This paper presents an approach to analyze the critical drawbacks and attributes of Additive Manufacturing (AM) simultaneously to find the best manufacturing parameters to fabricate the AM products. In this study, Fus...
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This paper presents an approach to analyze the critical drawbacks and attributes of Additive Manufacturing (AM) simultaneously to find the best manufacturing parameters to fabricate the AM products. In this study, Fused Deposition Modeling (FDM) is investigated as a common AM technology. For this purpose, a multi-optimizationproblem is formulated according to the analysis of FDM technology. In this problem, layer thickness and part orientation are determined as the decision variables which are the important parameters of manufacturing. As objective functions, production time and material mass are considered and the surface roughness of FDM products and mechanical behavior of material are defined as the constraint functions. Different methodologies are developed to model the AM criteria according to these decision variables. To find the optimal solutions for manufacturing, Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is used. Finally, a case study highlighted the reliability of the proposed approach. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
The design of digital IIR filter design by using evolutionary algorithms has gained much attention in the previous years. Most of the researchers treated the design problem as a single objectiveoptimizationproblem a...
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The design of digital IIR filter design by using evolutionary algorithms has gained much attention in the previous years. Most of the researchers treated the design problem as a single objectiveoptimizationproblem and applied the techniques for minimizing the magnitude response error. In this paper the design of filter is treated as a multi-objectiveproblem by simultaneously minimizing the magnitude response error, linear phase response error and optimal order along with meeting the stability criterion. A hybrid heuristic search technique having differential evolution (DE) method as a global search technique and binary successive approximation based evolutionary search method as a local search technique has been proposed. Based on mean value of population, new mutation strategies have been proposed. The above proposed hybrid heuristic search technique has been applied effectively to solve the multi-parameter and multi-objective optimization problem of low-pass, high-pass, band-pass and band-stop digital IIR filter design. The obtained results reveal that the proposed technique with new proposed mutation strategies performs better than the already existing mutation strategies of DE and other algorithms applied by other researchers for the design of digital IIR filter.
Supercritical CO2 cycle has become one of the most popular research fields of thermal science. The selection of operation parameters on thermodynamic cycle process is an important task. The computational model of supe...
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Supercritical CO2 cycle has become one of the most popular research fields of thermal science. The selection of operation parameters on thermodynamic cycle process is an important task. The computational model of supercritical CO2 recompression cycle is built to solve the multi-objectiveproblem in this paper. Then, the optimization of parameters is performed based on genetic algorithm. Several Kriging models are also used to reduce the quantity of samples. According to the calculation, the influence of sample quantity on the result and the time cost is obtained The results show that it is required to improve the heat transfer when improvement of the cycle efficiency is desired.
This paper addresses a novel multi-objective fruit fly optimization algorithm (MOFOA) for solving multi-objective optimization problems. The essence of MOFOA lies in its having two characteristic features. For the fir...
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This paper addresses a novel multi-objective fruit fly optimization algorithm (MOFOA) for solving multi-objective optimization problems. The essence of MOFOA lies in its having two characteristic features. For the first feature, a population of random fruit flies initializes the algorithm. During this initialization phase, the dominated fruit fly is replaced by the nearest non-dominated one. Subsequently, the fruit flies undergo evolution by flying randomly around the non-dominated solution or around the reference point, i.e., the best location of the individual objectives. Afterwards, the fruit flies are updated according to the nearest location whether from the reference point or the previous non-dominated location. For the second feature, the weighted sum method is incorporated to update the previous best locations of fruit flies and the reference point to emphasize the convergence of the non-dominated solutions. To prove the capability of the proposed MOFOA, two standard benchmark problems in addition to the real world application, namely, multi-objective shape design of tubular linear synchronous motor (TLSM) are checked. The corresponding TLSM objective functions aims to maximize operating force and to minimize the flux saturation. The outcomes clearly demonstrate the effectiveness of the proposed algorithm for finding the non-dominated solutions.
In this paper a new multi-objective clonal selection algorithm (theta-MCSA) is presented to solve multi-objectiveproblems with multimodal and non-continuous functions. The concept of clonal selection algorithm (CSA) ...
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In this paper a new multi-objective clonal selection algorithm (theta-MCSA) is presented to solve multi-objectiveproblems with multimodal and non-continuous functions. The concept of clonal selection algorithm (CSA) is based on the immune system and white blood cells behavior that select the antibodies similar to antigen for cloning. Although the clonal selection is a robust optimization method, however, as a shortcoming, it takes long time to find optimal Pareto front especially in problems with large search space. To overcome this problem, the proposed method replaces the large search space with the theta-search based on the phase angles. To avoid trapping into local optima in mutation step, two strong mutation methods are implemented according to the iteration number and algorithm efficiency. For converging to uniformly Pareto front in less iterations, the proposed multi-objective algorithm handles the size of the repository and a new population updating mechanism is iteratively applied to select the non-dominate, one-dominate and two-dominate solutions of prior iteration. The experimental results show the efficiency of the proposed theta-MCSA algorithm compared to other methods.
Interval multi-objective optimization problems (IMOPs) are ubiquitous and challenging. There are many optimizers for solving them;however, their drawbacks, such as the high computational cost and big uncertainty of th...
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ISBN:
(纸本)9781538627266
Interval multi-objective optimization problems (IMOPs) are ubiquitous and challenging. There are many optimizers for solving them;however, their drawbacks, such as the high computational cost and big uncertainty of the final front, hinder their applications in real-world situation. This paper proposes a surrogate-assisted interval multi-objective memetic algorithm (SA-IMOMA) that incorporates a surrogate model into the local search. In the framework of interval multi-objective memetic algorithms (IMOMAs), the fitness function of a local search is first defined by both the contribution of an individual to hyper-volume and the imprecision of the individual, and then a support vector machine (SVM) is trained and employed to evaluate local individuals so as to cut down the high computational cost of IMOMAs and further reduce the imprecision of the final front. The proposed algorithm was tested on 10 benchmark IMOPs and an IMOP in solar desalination. The empirical results indicate that SA-IMOMA is more economical than non-surrogate IMOMAs and superior to non-local-search IP-MOEA.
Biologically inspired Autonomous Underwater Vehicles (AUVs) have been developed in the recent decades. Tis thesis focuses on the AUVs that are biologically inspired by snakes, called Underwater Snake Robots (USRs). A ...
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Biologically inspired Autonomous Underwater Vehicles (AUVs) have been developed in the recent decades. Tis thesis focuses on the AUVs that are biologically inspired by snakes, called Underwater Snake Robots (USRs). A well-known issue of the USRs, or any AUVs, is the long- term autonomy. To achieve this, energy efcient approaches are required. Many studies have considered single-objectiveoptimizationproblems regarding the energy efciency of the USR, but almost none with multi-objective optimization problems (MOPs). Tis thesis presents MOPs of diferent locomotions of the USR. Te presented MOPs consider the energy efcient optimiza- tion of maximizing the forward velocity, while minimizing the power consumption of the USR. For computing the efcient motion paterns, two multi-objective Evolutionary Algorithms (MOEAs) called Non-dominated Sort Genetic Algorithm II (NSGA-II), and Hypervolume Estimation Algo- rithm for multi-objectiveoptimization (HypE) are applied. A challenging topic of the USR, is their adaptability of diferent locomotions. Diferent locomotions of the USR give rise to diferent search spaces for optimization. We present simulation studies of the two most common snake locomotions: (i) lateral undulation and (ii) eel-like motion. Furthermore, we also present and in- vestigate three altered motion patern of the USR. Te aim of the altered locomotions is to let the MOEAs generate efcient locomotions through evolutionary, which we do not know the gait of. From the simulation results, it turns out that one of the altered motion patern approximates a motion similar to the lateral undulation. Tis motion patern is generated based on Fourier se- ries. Te obtained simulation results are based on optimization with optimal Genetic Algorithm (GA) parameters, found by numerous presimulations of the MOPs. Since this is multi-objective optimization problems, the end results will be in the form of Pareto fronts. Tese Pareto fronts can be used as trade-ofs for selecting th
Cloud computing is a promising research domain used to enhance the performance of information systems and applications used at our daily life. Workflow scheduling in cloud platform is a complex optimizationproblem be...
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Cloud computing is a promising research domain used to enhance the performance of information systems and applications used at our daily life. Workflow scheduling in cloud platform is a complex optimizationproblem because of its heterogeneous nature, complex billing models and infinite resources requirements which has lead the users to look for an optimal or a suboptimal solution that satisfy multiple competing goals such as minimum makespan and energy efficient solutions. The focus of this paper is to schedule workflow applications, optimizing makespan and energy consumption. A high-performance method for scheduling workflow on cloud computing platform will be presented in this paper, called non-dominated sorting particle swarm optimization (NSPSO). The proposed method is evaluated on synthetic representation of real world scientific workflow applications. The simulation results shows that the proposed method can provide better workflow scheduling solution for cloud when compared with other state-of-the-art methods.
Cloud computing is a promising research domain used to enhance the performance of information systems and applications used at our daily life. Workflow scheduling in cloud platform is a complex optimizationproblem be...
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Cloud computing is a promising research domain used to enhance the performance of information systems and applications used at our daily life. Workflow scheduling in cloud platform is a complex optimizationproblem because of its heterogeneous nature, complex billing models and infinite resources requirements which has lead the users to look for an optimal or a suboptimal solution that satisfy multiple competing goals such as minimum makespan and energy efficient solutions. The focus of this paper is to schedule workflow applications, optimizing makespan and energy consumption. A high-performance method for scheduling workflow on cloud computing platform will be presented in this paper, called non-dominated sorting particle swarm optimization (NSPSO). The proposed method is evaluated on synthetic representation of real world scientific workflow applications. The simulation results shows that the proposed method can provide better workflow scheduling solution for cloud when compared with other state-of-the-art methods.
Many evolutionary multi-objectiveoptimization (EMOs) methodologies have been proposed and shown a great potential in approximating the entire Pareto front. While in real world, what decision makers (DMs) want is one ...
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Many evolutionary multi-objectiveoptimization (EMOs) methodologies have been proposed and shown a great potential in approximating the entire Pareto front. While in real world, what decision makers (DMs) want is one or several solutions to satisfy their requirements. It has become a hot problem that dynamically using preference information provided by DMs during the optimization process guides the search of EMO algorithms. An interactive reference region-based evolutionary algorithm through decomposition is proposed, denoted as RR-MOEA/D in this paper, which focuses the search on the desire of DMs to save computational resources. MOEA/D, as a well-known multi-objectiveoptimization algorithm, is used as a basic framework here. In MOEA/D, by dealing with the sub-problems in the preference region and ignoring uninterested ones, the solutions obtained can converge to the regions which the DM prefers on the Pareto front and the computational complexity can be saved to a great extent. At each interaction, a humanized and simple interactive condition is adopted so that the reference region can be changed in a very intuitive way if the DM is unsatisfied the results in the interactive process. A rapid interaction is designed and a set of rough solutions can be obtained quickly whenever the preference information is changed. The proposed algorithm is tested on several benchmark problems and the experimental results show that the proposed algorithm can take full use of preference information and successfully converge to the reference region due to its reasonable and simple interaction mechanism.
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