scholarly journals Multiresponse Optimization of Linkage Parameters of a Compliant Mechanism Using Hybrid Genetic Algorithm-Based Swarm Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Rami Alfattani ◽  
Mohammed Yunus ◽  
Turki Alamro ◽  
Ibrahim A. Alnaser
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Compliant Mechanism ◽  
Hybrid Genetic Algorithm ◽  
Pso Algorithm ◽  
Optimal Solutions ◽  
Space Applications ◽  
Rigid Body Model ◽  
Set Up ◽  
The Impact

This research focuses on the synthesis of linkage parameters for a bistable compliant system (BSCS) to be widely implemented within space applications. Initially, BSCS was theoretically modeled as a crank-slider mechanism, utilizing pseudo-rigid-body model (PRBM) on stiffness coefficient (v), with a maximum vertical footprint (bmax) for enhancing vibration characteristics. Correlations for mechanism linkage parameters (MLPs) and responses (v and bmax) were set up by utilizing analysis of variance for response surface (RSM) technique. RSM evaluated the impact of MLPs at individual/interacting levels on responses. Consequently, a hybrid genetic algorithm-based particle swarm/flock optimization (GA-PSO) technique was employed and optimized at multiple levels for assessing ideal MLP combinations, in order to minimize characteristics (10% v  + 90% of bmax). Finally, GA-PSO estimated the most appropriate Pareto-frontal optimum solutions (PFOS) from nondominance set and crowd/flocking space approaches. The resulting PFOS from validation trials demonstrated significant improvement in responses. The adapted GA-PSO algorithm was executed with ease, extending the convergence period (through GA) and exhibiting a good diversity of objectives, allowing the development of large-scale statistics for all MLP permutations as optimal solutions. A vast set of optimal solutions can be used as a reference manual for mechanism developers.

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 Experimental illumination of natural habitat—an experimental set-up to assess the direct and indirect ecological consequences of artificial light of different spectral composition

2015 ◽  
Vol 370 (1667) ◽  
pp. 20140129 ◽  
Author(s):  
Kamiel Spoelstra ◽  
Roy H. A. van Grunsven ◽  
Maurice Donners ◽  
Phillip Gienapp ◽  
Martinus E. Huigens ◽  
...  
Keyword(s):  
Large Scale ◽  
Negative Impact ◽  
Natural Habitat ◽  
Spectral Composition ◽  
Community Level ◽  
Artificial Light ◽  
Night Time ◽  
Natural Habitats ◽  
Set Up ◽  
The Impact

Artificial night-time illumination of natural habitats has increased dramatically over the past few decades. Generally, studies that assess the impact of artificial light on various species in the wild make use of existing illumination and are therefore correlative. Moreover, studies mostly focus on short-term consequences at the individual level, rather than long-term consequences at the population and community level—thereby ignoring possible unknown cascading effects in ecosystems. The recent change to LED lighting has opened up the exciting possibility to use light with a custom spectral composition, thereby potentially reducing the negative impact of artificial light. We describe here a large-scale, ecosystem-wide study where we experimentally illuminate forest-edge habitat with different spectral composition, replicated eight times. Monitoring of species is being performed according to rigid protocols, in part using a citizen-science-based approach, and automated where possible. Simultaneously, we specifically look at alterations in behaviour, such as changes in activity, and daily and seasonal timing. In our set-up, we have so far observed that experimental lights facilitate foraging activity of pipistrelle bats, suppress activity of wood mice and have effects on birds at the community level, which vary with spectral composition. Thus far, we have not observed effects on moth populations, but these and many other effects may surface only after a longer period of time.

scholarly journals Optimal Solution for VLSI Physical Design Automation Using Hybrid Genetic Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
I. Hameem Shanavas ◽  
R. K. Gnanamurthy
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithm ◽  
Design Automation ◽  
Large Scale ◽  
Hybrid Approach ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Physical Design ◽  
Silicon Area ◽  
Vlsi Physical Design

In Optimization of VLSI Physical Design, area minimization and interconnect length minimization is an important objective in physical design automation of very large scale integration chips. The objective of minimizing the area and interconnect length would scale down the size of integrated chips. To meet the above objective, it is necessary to find an optimal solution for physical design components like partitioning, floorplanning, placement, and routing. This work helps to perform the optimization of the benchmark circuits with the above said components of physical design using hierarchical approach of evolutionary algorithms. The goal of minimizing the delay in partitioning, minimizing the silicon area in floorplanning, minimizing the layout area in placement, minimizing the wirelength in routing has indefinite influence on other criteria like power, clock, speed, cost, and so forth. Hybrid evolutionary algorithm is applied on each of its phases to achieve the objective. Because evolutionary algorithm that includes one or many local search steps within its evolutionary cycles to obtain the minimization of area and interconnect length. This approach combines a hierarchical design like genetic algorithm and simulated annealing to attain the objective. This hybrid approach can quickly produce optimal solutions for the popular benchmarks.

Improved Genetic Algorithms Based Optimal Design of The Shuangji River Aqueduct

2011 ◽  
Vol 201-203 ◽  
pp. 1288-1291
Author(s):  
Xin Li Bai ◽  
Wei Yu ◽  
Dan Fei Wang ◽  
Yuan Yuan Fan
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Structural Optimization ◽  
Large Scale ◽  
Hybrid Genetic Algorithm ◽  
Computer Software ◽  
Search Method ◽  
Discrete Variables ◽  
Original Design ◽  
Design Plan

The simple genetic algorithm (SGA) is taken as the global search method, and the traditional direct search method for mixed-discrete variables as the local search method. The improved (hybrid) genetic algorithm (IGA) is obtained by improving the SGA. And through the introduction of penalty constraints, the problem dealing with the constraints in GA is successfully resolved. A mathematical model for structural optimization of aqueduct is established, and computer software is developed for structural optimization of large-scale aqueduct based on IGA. Using this program, the Shuangji River aqueduct is optimized and Rectangle-sectioned aqueduct design plan is obtained. Compared with the original design plan, optimal design is very economical and was adopted by Design Institute.

scholarly journals HIERARCHICAL CLUSTERING APPROACH WITH HYBRID GENETIC ALGORITHM FOR COMBINATORIAL OPTIMIZATION PROBLEMS

2016 ◽  
pp. 214-218
Author(s):  
M. H. MEHTA ◽  
V. V. KAPADIA
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Hierarchical Clustering ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Pso Algorithm ◽  
Combinatorial Optimization Problems ◽  
Input Size ◽  
Clustering Approach ◽  
Definite Interval

Engineering field has inherently many combinatorial optimization problems which are hard to solve in some definite interval of time especially when input size is big. Although traditional algorithms yield most optimal answers, they need large amount of time to solve the problems. A new branch of algorithms known as evolutionary algorithms solve these problems in less time. Such algorithms have landed themselves for solving combinatorial optimization problems independently, but alone they have not proved efficient. However, these algorithms can be joined with each other and new hybrid algorithms can be designed and further analyzed. In this paper, hierarchical clustering technique is merged with IAMB-GA with Catfish-PSO algorithm, which is a hybrid genetic algorithm. Clustering is done for reducing problem into sub problems and effectively solving it. Results taken with different cluster sizes and compared with hybrid algorithm clearly show that hierarchical clustering with hybrid GA is more effective in obtaining optimal answers than hybrid GA alone.

Vehicle Scheduling Model for Fresh Agriculture Products Pickup with Uncertain Demands

2014 ◽  
Vol 974 ◽  
pp. 282-287
Author(s):  
Li Xia Rong ◽  
Huan Bin Sha
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Vehicle Scheduling ◽  
Uncertain Measure ◽  
Uncertainty Distribution ◽  
Uncertain Variables ◽  
Scheduling Model ◽  
Agriculture Products ◽  
The Impact

A chance-constrained vehicle scheduling model for fresh agriculture products pickup with uncertain demands is proposed in this paper. The uncertain measure that vehicle loading will not exceed capacity constraint is presented in the model because of the uncertainty of demands. Based on uncertainty theory, when the demands are some special uncertain variables with uncertainty distribution such as linear, zigzag and normal uncertain distribution etc., the model can be transformed to a deterministic form and solved by genetic algorithm. When the demands are general uncertain variables, a hybrid genetic algorithm with uncertain simulation is presented to obtain the optimal solution. At last, to illustrate the effective of the model and algorithm, and to analyze the impact of parameters on model solution, an experiment is provided.

scholarly journals Increased grazing drives homogenisation but reduced grazing increases turnover in upland habitat mosaics

2021 ◽  
Author(s):  
Robin J. Pakeman ◽  
Debbie A. Fielding
Keyword(s):  
Large Scale ◽  
Grazing Intensity ◽  
Sheep Grazing ◽  
Species Replacement ◽  
Habitat Mosaics ◽  
Trade Offs ◽  
Set Up ◽  
Original Species ◽  
The Impact

AbstractMany ecosystems are grazed by livestock or large, wild herbivores and exist as mosaics of different vegetation communities. Changing grazing could have an impact on heterogeneity as well as on composition. A long-term, large-scale grazing experiment that maintained existing low-intensity sheep grazing, tripled it, removed it and partially substituted sheep grazing by cattle grazing was set up on a mosaic of upland vegetation types. The impact of changing grazing regimes was assessed in terms of changes in temporal and spatial species and functional beta diversity. Removal of grazing had the highest impact on species replacement, whilst increased grazing was closest to maintaining the original species complement. Wet heath and Molina mire had the lowest turnover, but wet heath showed the highest changes in unidirectional abundance as it contained species capable of increasing in abundance in response to changing grazing intensity. Agrostis-Festuca and Nardus grasslands displayed the highest level of balanced species replacement reflecting their more dynamic vegetation. In functional terms, there was no clear separation of communities based on their grazing preference, all were relatively resistant to change but Nardus grassland was the most resistant to the removal of grazing. The increased offtake associated with increased grazing led to a degree of homogenisation as grazing tolerant species associated with preferred communities increased in the unpreferred ones. Decisions about grazing management of the uplands involve many trade-offs, and this study identified potential trade-offs between stability and homogenisation to add to existing ones on the biodiversity of different groups of species and on ecosystem services.

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