spacing metrics performed on ZDT, DTLZ, and UF test suites demonstrate the superiority of MOQSOA over NSGA-II, MOEA/D, MOPSO, IMMOEA, RVEA, and LMEA for enhancing the distribution and convergence performance of MOPs.
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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 features selected by MOPSO and NSGA-III are assessed using artificial neural networks. The experimental results show that MOPSO based optimization is superior to NSGA-III. MOPSO achieves high accuracy with a 55% feature reduction. MOPSO based feature selection and classification deliver an efficiency of 97.54% with 98.22% sensitivity, 96.82% specificity, 0.9508 Cohen’s kappa coefficient, and area under curve AZ= 0.983 ± 0.003.
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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 modified isocyanate (MDI) are selected as design variables. The optimization process of GRA method is based on the orthogonal arrays L9(34), and the MOPSO algorithm is based on the Response Surface (RS) surrogate model. The results show that the acoustic performances of PU foam composites can be improved by optimizing the synthetic formula. Meanwhile, the results that were obtained by GRA method show the degree of influence of the four design variables on the optimization objectives, and the results obtained by MOPSO algorithm show the specific effects of the four design variables on the optimization objectives. Moreover, according to the confirmation experiment, the optimal synthetic formula is obtained by MOPSO algorithm when the weight coefficient of the two objectives set as 0.5.
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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 reproducing method inspired by a Rubik's cube shape (RCR) and NSGA-III are building blocks of RCR-MOPSO. Three benchmark problems and two real-world problems were solved using RCR-MOPSO and compared with NSGA-III and MOPSO to ensure the performance of RCR-MOPSO. The solution quality and performance of RCR-MOPSO are significantly better than the original MOPSO and close to NSGA-III. Nevertheless, RCR-MOPSO recorded a 38% shorter runtime than NSGA-III. RCR-MOPSO presented a set of non-dominated solutions as final results for the Sardasht rockfill dam shape optimization. Due to the defined constraints, all solutions dominate the original design. Regarding the final results, compared with Sardasht dam's original design, the construction price was reduced by 31.12% on average, while seepage and safety factor improved by 15.84% and 27.78% on average, respectively.
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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 power system in an effort to achieve a good performance with stable and secure operation of electric power systems. Design/methodology/approach A MOALO algorithm is presented and applied to solve the MOORPD problem. Fuzzy set theory was implemented to identify the best compromise solution from the set of the non-dominated solutions. A comparison with enhanced version of multi-objective particle swarm optimization (MOEPSO) algorithm and original (MOPSO) algorithm confirms the solutions. An in-depth analysis on the findings was conducted and the feasibility of solutions were fully verified and discussed. Findings Three test systems – the IEEE 30-bus, IEEE 57-bus and large-scale IEEE 300-bus – were used to examine the efficiency of the proposed algorithm. The findings obtained amply confirmed the superiority of the proposed approach over the multi-objective enhanced PSO and basic version of MOPSO. In addition to that, the algorithm is benefitted from good distributions of the non-dominated solutions and also guarantees the feasibility of solutions. Originality/value The proposed algorithm is applied to solve three versions of ORPD problem, active power losses, voltage deviation and voltage stability index, considering large -scale power system IEEE 300 bus.
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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 this purpose, these algorithms are used to solve five instances of modern manufacturing process—CNC process, continuous drive friction welding process, EDM process, injection molding process, and friction stir welding process—during this study. The performance of these algorithms is measured using three parameters, namely coverage to two sets, spacing, and CPU time. The obtained experimental results initially reveal that MODA, MOPSO, and MOTLBO provide better solutions as compared to widely used nondominated sorting genetic algorithm II (NSGA-II). Moreover, this study also shows the superiority of MODA over MOPSO and MOTLBO while considering coverage to two sets and CPU time. Further, in terms of spacing a marginally inferior performance is observed in MODA as compared to MOPSO and MOTLBO.