Integrated total cost and Tolerance Optimization with Genetic Algorithm

Kumar, R.Sampath; Alagumurthi, N.

doi:10.2991/ijcis.2010.3.3.8

Cited by 7 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, the roulette-wheel method of selection and the single-point method of crossover are applied to achieve the proposed procedure (Beasley and Chu, 1996;Kumar and Alagumurthi, 2010). In view of the fact that the number of '0' is more than '1' in a chromosome coded in our problem, this study constructs a special mutation operator to respond this situation.…”

Section: Genetic Operatorsmentioning

confidence: 99%

Computational Intelligence for Traffic and Mobility

Wang¹,

Wets²

2013

Atlantis Computational Intelligence Systems

View full text Add to dashboard Cite

Aims and scope of the seriesThe series 'Atlantis Computational Intelligence Systems' aims at covering state-of-theart research and development in all fields where computational intelligence is investigated and applied. The series seeks to publish monographs and edited volumes on foundations and new developments in the field of computational intelligence, including fundamental and applied research as well as work describing new, emerging technologies originating from computational intelligence research. Applied CI research may range from CI applications in the industry to research projects in the life sciences, including research in biology, physics, chemistry and the neurosciences.All books in this series are co-published with Springer.For more information on this series and our other book series, please visit our website at:www.atlantis-press.com PrefaceAdvances in mobility are most clearly illustrated by the spread of motorized traffic and transport, the development of the transportation industry, including vehicle manufacturing and its associated infrastructure. These developments represent one of the main challenges for information science and intelligent technology. The discipline of Intelligent Transportation System (ITS) is widely applied to solve transportation problems including congestion, accidents and pollution emissions.Since our daily life and work are closely related to traffic and mobility, traffic demand has increased dramatically. Following this increase, we additionally have to balance the increasing desire for mobility and efficiency with the societal concerns about traffic problems. Today's traffic demand is predominantly served by individual motorized vehicles, which are the primary means of transportation. As the traffic demand has continuously grown faster than the construction of infrastructure over the last decades, traffic congestion has become a severe problem in many countries. Meanwhile, as vehicle speed is increasing, traffic safety has also become an important and socially relevant topic, which has impact on social and economic developments. Additionally, the environmental impact and energy consumption caused by traffic systems also arouse great public concern.The engineers and scientists in different areas are seeking solutions as to how the traffic system could be used more efficiently and how operations could be improved by using new technologies and new methodologies. Recently, computational intelligence methods have received considerable attention regarding their potential as a powerful technique for traffic and mobility problems. Computational Intelligence (CI) is the study of adaptive mechanisms enabling or facilitating intelligent behaviour in complex and changing environments. As such, CI combines artificial neural networks, evolutionary computing, swarm intelligence and fuzzy systems. The characteristic of 'intelligence' is usually attributed to v vi Computational Intelligence for Traffic and Mobility humans. The concept of intelligence is directly linked to reasoning and...

show abstract

Section: Genetic Operatorsmentioning

confidence: 99%

Computational Intelligence for Traffic and Mobility

Wang¹,

Wets²

2013

Atlantis Computational Intelligence Systems

View full text Add to dashboard Cite

show abstract

“…It is obvious that a trial and error approach does not ensure the convergence to optimal solutions. 19 In the recent years, several intelligent optimization algorithms have been used in different applications such as: (a) genetic algorithm (GA) in scheduling problem 21 , total cost and allocation problem 22 , obtaining the optimal rule set and the membership function for fuzzybased systems 23 , and facility location problem 24 , (b) ant colony optimization (ACO) in chaotic synchronization 25 and grouping machines and parts into cells 26 , (c) artificial immune method in several nonlinear systems 27 , (d) particle swarm optimization (PSO) in singleobjective and multi-objective problems 28,29 , bandwidth prediction 30 , parameter identification of chaotic systems 31 , QoS-aware web service selection in service oriented communication problem 32 , and solving multimodal problems 33 , (e) harmony search (HS) algorithm for synchronization of discrete-time chaotic systems 34 . Among the mentioned approaches, PSO which has been proposed by Kennedy and Eberhart 35 is inherently continuous and simulates the social behavior of a flock of birds.…”

Section: Introductionmentioning

confidence: 99%

PSO-Optimized Hopfield Neural Network-Based Multipath Routing for Mobile Ad-hoc Networks

Sheikhan¹,

Hemmati²

2012

IJCIS

View full text Add to dashboard Cite

Mobile ad-hoc network (MANET) is a dynamic collection of mobile computers without the need for any existing infrastructure. Nodes in a MANET act as hosts and routers. Designing of robust routing algorithms for MANETs is a challenging task. Disjoint multipath routing protocols address this problem and increase the reliability, security and lifetime of network. However, selecting an optimal multipath is an NP-complete problem. In this paper, Hopfield neural network (HNN) which its parameters are optimized by particle swarm optimization (PSO) algorithm is proposed as multipath routing algorithm. Link expiration time (LET) between each two nodes is used as the link reliability estimation metric. This approach can find either node-disjoint or link-disjoint paths in singlephase route discovery. Simulation results confirm that PSO-HNN routing algorithm has better performance as compared to backup path set selection algorithm (BPSA) in terms of the path set reliability and number of paths in the set.

show abstract

“…It is also a bio-inspired algorithm and is used to solve many optimization problems. 12,29,30 It starts searching with the initial population where individuals are distributed in the search space. It works by iterating the three operators that are selection, crossover and mutation.…”

Section: Introductionmentioning

confidence: 99%

A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem

Gao¹,

Chen²

2011

IJCIS

View full text Add to dashboard Cite

Distributed Permutation Flowshop Scheduling Problem (DPFSP) is a newly proposed scheduling problem, which is a generalization of classical permutation flow shop scheduling problem. The DPFSP is NP-hard in general. It is in the early stages of studies on algorithms for solving this problem. In this paper, we propose a GA-based algorithm, denoted by GA_LS, for solving this problem with objective to minimize the maximum completion time. In the proposed GA_LS, crossover and mutation operators are designed to make it suitable for the representation of DPFSP solutions, where the set of partial job sequences is employed. Furthermore, GA_LS utilizes an efficient local search method to explore neighboring solutions. The local search method uses three proposed rules that move jobs within a factory or between two factories. Intensive experiments on the benchmark instances, extended from Taillard instances, are carried out. The results indicate that the proposed hybrid genetic algorithm can obtain better solutions than all the existing algorithms for the DPFSP, since it obtains better relative percentage deviation and differences of the results are also statistically significant. It is also seen that best-known solutions for most instances are updated by our algorithm. Moreover, we also show the efficiency of the GA_LS by comparing with similar genetic algorithms with the existing local search methods.

show abstract

Integrated total cost and Tolerance Optimization with Genetic Algorithm

Cited by 7 publications

References 16 publications

Computational Intelligence for Traffic and Mobility

Computational Intelligence for Traffic and Mobility

PSO-Optimized Hopfield Neural Network-Based Multipath Routing for Mobile Ad-hoc Networks

A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem

Contact Info

Product

Resources

About