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

Author: Grafiati

Published: 11 September 2023

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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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Fan, Qin Man, and Qin Man Fan. "The Multi-Objective Optimization Design of Leaf Spring of few Piece Variable Cross-Section." Advanced Materials Research 213 (February 2011): 231–35. http://dx.doi.org/10.4028/www.scientific.net/amr.213.231.

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Being the ability of global optimization, MOPSO algorithm have some virtue such as high calculate velocity, good solution quality, great robustness, and so on. In allusion to a leaf spring of few piece variable cross-section, its multi-objective optimization mathematical model was built regarding minimum mass and minimum stiffness deviation as sub-objective functions. Taking the leaf spring of front suspension of a light truck as an example, the Pareto optimal solution set of optimization problem was obtained by using MOPSO algorithm. The optimization results show that the mass of the leaf spr

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Ouyang, Aijia, Kenli Li, Xiongwei Fei, Xu Zhou, and Mingxing Duan. "A Novel Hybrid Multi-Objective Population Migration Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 01 (2015): 1559001. http://dx.doi.org/10.1142/s0218001415590016.

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This paper presents a multi-objective co-evolutionary population migration algorithm based on Good Point Set (GPSMCPMA) for multi-objective optimization problems (MOP) in view of the characteristics of MOPs. The algorithm introduces the theory of good point set (GPS) and dynamic mutation operator (DMO) and adopts the entire population co-evolutionary migration, based on the concept of Pareto nondomination and global best experience and guidance. The performance of the algorithm is tested through standard multi-objective functions. The experimental results show that the proposed algorithm perfo

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CHEN, LEI, and HAI-LIN LIU. "A REGION DECOMPOSITION-BASED MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION ALGORITHM." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 08 (2014): 1459009. http://dx.doi.org/10.1142/s0218001414590095.

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In this paper, a novel multi-objective particle swarm optimization algorithm based on MOEA/D-M2M decomposition strategy (MOPSO-M2M) is proposed. MOPSO-M2M can decompose the objective space into a number of subregions and then search all the subregions using respective sub-swarms simultaneously. The M2M decomposition strategy has two very desirable properties with regard to MOPSO. First, it facilitates the determination of the global best (gbest) for each sub-swarm. A new global attraction strategy based on M2M decomposition framework is proposed to guide the flight of particles by setting an a

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Chen, Taowei, Yiming Yu, and Kun Zhao. "A Multi-objective Particle Swarm Optimization Based on P System Theory." MATEC Web of Conferences 232 (2018): 03039. http://dx.doi.org/10.1051/matecconf/201823203039.

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Particle swarm optimization(PSO) algorithm has been widely applied in solving multi-objective optimization problems(MOPs) since it was proposed. However, PSO algorithms updated the velocity of each particle using a single search strategy, which may be difficult to obtain approximate Pareto front for complex MOPs. In this paper, inspired by the theory of P system, a multi-objective particle swarm optimization (PSO) algorithm based on the framework of membrane system(PMOPSO) is proposed to solve MOPs. According to the hierarchical structure, objects and rules of P system, the PSO approach is use

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Ünal, Ali Nadi, and Gülgün Kayakutlu. "Multi-objective particle swarm optimization with random immigrants." Complex & Intelligent Systems 6, no. 3 (2020): 635–50. http://dx.doi.org/10.1007/s40747-020-00159-y.

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Abstract Complex problems of the current business world need new approaches and new computational algorithms for solution. Majority of the issues need analysis from different angles, and hence, multi-objective solutions are more widely used. One of the recently well-accepted computational algorithms is Multi-objective Particle Swarm Optimization (MOPSO). This is an easily implemented and high time performance nature-inspired approach; however, the best solutions are not found for archiving, solution updating, and fast convergence problems faced in certain cases. This study investigates the pre

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Karimi, Mohammad, Maryam Miriestahbanati, Hamed Esmaeeli, and Ciprian Alecsandru. "Multi-Objective Stochastic Optimization Algorithms to Calibrate Microsimulation Models." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 4 (2019): 743–52. http://dx.doi.org/10.1177/0361198119838260.

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The calibration process for microscopic models can be automatically undertaken using optimization algorithms. Because of the random nature of this problem, the corresponding objectives are not simple concave functions. Accordingly, such problems cannot easily be solved unless a stochastic optimization algorithm is used. In this study, two different objectives are proposed such that the simulation model reproduces real-world traffic more accurately, both in relation to longitudinal and lateral movements. When several objectives are defined for an optimization problem, one solution method may ag

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Yao, Zhi Hui, and Min Zhou. "Applying Multi-Objective Particle Swarm Optimization to Maintenance Scheduling for CNC Machine Tools." Applied Mechanics and Materials 721 (December 2014): 144–48. http://dx.doi.org/10.4028/www.scientific.net/amm.721.144.

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This paper focuses on the maintenance scheduling for CNC machine tools. A bi-objective mathematical model is built with the repair time and maintenance cost. A multi-objective particle swarm optimization (MOPSO), which combines the global best position adaptive selection and local search, is proposed to solve the mathematical model. The results show that MOPSO has a better performance than other method for solving the maintenance scheduling. They also show that MOPSO is an effective algorithm that has strong convergence.

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Chen, Fei, Yanmin Liu, Jie Yang, Meilan Yang, Qian Zhang, and Jun Liu. "Multi-objective particle swarm optimization with reverse multi-leaders." Mathematical Biosciences and Engineering 20, no. 7 (2023): 11732–62. http://dx.doi.org/10.3934/mbe.2023522.

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<abstract> <p>Despite being easy to implement and having fast convergence speed, balancing the convergence and diversity of multi-objective particle swarm optimization (MOPSO) needs to be further improved. A multi-objective particle swarm optimization with reverse multi-leaders (RMMOPSO) is proposed as a solution to the aforementioned issue. First, the convergence strategy of global ranking and the diversity strategy of mean angular distance are proposed, which are used to update the convergence archive and the diversity archive, respectively, to improve the convergence and diversi

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Singh, Dhirendra Pratap. "Optimization of Electric Discharge Machining of Al/Al2O3 Metal Matrix Composites using MOPSO." International Journal of Engineering Research in Mechanical and Civil Engineering (IJERMCE) 9, no. 5 (2022): 39–47. http://dx.doi.org/10.36647/ijermce/09.05.a007.

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In this article, Response Surface Methodology (RSM) and Multi-objective Particle Swarm Optimization (MOPSO) were used to optimize the output response of Material Removal Rate (MRR) and Surface Roughness(SR) of die-sinking Electrical discharge machining (EDM). An aluminum based metal matrix composites, reinforced with alumina, prepared by stir casting, was used for machining on EDM by Copper (Cu) and Titanium (Ti) tool. Box- Behnken Design (BBD) approach of RSM was used to design the experiment by considering four input factors at three levels. This developed model for multi-objective optimizat

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Shang, Junliang, Yiting Li, Yan Sun, Feng Li, Yuanyuan Zhang, and Jin-Xing Liu. "MOPIO: A Multi-Objective Pigeon-Inspired Optimization Algorithm for Community Detection." Symmetry 13, no. 1 (2020): 49. http://dx.doi.org/10.3390/sym13010049.

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Community detection is a hot research direction of network science, which is of great importance to complex system analysis. Therefore, many community detection methods have been developed. Among them, evolutionary computation based ones with a single-objective function are promising in either benchmark or real data sets. However, they also encounter resolution limit problem in several scenarios. In this paper, a Multi-Objective Pigeon-Inspired Optimization (MOPIO) method is proposed for community detection with Negative Ratio Association (NRA) and Ratio Cut (RC) as its objective functions. In

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Welhazi, Yosra, Tawfik Guesmi, and Hsan Hadj Abdallah. "Eigenvalue Assignments in Multimachine Power Systems using Multi-Objective PSO Algorithm." International Journal of Energy Optimization and Engineering 4, no. 3 (2015): 33–48. http://dx.doi.org/10.4018/ijeoe.2015070103.

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Applying multi-objective particle swarm optimization (MOPSO) algorithm to multi-objective design of multimachine power system stabilizers (PSSs) is presented in this paper. The proposed approach is based on MOPSO algorithm to search for optimal parameter settings of PSS for a wide range of operating conditions. Moreover, a fuzzy set theory is developed to extract the best compromise solution. The stabilizers are selected using MOPSO to shift the lightly damped and undamped electromechanical modes to a prescribed zone in the s-plane. The problem of tuning the stabilizer parameters is converted

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Pieprzycki, Adam, and Wiesław Ludwin. "Selected issues of multi-objective WLAN planning." Science, Technology and Innovation 3, no. 2 (2018): 69–78. http://dx.doi.org/10.5604/01.3001.0012.8170.

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The aim of the article is to apply a multicriteria approach to MOO (Multi Objective Optimization) planning for WLAN (Wireless Local Area Network) using selected swarm optimization methods. For this purpose, in the process of searching for the extremum of two criterion functions, which are an optimization index, two swarm algorithms were used: MOCS (Multi Objective Cuckoo Search) and MOPSO (Multi Objective Particle Swarm Optimization). The results were compared with the single-criterion SOO (Single Objective Optimization) range-based network planning based on the regular distribution of TP (tes

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Reddy, M. Janga, and D. Nagesh Kumar. "Performance evaluation of elitist-mutated multi-objective particle swarm optimization for integrated water resources management." Journal of Hydroinformatics 11, no. 1 (2009): 79–88. http://dx.doi.org/10.2166/hydro.2009.042.

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Optimal allocation of water resources for various stakeholders often involves considerable complexity with several conflicting goals, which often leads to multi-objective optimization. In aid of effective decision-making to the water managers, apart from developing effective multi-objective mathematical models, there is a greater necessity of providing efficient Pareto optimal solutions to the real world problems. This study proposes a swarm-intelligence-based multi-objective technique, namely the elitist-mutated multi-objective particle swarm optimization technique (EM-MOPSO), for arriving at

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Leng, Rui, Aijia Ouyang, Yanmin Liu, Lian Yuan, and Zongyue Wu. "A Multi-Objective Particle Swarm Optimization Based on Grid Distance." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 03 (2019): 2059008. http://dx.doi.org/10.1142/s0218001420590089.

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In modern intelligent algorithms and real-industrial applications, there are many fields involving multi-objective particle swarm optimization algorithms, but the conflict between each objective in the optimization process will easily lead to the algorithm falling into local optimal. In order to prevent the algorithm from quickly falling into local optimization and improve the robustness of the algorithm, a multi-objective particle swarm optimization algorithm based on grid distance (GDMOPSO) was proposed, which has to improve the diversity of the algorithm and the search ability. Based on the

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Haeri, Abdorrahman, and Reza Tavakkoli-Moghaddam. "DEVELOPING A HYBRID DATA MINING APPROACH BASED ON MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION FOR SOLVING A TRAVELING SALESMAN PROBLEM." Journal of Business Economics and Management 13, no. 5 (2012): 951–67. http://dx.doi.org/10.3846/16111699.2011.643445.

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A traveling salesman problem (TSP) is an NP-hard optimization problem. So it is necessary to use intelligent and heuristic methods to solve such a hard problem in a less computational time. This paper proposes a novel hybrid approach, which is a data mining (DM) based on multi-objective particle swarm optimization (MOPSO), called intelligent MOPSO (IMOPSO). The first step of the proposed IMOPSO is to find efficient solutions by applying the MOPSO approach. Then, the GRI (Generalized Rule Induction) algorithm, which is a powerful association rule mining, is used for extracting rules from effici

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Li, Bo, Chao Xiong, Yong Shun Zhang, and Jian Xun Gao. "Multi-Objective Optimization Design of CFRP Winding Mortar Barrel with Metal Liner Based on MOPSO." Key Engineering Materials 753 (August 2017): 109–13. http://dx.doi.org/10.4028/www.scientific.net/kem.753.109.

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A model of composite mortar barrel made of carbon fibre reinforced plastic (CFRP) jacket and steel liner was developed based on the finite method. The multi-objective particle swarm optimization (MOPSO) was employed for the multi-objective design of composite mortar barrel during design processing. The winding angle of carbon fibre and the thickness of composite layers are defined as optimization variables. The fundamental frequency and structure weight of barrel are defined as optimization objectives. The Pareto solution set of composite mortar barrel is obtained by MOPSO. The corresponding d

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Fu, Yanming, Xiao Liu, Weigeng Han, Shenglin Lu, Jiayuan Chen, and Tianbing Tang. "Overlapping Coalition Formation Game via Multi-Objective Optimization for Crowdsensing Task Allocation." Electronics 12, no. 16 (2023): 3454. http://dx.doi.org/10.3390/electronics12163454.

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With the rapid development of sensor technology and mobile services, the service model of mobile crowd sensing (MCS) has emerged. In this model, user groups perceive data through carried mobile terminal devices, thereby completing large-scale and distributed tasks. Task allocation is an important link in MCS, but the interests of task publishers, users, and platforms often conflict. Therefore, to improve the performance of MCS task allocation, this study proposes a repeated overlapping coalition formation game MCS task allocation scheme based on multiple-objective particle swarm optimization (

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Wei, Qing Guo, Yan Mei Wang, and Zong Wu Lu. "Cultural-Based Multi-Objective Particle Swarm Optimization for EEG Channel Reduction in Multi-Class Brain-Computer Interfaces." Applied Mechanics and Materials 239-240 (December 2012): 1027–32. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.1027.

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Applying many electrodes is undesirable for real-life brain-computer interface (BCI) application since the recording preparation can be troublesome and time-consuming. Multi-objective particle swarm optimization (MOPSO) has been widely utilized to solve multi-objective optimization problems and thus can be employed for channel selection. This paper presented a novel method named cultural-based MOPSO (CMOPSO) for channel selection in motor imagery based BCI. The CMOPSO method introduces a cultural framework to adapt the personalized flight parameters of the mutated particles. A comparison betwe

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Liu, Qi, Jiahao Liu, and Dunhu Liu. "Intelligent Multi-Objective Public Charging Station Location with Sustainable Objectives." Sustainability 10, no. 10 (2018): 3760. http://dx.doi.org/10.3390/su10103760.

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This paper investigates a multi-objective charging station location model with the consideration of the triple bottom line principle for green and sustainable development from economic, environmental and social perspectives. An intelligent multi-objective optimization approach is developed to handle this problem by integrating an improved multi-objective particle swarm optimization (MOPSO) process and an entropy weight method-based evaluation process. The MOPSO process is utilized to obtain a set of Pareto optimal solutions, and the entropy weight method-based evaluation process is utilized to

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Srivastav, Achin, and Sunil Agrawal. "Multi-objective optimization of a mixture inventory system using a MOPSO–TOPSIS hybrid approach." Transactions of the Institute of Measurement and Control 39, no. 4 (2015): 555–66. http://dx.doi.org/10.1177/0142331215611211.

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This paper studies a multi-objective mixture inventory problem for a pharmaceutical distributor. The work starts with a discussion of a mixture inventory model and three objectives, namely the minimization of: 1) ordering and holding costs, 2) number of units that stockout and 3) frequency of stockout occasions. Multi-objective particle swarm optimization (MOPSO) is used to determine the non-dominated solutions and generate Pareto curves for the inventory system. Two variants of MOPSO are proposed, based on the selection of inertia weight. The performance of the proposed MOPSO algorithms is ev

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Fallah-Mehdipour, E., O. Bozorg Haddad, and M. A. Mariño. "MOPSO algorithm and its application in multipurpose multireservoir operations." Journal of Hydroinformatics 13, no. 4 (2010): 794–811. http://dx.doi.org/10.2166/hydro.2010.105.

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The main reason for applying evolutionary algorithms in multi-objective optimization problems is to obtain near-optimal nondominated solutions/Pareto fronts, from which decision-makers can choose a suitable solution. The efficiency of multi-objective optimization algorithms depends on the quality and quantity of Pareto fronts produced by them. To compare different Pareto fronts resulting from different algorithms, criteria are considered and applied in multi-objective problems. Each criterion denotes a characteristic of the Pareto front. Thus, ranking approaches are commonly used to evaluate d

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Nguyen, S., and V. Kachitvichyanukul. "Movement Strategies for Multi-Objective Particle Swarm Optimization." International Journal of Applied Metaheuristic Computing 1, no. 3 (2010): 59–79. http://dx.doi.org/10.4018/jamc.2010070105.

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Particle Swarm Optimization (PSO) is one of the most effective metaheuristics algorithms, with many successful real-world applications. The reason for the success of PSO is the movement behavior, which allows the swarm to effectively explore the search space. Unfortunately, the original PSO algorithm is only suitable for single objective optimization problems. In this paper, three movement strategies are discussed for multi-objective PSO (MOPSO) and popular test problems are used to confirm their effectiveness. In addition, these algorithms are also applied to solve the engineering design and

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Salmasnia, Ali, Saeed Hasannejad, and Hadi Mokhtari. "A multi-objective optimization for brush monofilament tufting process design." Journal of Computational Design and Engineering 5, no. 1 (2017): 120–36. http://dx.doi.org/10.1016/j.jcde.2017.08.001.

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Abstract This paper addresses the optimization of monofilament tufting process as the most important and the main stage of toothbrush production in sanitary industries. In order to minimize both process time and depreciation costs, and ultimately increase the production efficiency in such an industrial unit, we propose a metaheuristic based optimization approach to solve it. The Traveling Salesman Problem (TSP) is used to formulate the proposed problem. Then by using multi-objective evolutionary algorithms, NSGA-II and MOPSO, we seek to obtain the best solution and objective functions describe

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Xie, Zhengwei, Yilun Li, and Shiyou Yang. "A Hybrid Multi-Objective Optimization Method and Its Application to Electromagnetic Device Designs." Applied Sciences 12, no. 23 (2022): 12110. http://dx.doi.org/10.3390/app122312110.

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Optimization algorithms play a critical role in electromagnetic device designs due to the ever-increasing technological and economical competition. Although evolutionary algorithm-based methods have successfully been applied to different design problems, these methods exhibit deficiencies when solving complex problems with multimodal and discontinuous objective functions, which is quite common in electromagnetic device optimization designs. In this paper, a hybrid multi-objective optimization algorithm based on a non-dominated sorting genetic algorithm (NSGA-II) and a multi-objective particle

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Abbas, Nizar Hadi, and Jaafer Ahmed Abdulsaheb. "An Adaptive Multi-Objective Particle Swarm Optimization Algorithm for Multi-Robot Path Planning." Journal of Engineering 22, no. 7 (2016): 164–81. http://dx.doi.org/10.31026/j.eng.2016.07.10.

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This paper discusses an optimal path planning algorithm based on an Adaptive Multi-Objective Particle Swarm Optimization Algorithm (AMOPSO) for two case studies. First case, single robot wants to reach a goal in the static environment that contain two obstacles and two danger source. The second one, is improving the ability for five robots to reach the shortest way. The proposed algorithm solves the optimization problems for the first case by finding the minimum distance from initial to goal position and also ensuring that the generated path has a maximum distance from the danger zones. And fo

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Patel, G. C. M., P. Krishna, P. R. Vundavilli, and M. B. Parappagoudar. "Multi-Objective Optimization of Squeeze Casting Process using Genetic Algorithm and Particle Swarm Optimization." Archives of Foundry Engineering 16, no. 3 (2016): 172–86. http://dx.doi.org/10.1515/afe-2016-0073.

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Abstract The near net shaped manufacturing ability of squeeze casting process requiresto set the process variable combinations at their optimal levels to obtain both aesthetic appearance and internal soundness of the cast parts. The aesthetic and internal soundness of cast parts deal with surface roughness and tensile strength those can readily put the part in service without the requirement of costly secondary manufacturing processes (like polishing, shot blasting, plating, hear treatment etc.). It is difficult to determine the levels of the process variable (that is, pressure duration, squee

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Kumar, Vijendra, and S. M. Yadav. "Multi-objective reservoir operation of the Ukai reservoir system using an improved Jaya algorithm." Water Supply 22, no. 2 (2021): 2287–310. http://dx.doi.org/10.2166/ws.2021.374.

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Abstract This paper introduces an effective and reliable approach based on a multi-population approach, namely the self-adaptive multi-population Jaya algorithm (SAMP-JA), to extract multi-purpose reservoir operation policies. The current research focused on two goals: minimizing irrigation deficits and maximizing hydropower generation. Three different models were formulated. The results were compared with those for an ordinary Jaya algorithm (JA), particle swarm optimization (PSO), and an invasive weed optimization (IWO) algorithm. In Model-1, the minimum irrigation deficit obtained by SAMP-J

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Fatima, Aisha, Nadeem Javaid, Ayesha Anjum Butt, et al. "An Enhanced Multi-Objective Gray Wolf Optimization for Virtual Machine Placement in Cloud Data Centers." Electronics 8, no. 2 (2019): 218. http://dx.doi.org/10.3390/electronics8020218.

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Cloud computing offers various services. Numerous cloud data centers are used to provide these services to the users in the whole world. A cloud data center is a house of physical machines (PMs). Millions of virtual machines (VMs) are used to minimize the utilization rate of PMs. There is a chance of unbalanced network due to the rapid growth of Internet services. An intelligent mechanism is required to efficiently balance the network. Multiple techniques are used to solve the aforementioned issues optimally. VM placement is a great challenge for cloud service providers to fulfill the user req

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Ma, Zi Rui. "Particle Swarm Optimization Based on Multiobjective Optimization." Applied Mechanics and Materials 263-266 (December 2012): 2146–49. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2146.

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PSO will population each individual as the search space without a volume and quality of particle. These particles in the search space at a certain speed flight, the speed according to its own flight experience and the entire population of flight experience dynamic adjustment. We describe the standard PSO, multi-objective optimization and MOPSO. The main focus of this thesis is several PSO algorithms which are introduced in detail and studied. MOPSO algorithm introduced adaptive grid mechanism of the external population, not only to groups of particle on variation, but also to the value scope o

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Zhuang, Yucheng, Yikun Huang, and Wenyu Liu. "Integrating Sensor Ontologies with Niching Multi-Objective Particle Swarm Optimization Algorithm." Sensors 23, no. 11 (2023): 5069. http://dx.doi.org/10.3390/s23115069.

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Sensor ontology provides a standardized semantic representation for information sharing between sensor devices. However, due to the varied descriptions of sensor devices at the semantic level by designers in different fields, data exchange between sensor devices is hindered. Sensor ontology matching achieves data integration and sharing between sensors by establishing semantic relationships between sensor devices. Therefore, a niching multi-objective particle swarm optimization algorithm (NMOPSO) is proposed to effectively solve the sensor ontology matching problem. As the sensor ontology meta

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Bakshi, Shalley, Surbhi Sharma, and Rajesh Khanna. "A Novel Metaheuristic Optimization for Throughput Maximization in Energy Harvesting Cognitive Radio Network." Elektronika ir Elektrotechnika 28, no. 3 (2022): 78–89. http://dx.doi.org/10.5755/j02.eie.31245.

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In this article, a novel technique is proposed, namely rank-based multi-objective antlion optimization (RMOALO), and applied to optimize the performance of the energy harvesting cognitive radio network (EHCRN). The original selection method in multi-objective antlion optimizer (MOALO) is suitably changed to improve the algorithm, thus reaching the optimal solution for the problem. The proposed technique shows considerable performance improvement over the method used in the multi-objective antlion optimizer (MOALO). The performance of the proposed RMOALO is demonstrated on five benchmark mathem

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Ling, Qing-Hua, Zhi-Hao Tang, Gan Huang, and Fei Han. "An Improved Multi-Objective Particle Swarm Optimization Algorithm Based on Angle Preference." Symmetry 14, no. 12 (2022): 2619. http://dx.doi.org/10.3390/sym14122619.

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Multi-objective particle swarm optimization (MOPSO) algorithms based on angle preference provide a set of preferred solutions by incorporating a user’s preference. However, since the search mechanism is stochastic and asymmetric, traditional MOPSO based on angle preference are still easy to fall into local optima and lack enough selection pressure on excellent individuals. In this paper, an improved MOPSO algorithm based on angle preference called IAPMOPSO is proposed to alleviate those problems. First, to create a stricter partial order among the non-dominated solutions, reference vectors are

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Gao, Meng, Chenji Wei, Xiangguo Zhao, et al. "Intelligent Optimization of Gas Flooding Based on Multi-Objective Approach for Efficient Reservoir Management." Processes 11, no. 7 (2023): 2226. http://dx.doi.org/10.3390/pr11072226.

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The efficient development of oil reservoirs mainly depends on the comprehensive optimization of the subsurface fluid flow process. As an intelligent analysis technique, artificial intelligence provides a novel solution to multi-objective optimization (MOO) problems. In this study, an intelligent agent model based on the Transformer framework with the assistance of the multi-objective particle swarm optimization (MOPSO) algorithm has been utilized to optimize the gas flooding injection–production parameters in a well pattern in the Middle East. Firstly, 10 types of surveillance data covering 12

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Nagalingam, Umadevi, Balaji Mahadevan, Kamaraj Vijayarajan, and Ananda Padmanaban Loganathan. "Design optimization for cogging torque mitigation in brushless DC motor using multi-objective particle swarm optimization algorithm." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 34, no. 4 (2015): 1302–18. http://dx.doi.org/10.1108/compel-07-2014-0162.

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Purpose – The purpose of this paper is to propose a multi-objective particle swarm optimization (MOPSO) algorithm based design optimization of Brushless DC (BLDC) motor with a view to mitigate cogging torque and enhance the efficiency. Design/methodology/approach – The suitability of MOPSO algorithm is tested on a 120 W BLDC motor considering magnet axial length, stator slot opening and air gap length as the design variables. It avails the use of MagNet 7.5.1, a Finite Element Analysis tool, to account for the geometry and the non-linearity of material for assuaging an improved design framewor

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Bouali, Hamid, Bachir Benhala, and Mohammed Guerbaoui. "Multi-objective optimization of CMOS low noise amplifier through nature-inspired swarm intelligence." Bulletin of Electrical Engineering and Informatics 12, no. 5 (2023): 2824–36. http://dx.doi.org/10.11591/eei.v12i5.5512.

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This paper presents the application of two swarm intelligence techniques, multi-objective artificial bee colony (MOABC) and multi-objective particle swarm optimization (MOPSO), to the optimal design of a complementary metal oxide semiconductor (CMOS) low noise amplifier (LNA) cascode with inductive source degeneration. The aim is to achieve a balanced trade-off between voltage gain and noise figure. The optimized LNA circuit operates at 2.4 GHz with a 1.8 V power supply and is implemented in a 180 nm CMOS process. Both optimization algorithms were implemented in MATLAB and evaluated using the

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Ullah, Ubaid, and Arif Ullah. "An evolutionary algorithm for the solution of multi-objective optimization problem." International Journal of Advances in Applied Sciences 11, no. 4 (2022): 287. http://dx.doi.org/10.11591/ijaas.v11.i4.pp287-295.

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<span>Worldwide, the COVID-19 widespread has significant impact on a great number of people. The hospital admittance issue for patients with COVID-19 has been optimized by previous research. Identifying the symptoms that can be used to determine a patient's health status, whether they are dead or alive it is difficult task for medical professionals. To solve this issue, multi-objective group counselling optimization (MOGCO) algorithm used to control this problem. First, the zitzler-deb-thiele (ZDT)-2 benchmark function is used to evaluate the MOGCO, multi-objective particle swarm optimiz

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Wang, Mingming, Sen Zheng, and Chris Sweetapple. "A Framework for Comparing Multi-Objective Optimization Approaches for a Stormwater Drainage Pumping System to Reduce Energy Consumption and Maintenance Costs." Water 14, no. 8 (2022): 1248. http://dx.doi.org/10.3390/w14081248.

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Reducing energy consumption and maintenance costs of a pumping system is seen as an important but difficult multi-objective optimization problem. Many evolutionary algorithms, such as particle swarm optimization (PSO), multi-objective particle swarm optimization (MOPSO), and non-dominated sorting genetic algorithm II (NSGA-II) have been used. However, a lack of comparison between these approaches poses a challenge to the selection of optimization approach for stormwater drainage pumping stations. In this paper, a new framework for comparing multi-objective approaches is proposed. Two kinds of

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Liu, Wei Lin, and Li Na Liu. "Multi-Reservoir Ecological Operation Using Multi-Objective Particle Swarm Optimization." Applied Mechanics and Materials 641-642 (September 2014): 65–69. http://dx.doi.org/10.4028/www.scientific.net/amm.641-642.65.

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Traditional reservoir operation ignores ecological demands of rivers. This would probably lead to degradation of river ecosystem. In order to alleviate the influence of reservoirs on river ecosystem, multi-objective reservoir ecological operation was proposed from perspective of maintaining the river ecosystem health. Multi-objective mathematical model of multi-reservoir ecological operation was established. A multi-objective particle swarm optimization (MOPSO) algorithm was introduced to generate a set of Pareto-optimal solutions. In addition, to facilitate easy implementation for the reservo

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Amiryousefi, Mohammad Reza, Mohebat Mohebbi, Faramarz Khodaiyan, and Mostafa Ghazizadeh Ahsaee. "Multi-Objective Optimization of Deep-Fat Frying of Ostrich Meat Plates Using Multi-Objective Particle Swarm Optimization (MOPSO)." Journal of Food Processing and Preservation 38, no. 4 (2013): 1472–79. http://dx.doi.org/10.1111/jfpp.12106.

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Goudos, Sotirios K., Zaharias D. Zaharis, and Konstantinos B. Baltzis. "Particle Swarm Optimization as Applied to Electromagnetic Design Problems." International Journal of Swarm Intelligence Research 9, no. 2 (2018): 47–82. http://dx.doi.org/10.4018/ijsir.2018040104.

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Particle swarm optimization (PSO) is a swarm intelligence algorithm inspired by the social behavior of birds flocking and fish schooling. Numerous PSO variants have been proposed in the literature for addressing different problem types. In this article, the authors apply different PSO variants to common design problems in electromagnetics. They apply the Inertia Weight PSO (IWPSO), the Constriction Factor PSO (CFPSO), and the Comprehensive Learning Particle Swarm Optimization (CLPSO) algorithms to real-valued optimization problems, i.e. microwave absorber design, and linear array synthesis. Mo

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