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检索条件"主题词=Distributed genetic algorithm"

共 54 条记录，以下是41-50 订阅

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genetic algorithm with species and sexual selection

Genetic algorithm with species and sexual selection

引用

IEEE Conference on Cybernetics and Intelligent Systems

作者： Raghuwanshi, M. M. Kakde, O. G. RCERT Chandrapur 442403 MS India VNIT Nagpur Maharashtra India

ISBN: (纸本)9781424400225

In this paper we have proposed new real coded genetic algorithm with species and sexual selection (GAS3). GAS3 is a distributed quasi steady-state real-coded genetic algorithm. GAS3 uses sex determination method (SDM) to determine the sex (male or female) of members in population. Each female member is considered as a niche in population and the species formation take place around these niches. Sexual selection strategy selects female and required number of male members from species to perform the recombination operation. The parent-centric recombination operators are used to generate offspring. If species is not performing well, then the merging of species to the nearby species takes place. Performance of GAS3 is tested on unimodal and multi-modal test functions. It got success in solving wide range of problems. Its performance is also compared with other real-coded genetic algorithm and evolutionary strategies.)

关键词： real-coded genetic algorithms parent-centric crossover operators chromosome differentiation Clustering sexual selection scheme distributed genetic algorithm niche and species

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Displacement maximisation of haunched-rafter Pitched-roof Steel Portal Frames

Displacement maximisation of haunched-rafter Pitched-roof St...

引用

11th International Conference on Computer Aided Optimum Design of Structures

作者： Issa, H. K. Mohammad, F. A. Nottingham Trent Univ Sch Architecture Design & Built Environm Nottingham England

ISBN: (纸本)9781845641856

Pitched-roof Steel Portal Frames (SPFs) are common structures used in single storey buildings. It is necessary for this popular steelwork to pass through an optimisation process to minimise the total cost of the frame. Optimisation in terms of weight is well documented with different optimisation techniques. However, this approach to displacement maximisation is somehow rare. In this paper, an attempt is made to perform optimisation in terms of lateral displacements. To achieve this, a modified distributed genetic algorithm (DGA) is used to maximise lateral displacements of the SPF while the constraints meet the requirements of BS 5950. Furthermore, software 'DO-DGA' (Design Optimisation using DGA), coded by Visual Basic 6.0, has been developed by the authors to loop the optimisation process. Although the appearance and form of SPFs are simple, according to BS 5950, there are more limitations to be checked than in complex structures. A stiffness matrix has been derived for the haunched part of the rafter using a column analogy and a virtual work method to involve this part in the analysis process. Through two benchmark examples, the comparison is made between the results of weight minimisation and displacement maximisation.

关键词： distributed genetic algorithm steel portal frame optimisation displacement maximisation

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Assembling DNA fragments with parallel algorithms

Assembling DNA fragments with parallel algorithms

引用

IEEE Congress on Evolutionary Computation

作者： Alba, E Luque, G Khuri, S ETSI Informat Dpto Languajes & Ciencias Computac Malaga 29071 Spain

ISBN: (纸本)0780393635

As more research centers embark on sequencing new genomes, the problem of DNA fragment assembly for shotgun sequencing is growing in importance and complexity. Accurate and fast assembly is a crucial part of any sequencing project and since the DNA fragment assembly problem is NP-hard, exact solutions are very difficult to obtain. Various heuristics, including genetic algorithms, were designed for solving the fragment assembly problem. While the sequential genetic algorithm has given good results, it is unable to sequence very large DNA molecules. In this work, we present two parallel methods, a distributed genetic algorithm and a parallel simulated annealing, to solve problem instances that are 77K base pairs long accurately.

关键词： DNA biology computing genetic algorithms parallel algorithms simulated annealing DNA fragment assembly problem DNA molecules NP-hard problem distributed genetic algorithm genomes parallel methods parallel simulated annealing shotgun s

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An investigation into bay allocation model and algorithm for outbound containers

An investigation into bay allocation model and algorithm for...

引用

International Symposium on Knowledge Acquisition and Modeling

作者： Xie, Chen Yan, Wei Shanghai Maritime Univ Sch Logist Engn Shanghai 200135 Peoples R China

ISBN: (纸本)9780769534886

A bay allocation model is developed for outbound containers at container terminal based on objective programming, which aims at the problem of stack bay allocation for outbound containers. The model's objective function is subject to the minimization of the moving distance of the yard crane when bringing outbound containers into yard and loading ships. To resolve the model, a hybrid algorithm which employs heuristic rule and distributed genetic algorithm, is then employed. Finally, a case simulation study has been used for system illustration, and then verifies the validity and usefulness of the model and the algorithm.

关键词： bay allocation container terminal distributed genetic algorithm heuristic rule objective programming outbound containers

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A distributed Model for Manufacturing Scheduling: Approaching the EDGE

A Distributed Model for Manufacturing Scheduling: Approachin...

引用

International-Federation-of-Information-Processing-Working-Group-5.7 (IFIP WG 5.7) International Conference on Advances in Production Management Systems (APMS)

作者： Coelho, Pedro Silva, Cristovao Univ Coimbra Dept Mech Engn CEMMPRE P-3030788 Coimbra Portugal

ISBN: (纸本)9783030858742;9783030858735

Manufacturing scheduling has a crucial role in a company's performance. It's a hard optimization problem and due to the latest manufacturing trends, it is becoming even more complex. Metaheuristics are promising methods to solve those real-world problems. The latest distributed/parallel computing advances may support the increase of computational power needed to get efficient schedules a suitable time period. In the last years, the Industrial Internet has also known some advances as the emergence of the Edge computing paradigm that increased the computational processing power near the factory floor. This work presents strategies to implement a distributed metaheuristic for manufacturing scheduling on the Edge. Under the scheduling problem context, the physical platform and the programming environment are examined. Based on an evolutionary metaheuristic (genetic algorithm), a model is developed, following strategies that take advantage of the Edge layer of the Industrial Internet. The generic algorithm steps are described for future deployment and validation.

关键词： Manufacturing Scheduling Industrial Internet Edge computing Metaheuristics distributed genetic algorithm

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Behavior learning and evolution of swarm robot system using SVM

Behavior learning and evolution of swarm robot system using ...

引用

International Conference on Control, Automation and Systems

作者： Seo, Sang-Wook Ko, Kwang-Eun Yang, Hyun-Chang Sim, Kwee-Bo Chung Ang Univ Dept Elect & Elect Engn Seoul 156756 South Korea

ISBN: (纸本)9788995003879

In swarm robot systems, each robot must behaves by itself according to the its states and environments, and if necessary, must cooperates with other robots in order to carry, out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, reinforcement learning method with SVM based on structural risk minimization and distributed genetic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. By distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the performance of evolution, selective crossover using the characteristic of reinforcement learning that basis of SVM is adopted in this paper.

关键词： behavior learning evolution swarm robot SVM distributed genetic algorithm

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Performance Evaluation of WMN-PSODGA System for Node Placement Problem in WMNs Considering Four Different Crossover Methods 32

Performance Evaluation of WMN-PSODGA System for Node Placeme...

引用

32nd IEEE International Conference on Advanced Information Networking and Applications (AINA)

作者： Barolli, Admir Sakamoto, Shinji Barolli, Leonard Takizawa, Makoto Aleksander Moisiu Univ Durres Dept Informat Technol L 1Rruga Currilave Durres Albania FIT Grad Sch Engn Higashi Ku 3-30-1 Wajiro Higashi Fukuoka Fukuoka 8110295 Japan FIT Dept Informat & Commun Engn Higashi Ku 3-30-1 Wajiro Higashi Fukuoka Fukuoka 8110295 Japan Hosei Univ Dept Adv Sci Fac Sci & Engn Kajino Machi Koganei Tokyo 1848584 Japan

ISBN: (纸本)9781538621950

Wireless Mesh Networks (WMNs) have many advantages such as low cost and increased high speed wireless Internet connectivity, therefore WMNs are becoming an important networking infrastructure. In our previous work, we implemented a Particle Swarm Optimization (PSO) based simulation system, called WMN-PSO, and a simulation system based on genetic algorithm (GA), called WMN-GA, for solving node placement problem in WMNs. In this paper, we implement a hybrid simulation system based on PSO and distributed GA (DGA), called WMN-PSODGA. We evaluate WMN-PSODGA system by computer simulations. The simulation results show that the WMN-PSODGA system has a good performance for SPX crossover method compared with other crossover methods.

关键词： Wireless Mesh Networks Particle Swarm Optimization distributed genetic algorithm Hybrid Intelligent algorithm Crossover Methods

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Spreading Factor Allocation for Extension of End Device Lifetime in LoRaWAN 9

Spreading Factor Allocation for Extension of End Device Life...

引用

9th IEEE World Forum on the Internet of Things (WF-IoT) - The Blue Planet - A Marriage of Sea and Space

作者： Narieda, Shusuke Fujii, Takeo Mie Univ Grad Sch Eng Dept Inform Eng Tsu Mie Japan Univ Elect Commun Adv Wireless Commun Res Ctr Chofu Tokyo Japan

ISBN: (纸本)9798350311617;9798350311624

In this study, we investigate a spreading factor allocation method for extending the end device (ED) lifetime in LoRaWAN. Long range (LoRa) is a communication standard that has attracted attention because it can facilitate the construction of a local or private network. The authors have defined a constrained optimization problem for spreading factor allocation in LoRaWAN with the average power consumption of EDs as the constraint, and have shown the solving method of the problem using a distributed genetic algorithm (GA). This study extends the approach by defining an optimization problem for power balancing by rotating the allocated spreading factor to reduce the increase in power consumption owing to the continuous use of high spreading factor by arbitrary EDs, and the solving method of the problem using a distributed GA is developed. The effectiveness of the studied method is demonstrated by computer experiments.

关键词： LPWA LoRaWAN constrained optimization problem distributed genetic algorithm

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distributed Nonconvex Economic Dispatch algorithm for Large-scale Power System 15

Distributed Nonconvex Economic Dispatch Algorithm for Large-...

引用

15th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Xu, Bowen Guo, Fanghong Zhang, Wen-An Chen, Jiawei Wen, Changyun Zhejiang Univ Technol Dept Automat Hangzhou Peoples R China Chongqing Univ Sch Automat Chongqing Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9781728151694

This paper proposes a distributed genetic algorithm to solve non-convex optimal power allocation problem in large-scale power system. Such problem is usually solved by some heuristic search methods such as genetic algorithm (GA). It is noted that traditional genetic algorithms are usually centralized, and their genetic operators are directly operated on the entire combined solution by a single control center. When considered system scales up, its computational cost would be tremendously high. In order to overcome this problem, in this paper we aim to revise the GA algorithm to be implemented in a fully distributed way. The main idea of the proposed distributed GA is to treat the power output solution of a single distributed generator as an individual, and each local agent has a separate population and performs genetic operations independently. The fitness information used for the crossover comes from the average consensus of the local fitness. The power outputs of the entire generator set is considered as the combined individual, which are found to converge near the optimal solution in the formulation experiment. At the end of the paper, a case study on 10 generator units shows that the distributed genetic algorithm is capable to solve nonconvex economic dispatch (ED) problem while perfectly fitting the distributed framework in most scenario.

关键词： distributed genetic algorithm Non-convex Optimization Problem Average Consensus Economic Dispatch Power System

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A distributed algorithm for Balanced Multi-Robot Task Allocation 11

A Distributed Algorithm for Balanced Multi-Robot Task Alloca...

引用

11th International Conference on Industrial and Information Systems (ICIIS)

作者： Gautam, Avinash Thakur, Amitayush Dhanania, Garima Mohan, Sudeept Birla Inst Technol & Sci Pilani Rajasthan India

ISBN: (纸本)9781509038183

In this paper the problem of static multi-robot task allocation is addressed. It is concerned with the distribution of static tasks in an environment to robots such that the robots complete the tasks in an optimal fashion. The cost of completing a task is proportional to the distance travelled by a robot to visit that task. This problem is of particular importance in multi-robot systems because finding an optimal solution is NP-hard. Earlier work has paid less attention towards load balanced task allocation. In this paper, a completely distributed algorithm is proposed. A travelling salesman tour (TST) considering all task locations is computed using distributed genetic algorithm. The TST is partitioned into fragments that are distributed amongst the robots using a novel auction algorithm. The proposed algorithm is compared with a state of the art algorithm in simulation. The results thus obtained substantiate the fact that the proposed algorithm shows improved performance in terms of load balanced distribution of tasks to the individual robots in multi-robot system.

关键词： Multi-Robot Systems Travelling Salesman Problem distributed genetic algorithm Task Partitioning Load balancing Task Allocation

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