On Optimization of 2D Compliant Mechanisms Using Honeycomb Discretization With Material-Mask Overlay Strategy

2007 ◽  
Author(s):  
Anupam Saxena
Keyword(s):  
Genetic Algorithm ◽  
Compliant Mechanisms ◽  
Optimal Solutions ◽  
Free Rotation ◽  
Post Processing ◽  
Hexagonal Cell ◽  
Rectangular Cell ◽  
New Material ◽  
Assignment Approach ◽  
The Continuum

Novel honeycomb tessellation and material mask overlay methods are proposed in this paper to obtain optimal planar compliant topologies free from checkerboard and point flexure pathologies. A cardinal reason, namely the presence of strain-free rotation regions in rectangular cell based discretization is identified to be a cause in appearance of such singularities. With each hexagonal cell sharing an edge with its neighboring cells, strain-free displacements are not permitted anywhere in the continuum. The new material assignment approach manipulates material within a group of cells as opposed to a single cell thereby reducing the number of variables making optimization efficient. Optimal solutions obtained are free from intermediate material states and can be manufactured directly after design, without requiring any post processing. The proposed procedure is illustrated using two classical examples in 2D compliant mechanisms solved using genetic algorithm.

A Material-Mask Overlay Strategy for Continuum Topology Optimization of Compliant Mechanisms Using Honeycomb Discretization

2008 ◽  
Vol 130 (8) ◽  
Author(s):  
Anupam Saxena
Keyword(s):  
Compliant Mechanisms ◽  
Large Strain ◽  
Optimal Solutions ◽  
Free Rotation ◽  
Hexagonal Cell ◽  
Rectangular Cell ◽  
New Material ◽  
Single Material ◽  
Assignment Approach ◽  
The Continuum

This paper proposes novel honeycomb tessellation and material-mask overlay methods to obtain optimal single-material compliant topologies free from checkerboard and point-flexure pathologies. The presence of strain-free rotation regions in rectangular cell based discretization is identified to be a cardinal cause for appearance of such singularities. With each hexagonal cell sharing an edge with its neighboring cells, strain-free displacements are not permitted anywhere in the continuum. The new material assignment approach manipulates material within a subregion of cells as opposed to a single cell thereby reducing the number of variables making optimization efficient. Cells are allowed to get filled with only the chosen material or they can remain void. Optimal solutions obtained are free from intermediate material states and can be manufactured requiring no material interpretation and less postprocessing. Though the hexagonal cells do not allow strain-free rotations, some subregions undergoing large strain deformations can still be present within the design. The proposed procedure is illustrated using three classical examples in compliant mechanisms solved using genetic algorithm.

A multi-population state optimization algorithm for rail crack fault diagnosis

2021 ◽  
Author(s):  
Mengmeng Liu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fault Diagnosis ◽  
Optimization Algorithm ◽  
High Stability ◽  
Detection Method ◽  
Optimal Solutions ◽  
Complex Environments ◽  
Fast Convergence Rate ◽  
Low Efficiency

Abstract The rails usually work in complex environments, which makes them more prone to mechanical failures. In order to better diagnose the crack faults, a multi-population state optimization algorithm (MPVHGA) is proposed in this paper, which is used to solve the problems of low efficiency, easy precocity, and easy convergence of local optimal solutions in traditional genetic algorithms. The detection results of fault signals show that MPVHGA has the advantages of fast convergence rate, high stability, no stagnation, and no limitation of fixed iterations number. The average iterations number of MPVHGA in 100 independent iterations is about 1/5 of the traditional genetic algorithm (SGA for short) and about 1/3 of the population state optimization algorithm (VHGA for short), and the total convergence number of MPVHGA converges to 55 and 10 more than SGA and VHGA respectively, and the accuracy of fault diagnosis can reach 95.04%. On the basis of improving the performance of simple genetic algorithm, this paper provides a new detection method for rail crack fault diagnosis, which has important engineering practical value.

Optimized Crossover JumpX in Genetic Algorithm for General Routing Problems

2018 ◽  
pp. 205-230
Author(s):  
Hicham El Hassani ◽  
Said Benkachcha ◽  
Jamal Benhra
Keyword(s):  
Genetic Algorithm ◽  
Optimization Methods ◽  
Search Space ◽  
Optimal Solutions ◽  
Solution Quality ◽  
Path Representation ◽  
Representation Technique ◽  
Hard Problems ◽  
The Traveling Salesman Problem

Inspired by nature, genetic algorithms (GA) are among the greatest meta-heuristics optimization methods that have proved their effectiveness to conventional NP-hard problems, especially the traveling salesman problem (TSP) which is one of the most studied Supply chain management problems. This paper proposes a new crossover operator called Jump Crossover (JMPX) for solving the travelling salesmen problem using a genetic algorithm (GA) for near-optimal solutions, to conclude on its efficiency compared to solutions quality given by other conventional operators to the same problem, namely, Partially matched crossover (PMX), Edge recombination Crossover (ERX) and r-opt heuristic with consideration of computational overload. We adopt the path representation technique for our chromosome which is the most direct representation and a low mutation rate to isolate the search space exploration ability of each crossover. The experimental results show that in most cases JMPX can remarkably improve the solution quality of the GA compared to the two existing classic crossover approaches and the r-opt heuristic.

Genetic Algorithm and Other Meta-Heuristics

2005 ◽  
pp. 144-173 ◽  
Author(s):  
Bernard K.S. Cheung
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Schema Theory ◽  
Heuristic Methods ◽  
Optimal Solutions ◽  
Corporate Globalization ◽  
Global Optimal ◽  
Mutation And Selection

Genetic algorithms have been applied in solving various types of large-scale, NP-hard optimization problems. Many researchers have been investigating its global convergence properties using Schema Theory, Markov Chain, etc. A more realistic approach, however, is to estimate the probability of success in finding the global optimal solution within a prescribed number of generations under some function landscapes. Further investigation reveals that its inherent weaknesses that affect its performance can be remedied, while its efficiency can be significantly enhanced through the design of an adaptive scheme that integrates the crossover, mutation and selection operations. The advance of Information Technology and the extensive corporate globalization create great challenges for the solution of modern supply chain models that become more and more complex and size formidable. Meta-heuristic methods have to be employed to obtain near optimal solutions. Recently, a genetic algorithm has been reported to solve these problems satisfactorily and there are reasons for this.

scholarly journals A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGA

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Alberto Pajares ◽  
Xavier Blasco ◽  
Juan M. Herrero ◽  
Gilberto Reynoso-Meza
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Decision Maker ◽  
Pareto Front ◽  
Optimization Problem ◽  
Engineering Problem ◽  
Multiobjective Optimization Problem ◽  
Optimal Solutions ◽  
Multiobjective Genetic Algorithm ◽  
Pi Controllers

Traditionally, in a multiobjective optimization problem, the aim is to find the set of optimal solutions, the Pareto front, which provides the decision-maker with a better understanding of the problem. This results in a more knowledgeable decision. However, multimodal solutions and nearly optimal solutions are ignored, although their consideration may be useful for the decision-maker. In particular, there are some of these solutions which we consider specially interesting, namely, the ones that have distinct characteristics from those which dominate them (i.e., the solutions that are not dominated in their neighborhood). We call these solutions potentially useful solutions. In this work, a new genetic algorithm called nevMOGA is presented, which provides not only the optimal solutions but also the multimodal and nearly optimal solutions nondominated in their neighborhood. This means that nevMOGA is able to supply additional and potentially useful solutions for the decision-making stage. This is its main advantage. In order to assess its performance, nevMOGA is tested on two benchmarks and compared with two other optimization algorithms (random and exhaustive searches). Finally, as an example of application, nevMOGA is used in an engineering problem to optimally adjust the parameters of two PI controllers that operate a plant.

A Genetic Algorithm Based Procedure for Extracting Optimal Solutions From a Morphological Chart

2007 ◽  
Author(s):  
Santosh Tiwari ◽  
Joshua Summers ◽  
Georges Fadel
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Mathematical Representation ◽  
Optimal Solutions ◽  
Proof Of Concept ◽  
Optimization Framework ◽  
Mutation Operators ◽  
Novel Approach ◽  
Morphological Chart ◽  
Crossover And Mutation

A novel approach using a genetic algorithm is presented for extracting globally satisfycing (Pareto optimal) solutions from a morphological chart where the evaluation and combination of “means to sub-functions” is modeled as a combinatorial multi-objective optimization problem. A fast and robust genetic algorithm is developed to solve the resulting optimization problem. Customized crossover and mutation operators specifically tailored to solve the combinatorial optimization problem are discussed. A proof-of-concept simulation on a practical design problem is presented. The described genetic algorithm incorporates features to prevent redundant evaluation of identical solutions and a method for handling of the compatibility matrix (feasible/infeasible combinations) and addressing desirable/undesirable combinations. The proposed approach is limited by its reliance on the quantifiable metrics for evaluating the objectives and the existence of a mathematical representation of the combined solutions. The optimization framework is designed to be a scalable and flexible procedure which can be easily modified to accommodate a wide variety of design methods that are based on the morphological chart.

A method of multiobjective optimization using a genetic algorithm and an artificial immune system

2009 ◽  
Vol 223 (5) ◽  
pp. 1243-1252 ◽  
Author(s):  
H Park ◽  
N-S Kwak ◽  
J Lee
Keyword(s):  
Genetic Algorithm ◽  
Pattern Recognition ◽  
Immune System ◽  
Artificial Immune System ◽  
Affinity Maturation ◽  
Pareto Optimal ◽  
Mathematical Function ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Artificial Immune

The immune system has pattern recognition capabilities based on reinforced learning, memory, and affinity maturation interacting between antigens (Ags) and antibodies (Abs). This article deals with an adaptation of artificial immune system (AIS) into genetic-algorithm (GA)-based multi-objective optimization. The present study utilizes the pattern recognition from an AIS and the evolution from a GA. Using affinity measures between Ags and Abs, GA-based immune simulation discovers a generalist Ab that represents the common pattern among Ags. Non-dominated Pareto-optimal solutions are obtained via GA-based immune simulation in which dominated designs are considered as Ags, whereas non-dominated designs are assigned to Abs. This article discusses the procedure of identifying Pareto-optimal solutions through the immune system-based pattern recognition. A number of mathematical function problems that are described by discontinuity or disconnection in the shape of Pareto surface are first examined as test examples. Subsequently, engineering optimization problems such as rotating flywheel disc and ten-bar planar truss are explored to support the present study.

Iterative Fixture Layout and Clamping Force Optimization Using the Genetic Algorithm

2001 ◽  
Vol 124 (1) ◽  
pp. 119-125 ◽  
Author(s):  
Krishnakumar Kulankara ◽  
Srinath Satyanarayana ◽  
Shreyes N. Melkote
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Programming ◽  
Primary Objective ◽  
Fixture Design ◽  
Clamping Force ◽  
Optimal Solutions ◽  
Fixture Layout ◽  
Force Optimization ◽  
Nonlinear Programming Methods ◽  
Objective Being

Fixture design is a critical step in machining. An important aspect of fixture design is the optimization of the fixture, the primary objective being the minimization of workpiece deflection by suitably varying the layout of fixture elements and the clamping forces. Previous methods for fixture design optimization have treated fixture layout and clamping force optimization independently and/or used nonlinear programming methods that yield sub-optimal solutions. This paper deals with application of the genetic algorithm (GA) for fixture layout and clamping force optimization for a compliant workpiece. An iterative algorithm that minimizes the workpiece elastic deformation for the entire cutting process by alternatively varying the fixture layout and clamping force is proposed. It is shown via an example of milling fixture design that this algorithm yields a design that is superior to the result obtained from either fixture layout or clamping force optimization alone.

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