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Статті в журналах з теми "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 (February 2020): 168781402090425. http://dx.doi.org/10.1177/1687814020904253.
Повний текст джерелаАнотація:
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 (December 2016): 1650020. http://dx.doi.org/10.1142/s1469026816500206.
Повний текст джерелаАнотація:
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 (February 27, 2021): 77. http://dx.doi.org/10.3390/a14030077.
Повний текст джерелаАнотація:
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 (February 7, 2020): 2085–110. http://dx.doi.org/10.1108/ec-05-2019-0194.
Повний текст джерелаАнотація:
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 (June 9, 2022): 1834. http://dx.doi.org/10.3390/electronics11121834.
Повний текст джерелаАнотація:
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 (March 24, 2021): 115–25. http://dx.doi.org/10.3233/idt-200049.
Повний текст джерелаАнотація:
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 (July 18, 2018): 788. http://dx.doi.org/10.3390/polym10070788.
Повний текст джерелаАнотація:
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 (November 25, 2021): 204–31. http://dx.doi.org/10.11113/aej.v11.18021.
Повний текст джерелаАнотація:
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 (January 7, 2019): 304–24. http://dx.doi.org/10.1108/compel-05-2018-0208.
Повний текст джерелаАнотація:
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 (July 2021): 96–124. http://dx.doi.org/10.4018/ijsir.2021070105.
Повний текст джерелаАнотація:
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.
Дисертації з теми "MULTI-OBJECTIVE OPTIMIZATION (MOPSO)"
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SETHI, RAJNI. "SOME STUDIES ON UNDERWATER IMAGE ENHANCEMENT." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18451.
Повний текст джерелаАнотація:
Oceans covers 70% of the earth and are required to be explored for various scientific
studies like setting up underwater pipelines, knowing the ecological balance by taking population
census of various aquatic species etc. To get such information, capturing images is
safe and effective solution, but underwater imagery suffers from poor contrast, bluish green
color cast, hazy appearance, blurring and noise due to inherent property of water.
Enhancement of underwater images is a challenging task as images are captured in different
media having varying salinity, turbidity, green matter etc. and captured at varying depth.
Thus, there is a need of an adaptive method which works for all type of underwater images.
This thesis provides an effective solution to pre-process the underwater images to make
them suitable for scientific studies. Under the umbrella of pre-processing, sub-problems
like color correction, contrast correction , haze removal have been addressed irrespective
of the underwater media and depth at which the images have been captured. The contributions
presented in this thesis are outlined below:
• We propose an adaptive method for enhancement of underwater images by using
fuzzy rules to find the color cast. Since the color correction depends upon the media,
some parameters required for controlling the degree of color correction are found
using Multi-Objective optimization (MOPSO). Performance measures which are optimized
using MOPSO act as proper guiding mechanisms so that the resultant images
have few or no artifacts. Various non-reference based performance measures like entropy,
histogram spread, UICM have been used to analyze the results.
• We propose a local enhancement solution which tackles the artifacts caused by artificial
lighting. It performs color and contrast correction in a localized manner. The two
versions are then fused using wavelet based fusion with the help of effective weight
maps which chooses the required features from the two versions.
ii
• We proposed a complete solution which addresses the three major issues of underwater
images i.e. color cast, poor contrast, haze. Using basic image enhancement and
restoration based methods, the proposed method handles the mentioned issues for the
images captured at different depths and in media with varying salinity, turbidity.
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Zhang, Jiahui. "Bi-Objective Dispatch of Multi-Energy Virtual Power Plant: Deep-Learning based Prediction and Particle Swarm Optimization." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.
Знайти повний текст джерелаАнотація:
This paper addresses the coordinative operation problem of multi-energy virtual power plant (ME-VPP) in the context of energy internet. A bi-objective dispatch model is established to optimize the performance of ME-VPP on both economic cost(EC) and power quality (PQ).Various realistic factors are considered, which include environmental governance, transmission ratings, output limits, etc. Long short-term memory (LSTM), a deep learning method, is applied to the promotion of the accuracy of wind prediction. An improved multi-objective particle swarm optimization (MOPSO) is utilized as the solving algorithm. A practical case study is performed on Hongfeng Eco-town in Southwestern China. Simulation results of three scenarios verify the advantages of bi-objective optimization over solely saving EC and enhancing PQ. The Pareto frontier also provides a visible and flexible way for decision-making of ME-VPP operator. Two strategies, “improvisational” and “foresighted”, are compared by testing on IEEE 118-bus benchmark system. It is revealed that “foresighted” strategy, which incorporates LSTM prediction and bi-objective optimization over 5-hr receding horizon, takes 10 Pareto dominances in 24 hours.
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Moravec, Prokop. "Shape Optimization of the Hydraulic Machine Flow Passages." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-433572.
Повний текст джерела
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Marek, Martin. "Více-kriteriální optimalizace EM struktur s proměnným počtem dimenzí." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-438585.
Повний текст джерелаАнотація:
Tato dizertační práce pojednává o více-kriteriálních optimalizačních algoritmech s proměnným počtem dimenzí. Takový algoritmus umožňuje řešit optimalizační úlohy, které jsou jinak řešitelné jen s použitím nepřirozených zjednodušení. Výzkum optimalizačních method s proměnnou dimenzí si vyžádal vytvoření nového optimalizačního frameworku, který obsahuje vedle zmíněných vícekriteriálních metod s proměnnou dimenzí – VND-GDE3 a VND-MOPSO – i další optimalizační metody různých tříd. Optimalizační framework obsahuje také knihovnu rozličných testovacích problémů. Mezi nimi je také sada více-kriteriálních testovacích problémů s proměnnou dimenzí, které byly navrženy pro nastavení a ověření nových metod s proměnnou dimenzí. Nové metody jsou dále použity k optimalizaci několika různorodých optimalizačních úloh z reálného světa.
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Marek, Martin. "Toolbox pro vícekriteriální optimalizační problémy." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-240999.
Повний текст джерелаАнотація:
This paper deals with multi-objective optimization problems (MOOP). It is explained, what solutions in multi-objetive search space are optimal and how are optimal (non-dominated) solutions found in the set of feasible solutions. Afterwards, principles of NSGA-II, MOPSO and GDE3 algorithms are described. In the following chapters, benchmark metrics and problems are introduced. In the last part of this paper, all the three algorithms are compared based on several benchmark metrics.
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Reddy, Manne Janga. "Swarm Intelligence And Evolutionary Computation For Single And Multiobjective Optimization In Water Resource Systems." Thesis, 2006. https://etd.iisc.ac.in/handle/2005/370.
Повний текст джерелаАнотація:
Most of the real world problems in water resources involve nonlinear formulations in
their solution construction. Obtaining optimal solutions for large scale nonlinear
optimization problems is always a challenging task. The conventional methods, such as linear programming (LP), dynamic programming (DP) and nonlinear programming
(NLP) may often face problems in solving them. Recently, there has been an increasing
interest in biologically motivated adaptive systems for solving real world optimization
problems. The multi-member, stochastic approach followed in Evolutionary Algorithms
(EA) makes them less susceptible to getting trapped at local optimal solutions, and they
can search easier for global optimal solutions.
In this thesis, efficient optimization techniques based on swarm intelligence and
evolutionary computation principles have been proposed for single and multi-objective
optimization in water resource systems. To overcome the inherent limitations of
conventional optimization techniques, meta-heuristic techniques like ant colony
optimization (ACO), particle swarm optimization (PSO) and differential evolution (DE) approaches are developed for single and multi-objective optimization. These methods are then applied to few case studies in planning and operation of reservoir systems in India.
First a methodology based on ant colony optimization (ACO) principles is investigated for reservoir operation. The utility of the ACO technique for obtaining
optimal solutions is explored for large scale nonlinear optimization problems, by solving a reservoir operation problem for monthly operation over a long-time horizon of 36 years. It is found that this methodology relaxes the over-year storage constraints and provides efficient operating policy that can be implemented over a long period of time. By using ACO technique for reservoir operation problems, some of the limitations of traditional nonlinear optimization methods are surmounted and thus the performance of the reservoir system is improved.
To achieve faster optimization in water resource systems, a novel technique based
on swarm intelligence, namely particle swarm optimization (PSO) has been proposed. In
general, PSO has distinctly faster convergence towards global optimal solutions for numerical optimization. However, it is found that the technique has the problem of
getting trapped to local optima while solving real world complex problems. To overcome such drawbacks, the standard particle swarm optimization technique has been further improved by incorporating a novel elitist-mutation (EM) mechanism into the algorithm. This strategy provides proper exploration and exploitation throughout the iterations. The improvement is demonstrated by applying it to a multi-purpose single reservoir problem and also to a multi reservoir system. The results showed robust performance of the EM-PSO approach in yielding global optimal solutions.
Most of the practical problems in water resources are not only nonlinear in their
formulations but are also multi-objective in nature. For multi-objective optimization,
generating feasible efficient Pareto-optimal solutions is always a complicated task. In the past, many attempts with various conventional approaches were made to solve water resources problems and some of them are reported as successful. However, in using the conventional linear programming (LP) and nonlinear programming (NLP) methods, they usually involve essential approximations, especially while dealing withdiscontinuous, non-differentiable, non-convex and multi-objective functions. Most of these methods consider multiple objective functions using weighted approach or constrained approach without considering all the objectives simultaneously. Also, the conventional approaches use a point-by-point search approach, in which the outcome of these methods is a single optimal solution. So they may require a large number of simulation runs to arrive at a good Pareto optimal front. One of the major goals in multi-objective optimization is to find a set of well distributed optimal solutions along the true Pareto optimal front. The
classical optimization methods often fail to attain a good and true Pareto optimal front
due to accretion of the above problems. To overcome such drawbacks of the classical
methods, there has recently been an increasing interest in evolutionary computation methods for solving real world multi-objective problems. In this thesis, some novel approaches for multi-objective optimization are developed based on swarm intelligence and evolutionary computation principles.
By incorporating Pareto optimality principles into particle swarm optimization
algorithm, a novel approach for multi-objective optimization has been developed. To
obtain efficient Pareto-frontiers, along with proper selection scheme and diversity
preserving mechanisms, an efficient elitist mutation strategy is proposed. The developed
elitist-mutated multi-objective particle swarm optimization (EM-MOPSO) technique is
tested for various numerical test problems and engineering design problems. It is found
that the EM-MOPSO algorithm resulting in improved performance over a state-of-the-art
multi-objective evolutionary algorithm (MOEA). The utility of EM-MOPSO technique
for water resources optimization is demonstrated through application to a case study, to obtain optimal trade-off solutions to a reservoir operation problem. Through multi-objective analysis for reservoir operation policies, it is found that the technique can offer wide range of efficient alternatives along with flexibility to the decision maker.
In general, most of the water resources optimization problems involve interdependence relations among the various decision variables. By using differential
evolution (DE) scheme, which has a proven ability of effective handling of this kind of
interdependence relationships, an efficient multi-objective solver, namely multi-objective differential evolution (MODE) is proposed. The single objective differential evolution algorithm is extended to multi-objective optimization by integrating various operators like, Pareto-optimality, non-dominated sorting, an efficient selection strategy, crowding distance operator for maintaining diversity, an external elite archive for storing non-
dominated solutions and an effective constraint handling scheme. First, different
variations of DE approaches for multi-objective optimization are evaluated through
several benchmark test problems for numerical optimization. The developed MODE
algorithm showed improved performance over a standard MOEA, namely non-dominated
sorting genetic algorithm–II (NSGA-II). Then MODE is applied to a case study of Hirakud reservoir operation problem to derive operational tradeoffs in the reservoir
system optimization. It is found that MODE is achieving robust performance in
evaluation for the water resources problem, and that the interdependence relationships
among the decision variables can be effectively modeled using differential evolution operators.
For optimal utilization of scarce water resources, an integrated operational model
is developed for reservoir operation for irrigation of multiple crops. The model integrates the dynamics associated with the water released from a reservoir to the actual water utilized by the crops at farm level. It also takes into account the non-linear relationship of root growth, soil heterogeneity, soil moisture dynamics for multiple crops and yield response to water deficit at various growth stages of the crops. Two types of objective functions are evaluated for the model by applying to a case study of Malaprabha reservoir project. It is found that both the cropping area and economic benefits from the crops need to be accounted for in the objective function. In this connection, a multi-objective frame
work is developed and solved using the MODE algorithm to derive simultaneous policies
for irrigation cropping pattern and reservoir operation. It is found that the proposed frame work can provide effective and flexible policies for decision maker aiming at maximization of overall benefits from the irrigation system.
For efficient management of water resources projects, there is always a great
necessity to accurately forecast the hydrologic variables. To handle uncertain behavior of hydrologic variables, soft computing based artificial neural networks (ANNs) and fuzzy inference system (FIS) models are proposed for reservoir inflow forecasting. The forecast models are developed using large scale climate inputs like indices of El-Nino Southern Oscialltion (ENSO), past information on rainfall in the catchment area and inflows into the reservoir. In this purpose, back propagation neural network (BPNN), hybrid particle
swarm optimization trained neural network (PSONN) and adaptive network fuzzy
inference system (ANFIS) models have been developed. The developed models are
applied for forecasting inflows into the Malaprabha reservoir. The performances of these models are evaluated using standard performance measures and it is found that the hybrid PSONN model is performing better than BPNN and ANFIS models. Finally by adopting PSONN model for inflow forecasting and EMPSO technique for solving the reservoir
operation model, the practical utility of the different models developed in the thesis are demonstrated through application to a real time reservoir operation problem. The
developed methodologies can certainly help in better planning and operation of the scarce water resources.
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Reddy, Manne Janga. "Swarm Intelligence And Evolutionary Computation For Single And Multiobjective Optimization In Water Resource Systems." Thesis, 2006. http://hdl.handle.net/2005/370.
Повний текст джерелаАнотація:
Most of the real world problems in water resources involve nonlinear formulations in
their solution construction. Obtaining optimal solutions for large scale nonlinear
optimization problems is always a challenging task. The conventional methods, such as linear programming (LP), dynamic programming (DP) and nonlinear programming
(NLP) may often face problems in solving them. Recently, there has been an increasing
interest in biologically motivated adaptive systems for solving real world optimization
problems. The multi-member, stochastic approach followed in Evolutionary Algorithms
(EA) makes them less susceptible to getting trapped at local optimal solutions, and they
can search easier for global optimal solutions.
In this thesis, efficient optimization techniques based on swarm intelligence and
evolutionary computation principles have been proposed for single and multi-objective
optimization in water resource systems. To overcome the inherent limitations of
conventional optimization techniques, meta-heuristic techniques like ant colony
optimization (ACO), particle swarm optimization (PSO) and differential evolution (DE) approaches are developed for single and multi-objective optimization. These methods are then applied to few case studies in planning and operation of reservoir systems in India.
First a methodology based on ant colony optimization (ACO) principles is investigated for reservoir operation. The utility of the ACO technique for obtaining
optimal solutions is explored for large scale nonlinear optimization problems, by solving a reservoir operation problem for monthly operation over a long-time horizon of 36 years. It is found that this methodology relaxes the over-year storage constraints and provides efficient operating policy that can be implemented over a long period of time. By using ACO technique for reservoir operation problems, some of the limitations of traditional nonlinear optimization methods are surmounted and thus the performance of the reservoir system is improved.
To achieve faster optimization in water resource systems, a novel technique based
on swarm intelligence, namely particle swarm optimization (PSO) has been proposed. In
general, PSO has distinctly faster convergence towards global optimal solutions for numerical optimization. However, it is found that the technique has the problem of
getting trapped to local optima while solving real world complex problems. To overcome such drawbacks, the standard particle swarm optimization technique has been further improved by incorporating a novel elitist-mutation (EM) mechanism into the algorithm. This strategy provides proper exploration and exploitation throughout the iterations. The improvement is demonstrated by applying it to a multi-purpose single reservoir problem and also to a multi reservoir system. The results showed robust performance of the EM-PSO approach in yielding global optimal solutions.
Most of the practical problems in water resources are not only nonlinear in their
formulations but are also multi-objective in nature. For multi-objective optimization,
generating feasible efficient Pareto-optimal solutions is always a complicated task. In the past, many attempts with various conventional approaches were made to solve water resources problems and some of them are reported as successful. However, in using the conventional linear programming (LP) and nonlinear programming (NLP) methods, they usually involve essential approximations, especially while dealing withdiscontinuous, non-differentiable, non-convex and multi-objective functions. Most of these methods consider multiple objective functions using weighted approach or constrained approach without considering all the objectives simultaneously. Also, the conventional approaches use a point-by-point search approach, in which the outcome of these methods is a single optimal solution. So they may require a large number of simulation runs to arrive at a good Pareto optimal front. One of the major goals in multi-objective optimization is to find a set of well distributed optimal solutions along the true Pareto optimal front. The
classical optimization methods often fail to attain a good and true Pareto optimal front
due to accretion of the above problems. To overcome such drawbacks of the classical
methods, there has recently been an increasing interest in evolutionary computation methods for solving real world multi-objective problems. In this thesis, some novel approaches for multi-objective optimization are developed based on swarm intelligence and evolutionary computation principles.
By incorporating Pareto optimality principles into particle swarm optimization
algorithm, a novel approach for multi-objective optimization has been developed. To
obtain efficient Pareto-frontiers, along with proper selection scheme and diversity
preserving mechanisms, an efficient elitist mutation strategy is proposed. The developed
elitist-mutated multi-objective particle swarm optimization (EM-MOPSO) technique is
tested for various numerical test problems and engineering design problems. It is found
that the EM-MOPSO algorithm resulting in improved performance over a state-of-the-art
multi-objective evolutionary algorithm (MOEA). The utility of EM-MOPSO technique
for water resources optimization is demonstrated through application to a case study, to obtain optimal trade-off solutions to a reservoir operation problem. Through multi-objective analysis for reservoir operation policies, it is found that the technique can offer wide range of efficient alternatives along with flexibility to the decision maker.
In general, most of the water resources optimization problems involve interdependence relations among the various decision variables. By using differential
evolution (DE) scheme, which has a proven ability of effective handling of this kind of
interdependence relationships, an efficient multi-objective solver, namely multi-objective differential evolution (MODE) is proposed. The single objective differential evolution algorithm is extended to multi-objective optimization by integrating various operators like, Pareto-optimality, non-dominated sorting, an efficient selection strategy, crowding distance operator for maintaining diversity, an external elite archive for storing non-
dominated solutions and an effective constraint handling scheme. First, different
variations of DE approaches for multi-objective optimization are evaluated through
several benchmark test problems for numerical optimization. The developed MODE
algorithm showed improved performance over a standard MOEA, namely non-dominated
sorting genetic algorithm–II (NSGA-II). Then MODE is applied to a case study of Hirakud reservoir operation problem to derive operational tradeoffs in the reservoir
system optimization. It is found that MODE is achieving robust performance in
evaluation for the water resources problem, and that the interdependence relationships
among the decision variables can be effectively modeled using differential evolution operators.
For optimal utilization of scarce water resources, an integrated operational model
is developed for reservoir operation for irrigation of multiple crops. The model integrates the dynamics associated with the water released from a reservoir to the actual water utilized by the crops at farm level. It also takes into account the non-linear relationship of root growth, soil heterogeneity, soil moisture dynamics for multiple crops and yield response to water deficit at various growth stages of the crops. Two types of objective functions are evaluated for the model by applying to a case study of Malaprabha reservoir project. It is found that both the cropping area and economic benefits from the crops need to be accounted for in the objective function. In this connection, a multi-objective frame
work is developed and solved using the MODE algorithm to derive simultaneous policies
for irrigation cropping pattern and reservoir operation. It is found that the proposed frame work can provide effective and flexible policies for decision maker aiming at maximization of overall benefits from the irrigation system.
For efficient management of water resources projects, there is always a great
necessity to accurately forecast the hydrologic variables. To handle uncertain behavior of hydrologic variables, soft computing based artificial neural networks (ANNs) and fuzzy inference system (FIS) models are proposed for reservoir inflow forecasting. The forecast models are developed using large scale climate inputs like indices of El-Nino Southern Oscialltion (ENSO), past information on rainfall in the catchment area and inflows into the reservoir. In this purpose, back propagation neural network (BPNN), hybrid particle
swarm optimization trained neural network (PSONN) and adaptive network fuzzy
inference system (ANFIS) models have been developed. The developed models are
applied for forecasting inflows into the Malaprabha reservoir. The performances of these models are evaluated using standard performance measures and it is found that the hybrid PSONN model is performing better than BPNN and ANFIS models. Finally by adopting PSONN model for inflow forecasting and EMPSO technique for solving the reservoir
operation model, the practical utility of the different models developed in the thesis are demonstrated through application to a real time reservoir operation problem. The
developed methodologies can certainly help in better planning and operation of the scarce water resources.
Книги з теми "MULTI-OBJECTIVE OPTIMIZATION (MOPSO)"
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López, Javier. Optimización multi-objetivo. Editorial de la Universidad Nacional de La Plata (EDULP), 2015. http://dx.doi.org/10.35537/10915/45214.
Повний текст джерелаАнотація:
Cuando hablamos de optimización en el ámbito de las ciencias de la computación hacemos referencia al mismo concepto coloquial asociado a esa palabra, la concreción de un objetivo utilizando la menor cantidad de recursos disponibles, o en una visión similar, la obtención del mejor objetivo posible utilizando todos los recursos con lo que se cuenta. Los métodos para encontrar la mejor solución (óptima) varían de acuerdo a la complejidad del problema enfrentado. Para problemas triviales, el cerebro humano posee la capacidad de resolverlos (encontrar la mejor solución) directamente, pero a medida que aumenta la complejidad del problema, se hace necesario contar con herramientas adicionales. En esta dirección, existe una amplia variedad de técnicas para resolver problemas complejos. Dentro de estas técnicas, podemos mencionar las técnicas exactas. Este tipo de algoritmos son capaces de encontrar las soluciones óptimas a un problema dado en una cantidad finita de tiempo. Como contrapartida, requiere que el problema a resolver cumpla con condiciones bastante restrictivas. Existen además un conjunto muy amplio de técnica aproximadas, conocidas como metaheurísticas. Estas técnicas se caracterizan por integrar de diversas maneras procedimientos de mejora local y estrategias de alto nivel para crear un proceso capaz de escapar de óptimos locales y realizar una búsqueda robusta en el espacio de búsqueda del problema. En su evolución, estos métodos han incorporado diferentes estrategias para evitar la convergencia a óptimos locales, especialmente en espacios de búsqueda complejos. Este tipo de procedimientos tienen como principal característica que son aplicables a cualquier tipo de problemas, sin requerir ninguna condición particular a cumplir por los mismos. Estas técnicas no garantizan en ningún caso la obtención de los valores óptimos de los problemas en cuestión, pero se ha demostrado que son capaces de alcanzar muy buenos valores de soluciones en períodos de tiempo cortos. Además, es posible aplicarlas a problemas de diferentes tipos sin mayores modificaciones, mostrando su robustez y su amplio espectro de uso. La mayoría de estas técnicas están inspiradas en procesos biológicos y/o físicos, y tratan de simular el comportamiento propio de estos procesos que favorecen la búsqueda y detección de soluciones mejores en forma iterativa. La más difundida de estas técnicas son los algoritmos genéticos, basados en el mecanismo de evolución natural de las especies. Existen diferentes tipos de problemas, y multitud de taxonomías para clasificar los mismos. En el alcance de este trabajo nos interesa diferenciar los problemas en cuanto a la cantidad de objetivos a optimizar. Con esta consideración en mente, surge una primera clasificación evidente, los problemas mono-objetivo, donde existe solo una función objetivo a optimizar, y los problemas multi-objetivo donde existe más de una función objetivo. En el presente trabajo se estudia la utilización de metaheurísticas evolutivas para la resolución de problemas complejos, con uno y con más de un objetivo. Se efectúa un análisis del estado de situación en la materia, y se proponen nuevas variantes de algoritmos existentes, validando que las mismas mejoran resultados reportados en la literatura. En una primera instancia, se propone una mejora a la versión canónica y mono-objetivo del algoritmo PSO, luego de un estudio detallado del patrón de movimientos de las partículas en el espacio de soluciones. Estas mejoras se proponen en las versiones de PSO para espacios continuos y para espacios binarios. Asimismo, se analiza la implementación de una versión paralela de esta técnica evolutiva. Como segunda contribución, se plantea una nueva versión de un algoritmo PSO multiobjetivo (MOPSO Multi Objective Particle Swarm Optimization) incorporando la posibilidad de variar dinámicamente el tamaño de la población, lo que constituye una contribución innovadora en problemas con mas de una función objetivo. Por último, se utilizan las técnicas representativas del estado del arte en optimización multi-objetivo aplicando estos métodos a la problemática de una empresa de emergencias médicas y atención de consultas domiciliarias. Se logró poner en marcha un proceso de asignación de móviles a prestaciones médicas basado en metaheurísticas, logrando optimizar el proceso de asignación de móviles médicos a prestaciones médicas en la principal compañía de esta industria a nivel nacional.
Частини книг з теми "MULTI-OBJECTIVE OPTIMIZATION (MOPSO)"
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Devi, Swagatika, Alok Kumar Jagadev, and Sachidananda Dehuri. "Comparison of Various Approaches in Multi-objective Particle Swarm Optimization (MOPSO): Empirical Study." In Multi-objective Swarm Intelligence, 75–103. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46309-3_3.
Повний текст джерела
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Al Moubayed, Noura, Andrei Petrovski, and John McCall. "D 2 MOPSO: Multi-Objective Particle Swarm Optimizer Based on Decomposition and Dominance." In Evolutionary Computation in Combinatorial Optimization, 75–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29124-1_7.
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Di-Ming, Ai, Zhang Zhe, Zhang Rui, and Pan Feng. "Research of Pareto-Based Multi-Objective Optimization for Multi-Vehicle Assignment Problem Based on MOPSO." In Lecture Notes in Computer Science, 10–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21524-7_2.
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Banerjee, Indranil, and Prasun Das. "Evolutionary Multi-Objective Bacterial Swarm Optimization (MOBSO): A Hybrid Approach." In Lecture Notes in Computer Science, 568–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17298-4_62.
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Yang, Kaifeng, and Michael Affenzeller. "Surrogate-assisted Multi-objective Optimization via Genetic Programming Based Symbolic Regression." In Lecture Notes in Computer Science, 176–90. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27250-9_13.
Повний текст джерелаАнотація:
AbstractSurrogate-assisted optimization algorithms are a commonly used technique to solve expensive-evaluation problems, in which a regression model is built to replace an expensive function. In some acquisition functions, the only requirement for a regression model is the predictions. However, some other acquisition functions also require a regression model to estimate the “uncertainty” of the prediction, instead of merely providing predictions. Unfortunately, very few statistical modeling techniques can achieve this, such as Kriging/Gaussian processes, and recently proposed genetic programming-based (GP-based) symbolic regression with Kriging (GP2). Another method is to use a bootstrapping technique in GP-based symbolic regression to estimate prediction and its corresponding uncertainty. This paper proposes to use GP-based symbolic regression and its variants to solve multi-objective optimization problems (MOPs), which are under the framework of a surrogate-assisted multi-objective optimization algorithm (SMOA). Kriging and random forest are also compared with GP-based symbolic regression and GP2. Experiment results demonstrate that the surrogate models using the GP2 strategy can improve SMOA’s performance.
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Goudos, Sotirios K. "Application of Multi-Objective Evolutionary Algorithms to Antenna and Microwave Design Problems." In Multidisciplinary Computational Intelligence Techniques, 75–101. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1830-5.ch006.
Повний текст джерелаАнотація:
Antenna and microwave design problems are, in general, multi-objective. Multi-objective Evolutionary Algorithms (MOEAs) are suitable optimization techniques for solving such problems. Particle Swarm Optimization (PSO) and Differential Evolution (DE) have received increased interest from the electromagnetics community. The fact that both algorithms can efficiently handle arbitrary optimization problems has made them popular for solving antenna and microwave design problems. This chapter presents three different state-of-the-art MOEAs based on PSO and DE, namely: the Multi-objective Particle Swarm Optimization (MOPSO), the Multi-objective Particle Swarm Optimization with fitness sharing (MOPSO-fs), and the Generalized Differential Evolution (GDE3). Their applications to different design cases from antenna and microwave problems are reported. These include microwave absorber, microwave filters and Yagi-uda antenna design. The algorithms are compared and evaluated against other evolutionary multi-objective algorithms like Nondominated Sorting Genetic Algorithm-II (NSGA-II). The results show the advantages of using each algorithm.
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Zeltni, Kamel, Souham Meshoul, and Heyam H. Al-Baity. "On the Convergence and Diversity of Pareto Fronts Using Swarm Intelligence Metaheuristics for Constrained Search Space." In Robotic Systems, 1573–93. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1754-3.ch075.
Повний текст джерелаАнотація:
This article reviews existing constraint-handling techniques then presents a new design for Swarm Intelligence Metaheuristics (SIM) to deal with constrained multi-objective optimization problems (CMOPs). This new design aims to investigate potential effects of leader concepts that characterize the dynamic of SIM in the hope to help the population to reach Pareto optimal solutions in a constrained search space. The new leader-based constraint handling mechanism is incorporated in Constrained Multi-Objective Cuckoo Search (C-MOCS) and Constrained Multi-Objective Particle Swarm Optimization (C-MOPSO) as specific instances of a more general class of SIMs. The experimental results are carried out using a set of six well-known test functions and two performance metrics. The convergence and diversity of C-MOCS and C-MOPSO are analysed and compared to the well-known Multi-Objective Evolutionary Algorithm (MOEA) NSGA-II and discussed based on the obtained results.
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Póvoa, Ricardo, Ricardo Lourenço, Nuno Lourenço, António Canelas, Ricardo Martins, and Nuno Horta. "Synthesis of LC-Oscillators Using Rival Multi-Objective Multi-Constraint Optimization Kernels." In Advances in Computer and Electrical Engineering, 1–27. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-6627-6.ch001.
Повний текст джерелаАнотація:
This chapter presents a state-of-the-art multi-objective/multi-constraint design automation approach applied to the design of an LC-Voltage Controlled Oscillator and an LC-Oscillator for a 130 nm technology node and leading to sets of design solutions showing figures-of-merit around -192 dBc/Hz and -186 dBc/Hz, respectively. The proposed approach, implemented in AIDA-C, guarantees accuracy by using commercial circuit simulators (HSPICE® and ELDO®) to evaluate the performance of the tentative circuit solutions, where the number of time-consuming circuit simulations is efficiently pruned by the optimization kernel. Three multi-objective optimization algorithms, the NSGA-II, the MOPSO, and the MOSA, are experimented with in the synthesis of the quoted oscillators and compared in terms of performance using statistical results obtained from multiple synthesis runs for each one of the oscillators. The performance of the optimized oscillators is then compared to other state-of-the-art results, showing the benefits of the presented multi-objective design approach.
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Abdelsalam, Hisham M., and Amany Magdy. "Optimal Ordering of Activities of New Product Development Projects with Time and Cost Considerations." In Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance, 546–63. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2086-5.ch018.
Повний текст джерелаАнотація:
This chapter presents a Discrete Multi-objective Particle Swarm Optimization (MOPSO) algorithm that determines the optimal order of activities execution within a design project that minimizes project total iterative time and cost. Numerical Design Structure Matrix (DSM) was used to model project activities’ execution order along with their interactions providing a base for calculating the objective functions. Algorithm performance was tested on a hypothetical project data and results showed its ability to reach Pareto fronts on different sets of objective functions.
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Ali, Hamid, Waseem Shahzad, and Farrukh Aslam Khan. "Using Multi-Objective Particle Swarm Optimization for Energy-Efficient Clustering in Wireless Sensor Networks." In Wireless Sensor Networks and Energy Efficiency, 291–304. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0101-7.ch013.
Повний текст джерелаАнотація:
In this chapter, the authors propose a multi-objective solution to the problem by using multi-objective particle swarm optimization (MOPSO) algorithm to optimize the number of clusters in a sensor network in order to provide an energy-efficient solution. The proposed algorithm considers the ideal degree of nodes and battery power consumption of the sensor nodes. The main advantage of the proposed method is that it provides a set of solutions at a time. The results of the proposed approach were compared with two other well-known clustering techniques: WCA and CLPSO-based clustering. Extensive simulations were performed to show that the proposed approach is an effective approach for clustering in WSN environments and performs better than the other two approaches.
Тези доповідей конференцій з теми "MULTI-OBJECTIVE OPTIMIZATION (MOPSO)"
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Wu, Dongmei, and Hao Gao. "An asynchronous MOPSO for multi-objective optimization problem." In 2014 10th France-Japan/ 8th Europe-Asia Congress on Mecatronics (MECATRONICS). IEEE, 2014. http://dx.doi.org/10.1109/mecatronics.2014.7018583.
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McDougall, Robin, and Scott Nokleby. "On the Application of Multi-Objective Parallel Asynchronous Particle Swarm Optimization to Engineering Design Problems." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86973.
Повний текст джерелаАнотація:
A distributed variant of multi-objective particle swarm optimization (MOPSO) called multi-objective parallel asynchronous particle swarm optimization (MOPAPSO) is presented and the effects of distribution of objective function calculations to slave processors on the results and performance are investigated. Two benchmark examples were used to verify the capability of this implementation of MOPAPSO to match previously published results of MOPSO. The computationally intensive task of multi-objective Optimization Based Mechanism Synthesis (OBMS) was used to verify that significant performance improvements were realized through parallelization. The results show that MOPAPSO is able to match the results of MOPSO in significantly less time. The fact that MOPAPSO is distributed results in an effective optimization tool for complex multi-objective design problems.
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Kumar, Anuj, Sangeeta Pant, Mangey Ram Kumar, and Ganga Negi. "Multi-Objective Optimization of Complex Bridge Network by MOPSO-CD." In 2022 International Conference on Advances in Computing, Communication and Materials (ICACCM). IEEE, 2022. http://dx.doi.org/10.1109/icaccm56405.2022.10009336.
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Wang, Shuai, Xiaohui Lei, and Xiaomin Huang. "Multi-objective optimization of reservoir flood dispatch based on MOPSO algorithm." In 2012 8th International Conference on Natural Computation (ICNC). IEEE, 2012. http://dx.doi.org/10.1109/icnc.2012.6234561.
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Al-Ani, Dhafar, and Saeid Habibi. "A New Particle Swarm Optimization and Differential Evolution Technique for Constrained Optimization Problems." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-63877.
Повний текст джерелаАнотація:
Real-world problems are often complex and may need to deal with constrained optimization problems (COPs). This has led to a growing interest in optimization techniques that involve more than one objective function to be simultaneously optimized. Accordingly, at the end of the multi-objective optimization process, there will be more than one solution to be considered. This enables a trade-off of high-quality solutions and provides options to the decision-maker to choose a solution based on qualitative preferences. Particle Swarm Optimization (PSO) algorithms are increasingly being used to solve NP-hard and constrained optimization problems that involve multi-objective mathematical representations by finding accurate and robust solutions. PSOs are currently used in many real-world applications, including (but not limited to) medical diagnosis, image processing, speech recognition, chemical reactor, weather forecasting, system identification, reactive power control, stock exchange market, and economic power generation. In this paper, a new version of Multi-objective PSO and Differential Evolution (MOPSO-DE) is proposed to solve constrained optimization problems (COPs). As presented in this paper, the proposed MOPSO-DE scheme incorporates a new leader(s) updating mechanism that is invoked when the system is under the risk of converging to premature solutions, parallel islands mechanism, adaptive mutation, and then integrated to the DE in order to update the particles’ best position in the search-space. A series of experiments are conducted using 12 well-known benchmark test problems collected from the 2006 IEEE Congress on Evolutionary Computation (CEC2006) to verify the feasibility, performance, and effectiveness of the proposed MOPSO-DE algorithm. The simulation results show the proposed MOPSO-DE is highly competitive and is able to obtain the optimal solutions for the all test problems.
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Zhang, Dan, Zhaochao Liu, Chen Zhou, and Dunwen Zuo. "Multi-objective Layout Optimization of Aircraft Multi-branch Cable Harness Based on MOPSO/D." In 2021 12th International Conference on Mechanical and Aerospace Engineering (ICMAE). IEEE, 2021. http://dx.doi.org/10.1109/icmae52228.2021.9522568.
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Robles-Rodriguez, C. E., C. Bideaux, S. E. Guillouet, N. Gorret, G. Roux, C. Molina-Jouve, and C. A. Aceves-Lara. "Multi-objective particle swarm optimization (MOPSO) of lipid accumulation in Fed-batch cultures." In 2016 24th Mediterranean Conference on Control and Automation (MED). IEEE, 2016. http://dx.doi.org/10.1109/med.2016.7535934.
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Hamid, Radwan H. A., Amr M. A. Amin, Refaat S. Ahmed, and Adel A. A. El-Gammal. "Optimal Operation of Induction Motors Based on Multi-Objective Particle Swarm Optimization (MOPSO)." In IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2007. http://dx.doi.org/10.1109/iecon.2007.4459912.
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Mollazei, Sara, Malihe M. Farsangi, Hossein Nezamabadi-pour, and Kwang Y. Lee. "Multi-objective Optimization of Power System Performance with TCSC Using the MOPSO Algorithm." In 2007 IEEE Power Engineering Society General Meeting. IEEE, 2007. http://dx.doi.org/10.1109/pes.2007.385878.
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Sharaf, Adel M., and Adel A. A. El-Gammal. "Optimal Hybrid Power Filter Compensator Design Using Multi-objective Particle Swarm Optimization (MOPSO)." In 2009 11th International Conference on Computer Modelling and Simulation. IEEE, 2009. http://dx.doi.org/10.1109/uksim.2009.62.
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