OAPS: An Optimization Algorithm for Part Separation in Assembly Design for Additive Manufacturing

2018 ◽  
Author(s):  
Angshuman Deka ◽  
Sara Behdad
Keyword(s):  
Additive Manufacturing ◽  
Optimization Algorithm ◽  
Cutting Planes ◽  
Processing Time ◽  
Optimization Technique ◽  
Optimal Number ◽  
Hill Climbing ◽  
Productivity Improvement ◽  
Cutting Processes ◽  
The Hill

Additive Manufacturing (AM) provides the advantage of producing complex shapes that are not possible through traditional cutting processes. Along with this line, assembly-based part design in AM creates some opportunities for productivity improvement. This paper proposes an improved optimization algorithm for part separation (OAPS) in assembly-based part design in additive manufacturing. For a given object, previous studies often provide the optimal number of parts resulting from cutting processes and their corresponding orientation to obtain the minimum processing time. During part separation, the cutting plane direction to generate subparts for assembly was often selected randomly in previous studies. The current work addresses the use of random cutting planes for part separation and instead uses the hill climbing optimization technique to generate the cutting planes to separate the parts. The OAPS provides the optimal number of assemblies and the build orientation of the parts for the minimum processing time. Two examples are provided to demonstrate the application of OAPS algorithm.

An Improved B-hill Climbing Optimization Technique for Solving the Text Documents Clustering Problem

2020 ◽  
Vol 16 (4) ◽  
pp. 296-306 ◽  
Author(s):  
Laith Mohammad Abualigah ◽  
Essam Said Hanandeh ◽  
Ahamad Tajudin Khader ◽  
Mohammed Abdallh Otair ◽  
Shishir Kumar Shandilya
Keyword(s):  
Optimization Technique ◽  
Document Clustering ◽  
Text Clustering ◽  
Hill Climbing ◽  
Text Documents ◽  
Clustering Problem ◽  
Text Document ◽  
Text Information ◽  
Amount Of Knowledge ◽  
The Hill

Background: Considering the increasing volume of text document information on Internet pages, dealing with such a tremendous amount of knowledge becomes totally complex due to its large size. Text clustering is a common optimization problem used to manage a large amount of text information into a subset of comparable and coherent clusters. Aims: This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster. Methods: The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean techniques. Results: Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem. Conclusion: The performance of the text clustering is useful by adding the β operator to the hill climbing.

scholarly journals Buyer Inspired Meta-Heuristic Optimization Algorithm

2020 ◽  
Vol 10 (1) ◽  
pp. 194-219 ◽  
Author(s):  
Sanjoy Debnath ◽  
Wasim Arif ◽  
Srimanta Baishya
Keyword(s):  
Optimization Algorithm ◽  
Optimization Technique ◽  
Statistical Test ◽  
Heuristic Optimization ◽  
Product Choice ◽  
Exploration And Exploitation ◽  
The Social ◽  
Engineering Problems ◽  
The Mean

AbstractNature inspired swarm based meta-heuristic optimization technique is getting considerable attention and established to be very competitive with evolution based and physical based algorithms. This paper proposes a novel Buyer Inspired Meta-heuristic optimization Algorithm (BIMA) inspired form the social behaviour of human being in searching and bargaining for products. In BIMA, exploration and exploitation are achieved through shop to shop hoping and bargaining for products to be purchased based on cost, quality of the product, choice and distance to the shop. Comprehensive simulations are performed on 23 standard mathematical and CEC2017 benchmark functions and 3 engineering problems. An exhaustive comparative analysis with other algorithms is done by performing 30 independent runs and comparing the mean, standard deviation as well as by performing statistical test. The results showed significant improvement in terms of optimum value, convergence speed, and is also statistically more significant in comparison to most of the reported popular algorithms.

scholarly journals A modified simplex based direct search optimization algorithm for adaptive transversal FIR filters

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110254
Author(s):  
Armaghan Mohsin ◽  
Yazan Alsmadi ◽  
Ali Arshad Uppal ◽  
Sardar Muhammad Gulfam
Keyword(s):  
Optimization Algorithm ◽  
Optimization Technique ◽  
Fast Convergence ◽  
Good Convergence ◽  
Optimum Point ◽  
Search Optimization ◽  
Higher Dimensional ◽  
Gradient Based ◽  
Improved Accuracy ◽  
Quadratic Cost Function

In this paper, a novel modified optimization algorithm is presented, which combines Nelder-Mead (NM) method with a gradient-based approach. The well-known Nelder Mead optimization technique is widely used but it suffers from convergence issues in higher dimensional complex problems. Unlike the NM, in this proposed technique we have focused on two issues of the NM approach, one is shape of the simplex which is reshaped at each iteration according to the objective function, so we used a fixed shape of the simplex and we regenerate the simplex at each iteration and the second issue is related to reflection and expansion steps of the NM technique in each iteration, NM used fixed value of [Formula: see text], that is, [Formula: see text]  = 1 for reflection and [Formula: see text]  = 2 for expansion and replace the worst point of the simplex with that new point in each iteration. In this way NM search the optimum point. In proposed algorithm the optimum value of the parameter [Formula: see text] is computed and then centroid of new simplex is originated at this optimum point and regenerate the simplex with this centroid in each iteration that optimum value of [Formula: see text] will ensure the fast convergence of the proposed technique. The proposed algorithm has been applied to the real time implementation of the transversal adaptive filter. The application used to demonstrate the performance of the proposed technique is a well-known convex optimization problem having quadratic cost function, and results show that the proposed technique shows fast convergence than the Nelder-Mead method for lower dimension problems and the proposed technique has also good convergence for higher dimensions, that is, for higher filter taps problem. The proposed technique has also been compared with stochastic techniques like LMS and NLMS (benchmark) techniques. The proposed technique shows good results against LMS. The comparison shows that the modified algorithm guarantees quite acceptable convergence with improved accuracy for higher dimensional identification problems.

scholarly journals A Hybrid Evolutionary Approach to Design Off-Grid Electrification Projects with Distributed Generation

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
J. Avilés ◽  
J. C. Mayo-Maldonado ◽  
O. Micheloud
Keyword(s):  
Distributed Generation ◽  
Network Topology ◽  
Optimization Technique ◽  
Optimal Number ◽  
Optimal Placement ◽  
Evolutionary Approach ◽  
Power Losses ◽  
Network Configuration ◽  
Optimal Network ◽  
Particle Swarm Optimization Technique

A hybrid evolutionary approach is proposed to design off-grid electrification projects that require distributed generation (DG). The design of this type of systems can be considered as an NP-Hard combinatorial optimization problem; therefore, due to its complexity, the approach tackles the problem from two fronts: optimal network configuration and optimal placement of DG. The hybrid scheme is based on a particle swarm optimization technique (PSO) and a genetic algorithm (GA) improved with a heuristic mutation operator. The GA-PSO scheme permits finding the optimal network topology, the optimal number, and capacity of the generation units, as well as their best location. Furthermore, the algorithm must design the system under power quality requirements, network radiality, and geographical constraints. The approach uses GPS coordinates as input data and develops a network topology from scratch, driven by overall costs and power losses minimization. Finally, the proposed algorithm is described in detail and real applications are discussed, from which satisfactory results were obtained.

scholarly journals Power Loss Calculation in 11-Level Cascaded H-Bridge Inverter using Harmony Search Optimized Switching Scheme

2019 ◽  
Vol 8 (6) ◽  
pp. 5200-5205
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
System Analysis ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Harmony Search ◽  
Multilevel Inverter ◽  
Total Power ◽  
Multi Objective Optimization ◽  
Multi Objective

Power loss is the most significant parameter in power system analysis and its adequate calculation directly effects the economic and technical evaluation. This paper aims to propose a multi-objective optimization algorithm which optimizes dc source magnitudes and switching angles to yield minimum THD in cascaded multilevel inverters. The optimization algorithm uses metaheuristic approach, namely Harmony Search algorithm. The effectiveness of the multi-objective algorithm has been tested with 11-level Cascaded H-Bridge Inverter with optimized DC voltage sources using MATLAB/Simulink. As the main objective of this research paper is to analyze total power loss, calculations of power loss are simplified using approximation of curves from datasheet values and experimental measurements. The simulation results, obtained using multi-objective optimization method, have been compared with basic SPWM, optimal minimization of THD, and it is confirmed that the multilevel inverter fired using multi- objective optimization technique has reduced power loss and minimum THD for a wide operating range of multilevel inverter.

Jaya Optimization Algorithm Implemented on A New Novel Design of 6-DOF AUV Body: A Case Study

2021 ◽  
Author(s):  
Noorulden Basil ◽  
Hamzah M. Marhoon ◽  
Ahmed R. Ibrahim
Keyword(s):  
Optimization Algorithm ◽  
Autonomous Underwater Vehicle ◽  
Optimization Technique ◽  
Pid Controllers ◽  
The Novel ◽  
Proportional Integral Derivative ◽  
New Novel ◽  
Novel Design ◽  
Optimum Output

Abstract The Novel Jaya Optimization Algorithm (JOA) was utilized in this research to evaluate the efficiency of a new novel design of Autonomous Underwater Vehicle (AUV). The Three Proportional Integral Derivative (PID) controllers were used to obtain the optimum output for the AUV Trajectory, which can be considered as a main side of the research for solving the AUV Performance. The optimization technique has been developed to solving the motion model of the AUV in order to reduce the rotations of trajectory for the AUV 6-DOF Body in the axis’s in x, y and z for the overall positions, velocity... etc., and to execute the optimum output for the dynamic kinematics model based on the Novel Euler-6 DOF AUV Body Equation implemented on MATLAB R2021a Version.

scholarly journals Modified Average of the Base-Level Models in the Hill-Climbing Bagged Ensemble Selection Algorithm for Credit Scoring

2019 ◽  
Vol 157 ◽  
pp. 229-237
Author(s):  
Tri Handhika ◽  
Achmad Fahrurozi ◽  
Revaldo Ilfestra Metzi Zen ◽  
Dewi Putrie Lestari ◽  
Ilmiyati Sari ◽  
...  
Keyword(s):  
Credit Scoring ◽  
Hill Climbing ◽  
Selection Algorithm ◽  
Base Level ◽  
Ensemble Selection ◽  
The Hill

Optimization of Smart Wells in the St. Joseph Field

2010 ◽  
Vol 13 (04) ◽  
pp. 588-595 ◽  
Author(s):  
G. M. van Essen ◽  
J. D. Jansen ◽  
D. R. Brouwer ◽  
S. G. Douma ◽  
M. J. Zandvliet ◽  
...  
Keyword(s):  
Optimization Technique ◽  
Gas Injection ◽  
Optimal Number ◽  
Control Parameters ◽  
Operational Strategies ◽  
Sector Model ◽  
Optimization Study ◽  
Smart Wells ◽  
Gradient Based ◽  
Reservoir Simulator

Summary The St. Joseph field has been on production since September 1981 under natural depletion supported by crestal gas injection. As part of a major redevelopment study, the scope for waterflooding was addressed using "smart" completions with multiple inflow control valves (ICVs) in the wells to be drilled for the redevelopment. Optimal control theory was used to optimize monetary value over the remaining producing life of the field, and in particular to select the optimal number of ICVs, the optimal configuration of the perforation zones, and the optimal operational strategies for the ICVs. A gradient-based optimization technique was implemented in a reservoir simulator equipped with the adjoint functionality to compute gradients of an objective function with respect to control parameters. For computational reasons, an initial optimization study was performed on a sector model, which showed promising results.

Efficiency Enhancement in a Medium Scale Gearbox Manufacturing Company through Different Lean Tools - A Case Study

2018 ◽  
Vol 34 ◽  
pp. 128-138 ◽  
Author(s):  
V. Saravanan ◽  
S. Nallusamy ◽  
Abraham George
Keyword(s):  
Lean Manufacturing ◽  
Processing Time ◽  
Assembly Line ◽  
Value Added ◽  
Value Stream Mapping ◽  
Productivity Improvement ◽  
Medium Scale ◽  
Value Stream ◽  
Manufacturing Company

Productivity is an important parameter for all small and medium scale manufacturing industries. Lean manufacturing emerged as production strategy capable of increasing productivity by identifying and eliminating non value added activities. This article deals with productivity improvement in a pre-assembly line of gearbox manufacturing company with a case study using lean concepts like process flow chart, process Gantt chart and time study. This paper illustrates using a case study on how a value stream mapping has to be carried out in a planet carrier pre-assembly line. Value stream mapping and work standardization are the key tools used in lean manufacturing and lean transformation. It makes the process smoother, helps in reduction of lead time and ultimately increasing the productivity. From the observed results it was found that, the productivity has been increased from 7 pieces to 10 pieces in the first step assembly when the proposed VSM was implemented. The second step processing time was reduced by the execution of proposed value stream mapping with TAKT time of 126 minutes and 165 minutes of processing time for demand of 10 pieces were achieved and the overall processing time has been reduced by about 24%.

scholarly journals Adaptive fitness landscape for replicator systems: to maximize or not to maximize

2018 ◽  
Vol 13 (3) ◽  
pp. 25 ◽  
Author(s):  
Alexander S. Bratus ◽  
Yuri S. Semenov ◽  
Artem S. Novozhilov
Keyword(s):  
Natural Selection ◽  
Fitness Landscape ◽  
Evolutionary Process ◽  
Hill Climbing ◽  
Adaptive Landscape ◽  
Basic Model ◽  
Mean Fitness ◽  
The Mean ◽  
The Hill ◽  
Fisher’S Fundamental Theorem

Sewall Wright’s adaptive landscape metaphor penetrates a significant part of evolutionary thinking. Supplemented with Fisher’s fundamental theorem of natural selection and Kimura’s maximum principle, it provides a unifying and intuitive representation of the evolutionary process under the influence of natural selection as the hill climbing on the surface of mean population fitness. On the other hand, it is also well known that for many more or less realistic mathematical models this picture is a severe misrepresentation of what actually occurs. Therefore, we are faced with two questions. First, it is important to identify the cases in which adaptive landscape metaphor actually holds exactly in the models, that is, to identify the conditions under which system’s dynamics coincides with the process of searching for a (local) fitness maximum. Second, even if the mean fitness is not maximized in the process of evolution, it is still important to understand the structure of the mean fitness manifold and see the implications of this structure on the system’s dynamics. Using as a basic model the classical replicator equation, in this note we attempt to answer these two questions and illustrate our results with simple well studied systems.

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