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International Conference on Artificial Intelligence and Computational Intelligence

作者： He, Junliang Mi, Weijian Chang, Daofang Yan, Wei Shanghai Maritime Univ Logist Engn Sch Shanghai 200135 Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200130 Peoples R China

ISBN: (纸本)9780769538167

A rolling-horizon approach was proposed, which aims at the problem of berth allocation and quay crane assignment. Then a dynamic allocation model using objective programming was initially developed for berths allocation and quay crane assignment, which more closes realistic as result of basing continuum quayside. The model objective function was subject to the minimization of the total berthing location deviation, the total penalty and the energy consumption of quay cranes. Then, a hybrid parallel genetic algorithm (HPGA) was employed for solving the model, which combines parallel genetic algorithm (PGA) and heuristic algorithm. Furthermore, a simulation model integrating HPGA was developed for evaluating this HPGA and executing gene repair techniques to repair the unfeasible individuals generated by HPGA. Finally, case study on a specific container terminal was used for system illustration, and then verified the validity and usefulness of this model and algorithm.

关键词： berth allocation quay crane assignment objective programming rolling-horizon technique heuristic algorithm parallel genetic algorithm

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A Improved parallel genetic algorithm Based on Fixed Point Theory for the Optimal Design of Multi-body Model Vehicle Suspensions

A Improved Parallel Genetic Algorithm Based on Fixed Point T...

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2nd IEEE International Conference on Computer Science and Information Technology

作者： Liu, Guangyuan Zhang, Jingjun Gao, Ruizhen Sun, Yang Hebei Univ Engn Sci Res Off Handan Peoples R China Hebei Univ Engn Coll Elect & Informat Engn Handan Peoples R China

ISBN: (纸本)9781424445189

Based on an improved genetic algorithm, a parallel genetic algorithm is presented and the skeleton implementing is constituted in this paper. The van der Laan-Talman algorithm is introduced to the genetic algorithm to design convergence criteria objectively and to solve the convergence problem in the later period. The parallel genetic algorithm of multi-body model vehicle suspension optimization is implemented through establishing the interface between ADAMS software and the genetic algorithm. The results show that the parallel genetic algorithm developed in this paper is efficient.

关键词： Vehicle suspension genetic algorithm parallel genetic algorithm Fixed Point Theory Cluster system

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Selective vehicle routing problems under uncertainty without recourse

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2014年 62卷 68-88页

作者： Allahviranloo, Mandieh Chow, Joseph Y. J. Recker, Will W. Univ Calif Irvine Inst Transportat Studies Irvine CA 92697 USA Ryerson Univ Dept Civil Engn Toronto ON M5B 2K3 Canada

We argue that the selective vehicle routing problem is more appropriate than the conventional VRP in handling uncertainty with limited resources. However, previous formulations of selective VRPs have all been deterministic. Three new formulations are proposed to account for different optimization strategies under uncertain demand (or utility) level: reliable, robust, and fuzzy selective vehicle routing problems. Three parallel genetic algorithms (PGAs) and a classic genetic algorithm are developed and compared to the deterministic solution. PGAs differ based on their communication strategies and diversity in sub-populations. Results show that a PGA, wherein communication between demes, or subpopulations, occurs in every generation and does not eliminate repeated chromosomes, outperforms other algorithms at the cost of higher computation time. A faster variation of PGA is used to solve the non-convex reliable selective VRP, robust selective VRP and the large-scale fuzzy selective VRP, consisting of 200 nodes. Large scale application demonstrates the value of fuzzy selective vehicle routing problem FSVRP in humanitarian logistics. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Selective vehicle routing problem Stochastic optimization Fuzzy optimization Reliability Robust optimization parallel genetic algorithm Humanitarian logistics

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A parallel genetic algorithm in Multi-objective Optimization

A Parallel Genetic Algorithm in Multi-objective Optimization

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21st Chinese Control and Decision Conference

作者： Wang Zhi-xin Ju Gang Southeast Univ Sch Energy & Environm Nanjing 210096 Peoples R China

ISBN: (纸本)9781424427239

Based on the combination of NSGA-II algorithm and parallel genetic algorithm, this paper presents a parallel genetic algorithm for multi-objective optimization (PNSGA). At the evolving process of this new algorithm, an individual migration to improve the parallel searching speed is applied to improve the efficiency of this algorithm and the accuracy of Pareto optimal set;at the same time, an individual update strategy is introduced to keep the diversity of Pareto optimal set. Data show that the Pareto optimal solutions or the solution candidates output by PNSGA that are scattered extensively and uniformly.

关键词： Multi-objective optimization NSGA-II parallel genetic algorithm Individual migration Individual update

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A parallel genetic algorithm for Shortest Path Routing Problem

A Parallel Genetic Algorithm for Shortest Path Routing Probl...

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International Conference on Future Computer and Communications (ICFCC 2009)

作者： Yussof, Salman Razali, Rina Azlin See, Ong Hang Department of System and Networking Malaysia Department of Electronic and Communication Engineering Malaysia

ISBN: (纸本)9780769535913

Shortest path routing is the type of routing widely used in computer networks nowadays. Even though shortest path routing algorithms are well established, other alternative methods may have their own advantages. One such alternative is to use a GA-based routing algorithm. Based on previous research, GA-based routing algorithm has been found to be more scalable and insensitive to variations in network topologies. However, it is also known that GA-based routing algorithm is not fast enough for real-time computation. In this paper, we proposed a parallel genetic algorithm for solving the shortest path routing problem with the aim to reduce its computation time. This algorithm is developed and run on an MPI cluster. Based on experimental result, there is a tradeoff between computation time and the result accuracy. However, for the same level of accuracy, the proposed parallel algorithm can perform much faster compared to its non-parallel counterpart.

关键词： component Coarse grained message passing interface parallel genetic algorithm shortest path routing

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A Coarse-grained parallel genetic algorithm with Migration for Shortest Path Routing Problem

A Coarse-grained Parallel Genetic Algorithm with Migration f...

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11th IEEE International Conference on High Performance Computing and Communications

作者： Yussof, Salman Razali, Rina Azlin See, Ong Hang Ghapar, Azimah Abdul Din, Marina Md

ISBN: (纸本)9781424446001

Shortest path routing is the ope of routing widely used in computer networks nowadays. Even though shortest path routing algorithms are well established, other alternative methods may have their own advantages. One such alternative is to use a GA-based routing algorithm. Based on previous research, GA-based routing algorithm has been found to be more scalable and insensitive to variations in network topologies, However, it is also known that GA-based routing algorithm is not fast enough for real-time computation. In this paper, we proposed a coarse-grained parallel genetic algorithm for solving the shortest path routing problem with the aim to reduce its computation time. The migration scheme, which is commonly used in coarse-grained parallel genetic algorithm, is also employed in the proposed algorithm. This algorithm is developed and run an an MPI cluster. This paper studies the effect of migration on the proposed algorithm and the performance of the algorithm as compared to its serial counterpart.

关键词： Coarse grained message passing interface parallel genetic algorithm shortest path routing

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Solving Cryptarithmetic Problems Using parallel genetic algorithm

Solving Cryptarithmetic Problems Using Parallel Genetic Algo...

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2nd International Conference on Computer and Electrical Engineering

作者： Abbasian, Reza Mazloom, Masoud Shahid Chamran Univ Dept Comp Engn Ahvaz Iran

ISBN: (纸本)9780769539256

Cryptarithmetic is a class of constraint satisfaction problems which includes making mathematical relations between meaningful words using simple arithmetic operators like 'plus' in a way that the result is conceptually true, and assigning digits to the letters of these words and generating numbers in order to make correct arithmetic operations as well. A simple way to solve such problems is by depth first search (DFS) algorithm which has a big search space even for quite small problems. In this paper we proposed a solution to this problem with genetic algorithm and then optimized it by using parallelism. We also showed that the algorithm reaches a solution faster and in a smaller number of iterations than similar algorithms.

关键词： constraint satisfaction cryptarithmetic parallel genetic algorithm

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parallelized genetic Operations for SBST using Hadoop MapReduce Framework

Parallelized Genetic Operations for SBST using Hadoop MapRed...

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International Conference on Advanced Communication Control and Computing Technologies*

作者： Mayandi, Geethapriya Arumugam, Chamundeswari SSN Coll Engn Dept Comp Sci & Engn Software Engn Madras Tamil Nadu India SSN Coll Engn Dept Comp Sci & Engn Madras Tamil Nadu India

ISBN: (纸本)9781479939145

Search Based Software Testing (SBST) is one of the most explored fields in Software Testing. It suffers from the optimization problem to execute the Software Under Test (SUT). This problem is addressed mostly using genetic algorithm (GA) and it involves three operations namely selection, crossover and mutation to accomplish a global search to yield fitness solution to run the SUT successfully. In existing work, GA is combined with Hadoop MapReduce to give parallel genetic algorithm (PGA). Here, mapper function performs parallel fitness computation and reducer function performs the GA. This PGA generates test suite that makes the entire SUT to get executed. This paper makes an attempt to existing by parallelizing fitness calculation and GA operations to generate search test data for the SUT based on branch coverage criteria.

关键词： genetic algorithm parallel genetic algorithm Search Based Software Testing Test Data Software Under Test

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A Globally DiversifiedIsland ModelPGA for Multimodal Optimization

A Globally DiversifiedIsland ModelPGA for Multimodal Optimiz...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Zhang, Li Feng He, Rong Renmin Univ China Sch Informat Beijing 100872 Peoples R China

ISBN: (纸本)9781479914883

Multimodal optimization aims to find multipleglobal and local optima as opposed to only the best optimum. parallel genetic algorithms (PGAs) provide a natural advantage for dealing with this issue, since they are multi-population based searching methodologies. For single population based evolutionary algorithms, a number of niching and multimodal optimization techniques have been proposed and successfully applied to cope with this problem. However, these approaches are definitely not applicable for PGAs, since due to communicational and computational costs it is very always impossible to obtain and compute global information of all the sub-populations during massive parallel evolution procedure. In this study, a new island model PGA, called local competitionmodel (LCM), is developed to cope with this issue. The new method only uses local information received from a few neighbouring subpopulations to reach a global diversification in which all the subpopulations are automatically allocated to differentareas of searching space so that they canconverge to multiple optima including both global optima and local optima. Finally, experimental studies on both real number optimization and combinatorial optimization are implemented to illustrate the performance of the new PGA model.

关键词： parallel genetic algorithm Island model Niching Multimodal optimization

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An Improved parallel genetic algorithm Based on Injection Island Approach and K1 Triangulation for the Optimal Design of the Flexible Multi-body Model Vehicle Suspensions

An Improved Parallel Genetic Algorithm Based on Injection Is...

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2009 ISECS International Colloquium on Computing, Communication, Control, and Management

作者： Guangyuan Liu College of Info & Electric Engineering Hebei University of Engineering Handan,China Jingjun Zhang Ruizhen Gao Yang Sun Scientific Research Office Hebei University of Engineering Handan,China

In this paper,we introduced IPGA(Improved parallel genetic algorithm),a new parallel genetic algorithm which is based on k1 triangulation of fixed point theory and an injection island *** approach guarantees that the processors only generate solutions within their assigned *** what's more,the k1 triangulation theory is introduced to the genetic algorithm to design convergence criteria objectively and to solve the convergence problem in the later *** running environment is constituted in this paper and the optimization of multi-body model vehicle suspension is implemented through establishing the interface between ADAMS software and genetic *** results show that the parallel genetic algorithm developed in this paper is efficient.

关键词： parallel genetic algorithm island model K1 triangulation Cluster system Vehicle suspension

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