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Development of a parallel optimization method based on genetic simulated annealing algorithm

parallel COMPUTING 2005年第8-9期31卷 839-857页

作者： Wang, ZG Wong, YS Rahman, M Natl Univ Singapore Dept Mech Engn Singapore 119260 Singapore

This paper presents a parallel genetic simulated annealing (PGSA) algorithm that has been developed and applied to optimize continuous problems. In PGSA, the entire population is divided into sub-populations, and in each sub-population the algorithm uses the local search ability of simulated annealing after crossover and mutation. The best individuals of each subpopulation are migrated to neighboring ones after a certain number of epochs. An implementation of the algorithm is discussed and the performance is evaluated against a standard set of test functions. PGSA shows some remarkable improvement in comparison with the conventional parallel genetic algorithm and the breeder genetic algorithm (BGA). (c) 2005 Elsevier B.V. All rights reserved.

关键词： genetic algorithm simulated annealing parallel genetic algorithm

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parallel image encryption with bitplane decomposition and genetic algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第19期77卷 25799-25819页

作者： Mozaffari, Saeed Semnan Univ Fac Elect & Comp Engn Semnan Iran

Image encryption is an efficient technique to protect image content from unauthorized parties. In this paper a parallel image encryption method based on bitplane decomposition is proposed. The original grayscale image is converted to a set of binary images by local binary pattern (LBP) technique and bitplane decomposition (BPD) methods. Then, permutation and substitution steps are performed by genetic algorithm (GA) using crossover and mutation operations. Finally, these scrambled bitplanes are combined together to obtain encrypted image. Instead of random population selection in GA, a deterministic method with security keys is utilized to improve security level. The proposed encryption method has parallel processing capability for multiple bitplanes encryption. This distributed GA with multiple populations increases encryption speed and makes it suitable for real-time applications. Simulations and security analysis are done to demonstrate efficiency of our algorithm.

关键词： parallel image encryption Bitplain decomposition Local binary pattern Metaheuristic algorithms parallel genetic algorithm GPU programming

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parallel genetic algorithms for the solution of inverse heat conduction problems

Parallel genetic algorithms for the solution of inverse heat...

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Joint Meeting of the International Symposium on Distributed Computing and Applications to Business, Engineering and Science/International Conference on parallel algorithms and Computing Evironments

作者： Guo, Q. Shen, D. Guo, Y. Lai, C.-H. Wuhan Univ Technol Wuhan 430070 Peoples R China Xiaogan Univ Xiaogan Peoples R China Univ Greenwich Old Royal Naval Coll Sch Comp & Math Sci London SE10 9LS England

A parallel genetic algorithm ( PGA) is proposed for the solution of two-dimensional inverse heat conduction problems involving unknown thermophysical material properties. Experimental results show that the proposed PG... 详细信息

关键词： heat conduction inverse problem parallel genetic algorithm

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Two-level parallel CPU/GPU-based genetic algorithm for association rule mining

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2020年第2-3期22卷 335-345页

作者： Hamdad, Leila Ournani, Zakaria Benatchba, Karima Bendjoudi, Ahcene Ecole Natl Super Informat ESI LCSI BP 68M Oued Smar El Harrach Algeria Ecole Natl Super Informat ESI BP 68M Oued Smar El Harrach Algeria Ecole Natl Super Informat ESI LMCS BP 68M Oued Smar El Harrach Algeria CERIST Res Ctr DTISI Algiers Algeria

genetic algorithms (GA) are widely used in the literature to extract interesting association rules. However, they are time consuming mainly due to the growing size of databases. To speed up this process, we propose two parallel GAs (ARMGPU and ARM-CPU/GPU). In ARM-GPU, parallelism is used to compute the fitness which is the most time consuming task;while, ARM-CPU/GPU proposes a two-level-based parallel GA. In the first level, the different cores of the CPU execute a GAARM on a sub-population. The second level of parallelism is used to compute the fitness, in parallel, on GPU. To validate the proposed two parallel GAs, several tests were conducted to solve well-known large ARM instances. Obtained results show that our parallel algorithms outperform state-of-the-art exact algorithms (APRIORI and FP-GROWTH) and approximate algorithms (SEGPU and ME-GPU) in terms of execution time.

关键词： association rules parallel genetic algorithm GPU CPU

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parallel genetic algorithms on line topology of heterogeneous computing resources 05

Parallel genetic algorithms on line topology of heterogeneou...

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genetic and Evolutionary Computation Conference

作者： Gong, Yiyuan Nakamura, Morikazu Tamaki, Shiro Univ Ryukyus Fac Informat Engn Okinawa 9030213 Japan

ISBN: (纸本)1595930108

This paper evaluates a parallel genetic algorithm (GA) on the line topology of heterogeneous computing resources. Evolution process of parallel GAs is investigated on two types of arrangements of heterogeneous computing resources: the ascending and descending order arrangement of computing capability. Their differences in chromosome variety, migration frequency and solution quality are investigated. The results in this paper can help to design parallel GAs in grid computing environments.

关键词： parallel genetic algorithm migration interval heterogeneous computing capability grid

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parallel genetic algorithms for third generation mobile network planning

Parallel genetic algorithms for third generation mobile netw...

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4th International Conference on parallel Computing in Electrical Engineering (PARELECT 2004)

作者： Maple, C Guo, L Zhang, J Univ Luton Dept Comp & Informat Syst Luton LU1 3JU Beds England

ISBN: (纸本)0769520804

As Third Generation (3G) mobile networks start to be implemented, there is a need for effective network planning. However, deciding upon the optimum placement for the base stations of the networks is a complex task requiring vast computational resource. This paper discusses the conflicting objectives of base station planning and characterises a multi-objective optimisation problem. We present a genetic encoding of the third generation mobile network planning problem and parallel genetic algorithms to solve it.

关键词： 3G CDMA parallel genetic algorithm optimisation coverage capacity

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parallel genetic-based hybrid pattern matching algorithm for isolated word recognition

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 1998年第5期12卷 573-594页

作者： Kwong, S He, QH Man, KF Tang, KS Chau, CW City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China S China Univ China Hong Kong Peoples R China

Dynamic Time Warping (DTW) is a common technique widely used for nonlinear time normalization of different utterances in many speech recognition systems. Two major problems are usually encountered when the DTW is applied for recognizing speech utterances: (i) the normalization factors used in a warping path;and (ii) finding the K-best warping paths. Although DTW is modified to compute multiple warping paths by using the Tree-Trellis Search (TTS) algorithm, the use of actual normalization factor still remains a major problem for the DTW. In this paper, a parallel genetic Time Warping (PGTW) is proposed to solve the above said problems. A database extracted from the TIMIT speech database of 95 isolated words is set up for evaluating the performance of the PGTW. In the database, each of the first 15 words had 70 different utterances, and the remaining 80 words had only one utterance. For each of the 15 words, one utterance is arbitrarily selected as the test template for recognition. Distance measure for each test template to the utterances of the same word and to those of the 80 words is calculated with three different time warping algorithms: TTS, PGTW and Sequential genetic Time Warping (SGTW). A Normal Distribution Model based on Rabiner(23) is used to evaluate the performance of the three algorithms analytically. The analyzed results showed that the PGTW had performed better than the TTS. It also showed that the PGTW had very similar results as the SGTW, but about 30% CPU time is saved in the single processor system.

关键词： speech recognition genetic algorithm parallel genetic algorithm dynamic time warping tree-trellis search

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A Selective Migration parallel Multi-objective genetic algorithm

A Selective Migration Parallel Multi-objective Genetic Algor...

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22nd Chinese Control and Decision Conference

作者： Qiu, Tengfei Ju, Gang Southeast Univ Sch Energy & Environm Nanjing 210096 Peoples R China

ISBN: (纸本)9781424451821

A new multi-objective evolutionary algorithm, called selective migration parallel genetic algorithm (SMPGA) was presented in this paper, which designs a new migration strategy and qualification based on the adaptive grid. In SMPGA, a searching population and a elite population evolve at the same time;unique migration strategy and qualification are used to keep and improve the convergence and diversity of the Pareto optimal set. Besides, according to their different purposes, the two populations adopt different crossover strength. Simulation results show that SMPGA can find accurate and uniform Pareto optimal solutions on different multi-objective problems.

关键词： Multi-objective optimization parallel genetic algorithm Migration strategy and qualification Adaptive grid

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parallel Optimization and Performance Tuning on a Kunpeng Cluster of genetic algorithm for Synthesis of Circulant Networks

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LOBACHEVSKII JOURNAL OF MATHEMATICS 2023年第8期44卷 3130-3139页

作者： Monakhov, O. G. Monakhova, E. A. Kireev, S. E. Russian Acad Sci Inst Computat Math & Math Geophys Siberian Branch Novosibirsk 630090 Russia

parallel versions of a genetic algorithm based on the hybrid MPI-OpenMP model are implemented to optimize circulant networks, which are of practical interest in the design of supercomputer systems and systems on a chip. An analysis of the efficiency of parallel programs with different numbers of MPI processes and OpenMP threads on a cluster of Kunpeng processors has been carried out. The speed-up of several hybrid parallel computing schemes was experimentally evaluated and analyzed. Two bottlenecks in terms of efficiency in parallel execution of the algorithm are identified and methods for their solution are proposed. By means of the parallel genetic algorithm the descriptions of circulant networks with better average distance and bisection width for the known large circulant networks were obtained.

关键词： optimal circulant networks parallel genetic algorithm hybrid parallel schemes

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Implementation of Multi-core parallel Computation for Real-time Turning of PID Parameters Based on genetic algorithm

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International Conference on Advanced Manufacturing Technology and Systems (AMTS 2012)

作者： Zhang, Shuangshi Wang, Xuben Wang, WeiHua Chengdu Univ Technol Sate Key Lab Oil & Gas Geol & Exploitat Chengdu Peoples R China Chinese Peoples Armed Police Force Acad Langfang Hebei Peoples R China

ISBN: (纸本)9783037853870

The aim of the work described in this paper is to investigate the implementation of Multi-core parallel genetic algorithm (McPGA) for real-time turning of PID parameters based on it using OpenMP. The performance of our scheme is discussed in comparison with that of the Sequential genetic algorithm (SGA) program running on the same computer. Numerical experimental results clearly show that McPGA is much better than SGA on convergence, premature and optimized speed. It can be used for PID parameter real-time turning in Industry Process Control Computer (IPC).

关键词： Multi-core parallel Computation Real-time tuning PID parallel genetic algorithm

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