A parallel genetic simulated annealing hybrid algorithm for task scheduling

Shu, Wanneng; Zheng, Shijue

doi:10.1007/bf02829270

Cited by 8 publications

(7 citation statements)

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“…Because the multireading parallel method can improve the stability and efficiency of search algorithms [31,[34][35][36][37][38][39][40], the parallel optimization method for picking path of A. bisporus is proposed. e parallel optimization method at high temperature runs multiple I-SAs at the same time.…”

Section: Parallel Optimization Methods For Picking Path Of a Bisporusmentioning

confidence: 99%

“…In general, T-VFSA increases the stability of search algorithm and improves the operation speed to a certain extent, but if more stable path solutions are needed, the traditional Markov chain temperature drop method is still the best choice. standard database TSPLIB [31,41]. Because the I-SA search algorithm is better than traditional SA and GA, so I-SA search algorithm is compared with GSAACS [42] and Mahi scholar's search algorithm [43].…”

Section: Comparison Of Temperature Dropmentioning

confidence: 99%

“…In order to improve the efficiency and stability of the search algorithm, parallel search algorithms are proposed and applied in various scenarios [13,30,31], and remarkable results are obtained at the same time. Gehring and Bortfeldt propose an improved parallel genetic algorithm (PGA) [32] to solve the container loading problem.…”

Section: Introductionmentioning

confidence: 99%

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Picking Path Optimization of Agaricus bisporus Picking Robot

Zhaoren

2019

Mathematical Problems in Engineering

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The design and application of the mushroom picking robot will greatly reduce the labor cost, and it has become one of the research hotspots in the world. Therefore, we independently developed an A. bisporus (a kind of mushroom) picking robot and introduced its functional principle in this paper. At the same time, in order to improve the picking efficiency of the picking robot, a picking path optimization algorithm based on TSP model is proposed. Firstly, based on the TSP model, a picking route model for A. bisporus was established to determine the storage location of each A. bisporus. Then, an improved simulated annealing (I-SA) search algorithm is proposed to find the optimal path sequence. By improving the path initialization module, path generation module, and temperature drop module, the I-SA search algorithm can optimize the picking path in a short time. Finally, in order to improve the stability and reduce the running time of the I-SA search algorithm, a parallel optimization method for global search (“rough exploration”) and local search (“precision exploration”) is proposed. Through simulation experiments, the I-SA search algorithm can search stable and excellent path solution in a relatively short time. Through field experiments on mushroom base, the efficiency of picking A. bisporus can be improved by 14% to 18%, which verifies the effectiveness of the I-SA search algorithm.

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Section: Parallel Optimization Methods For Picking Path Of a Bisporusmentioning

confidence: 99%

Section: Comparison Of Temperature Dropmentioning

confidence: 99%

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Picking Path Optimization of Agaricus bisporus Picking Robot

Zhaoren

2019

Mathematical Problems in Engineering

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“…The problem becomes more complicated due to non deterministic nature of task application model and heterogeneous resource environments. A plethora of heuristics such as clustering algorithms (Palis et al 1996, Topcuoglu et al 2002, list scheduling algorithms (Augonnet et al 2011, Topcuoglu et al 1999, task duplication based algorithms (Hagras & Janeèek 2005, Park & Choe, 2001, Ranaweera & Agrawal, 2000, genetic algorithms (Oh&Wu, 2004, Ulusoy 2004, simulated annealing (Braun et al 2001, Kazem et al 2008, Wanneng & Shijue 2006, tabu search (Porto et al 2000, Porto & Ribeiro 1995 and particle swam optimization (Jarboui et al 2008, Salman et al 2002 have been proposed in literature for the optimal solution of scheduling problem. Static (Shirazi et al 1990) as well as dynamic scheduling (Page & Naughton 2005, Rotithor 1994 schemes are generally employed for the optimal solution.…”

Section: Introductionmentioning

confidence: 99%

A Survey on ANN Based Task Scheduling Strategies in Heterogeneous Distributed Computing Systems

Hussain

Azam

Sharif

et al. 2016

Nepal Journal of Science and Technology

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Heterogeneous Distributed Computing Systems (HeDCS) efficiently utilize the heterogeneity of diverse computational resources which are interlinked through high speed networks for executing a group of computing intensive applications. Directed acyclic graphs (DAGs) are usually used to represent these parallel applications with varied computational requirements and constraints. The optimal scheduling of the given set of precedence constrained tasks to available resources is a core concern in HeDCS and is known to be NP Complete problem. Non deterministic nature of application programs and heterogeneous environment are the main challenges in designing, implementing and analyzing phases of task scheduling techniques. A myriad of heuristic and meta-heuristic approaches have been proposed in the literature to solve this complex problem. The basic purpose of this study is to cover ANN based task scheduling strategies in the distributed computing environment perspective. Further existing scheduling heuristics could be classified in a new state of art classification including the description of frequently used parameters in the mentioned scheduling strategies. The flexible and powerful nature of ANN for identifying the data patterns, underlying time and other constraints and learning capabilities have shown to be a promising candidate among other heuristics.Nepal Journal of Science and Technology Vol. 16, No.1 (2015) pp. 69-78

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“…GSAA is an optimal algorithm combining GA with SA. GA is weak in local search but powerful in global search while SA is weak in global search but powerful in local search [7][8][9][10] .…”

Section: Introductionmentioning

confidence: 99%

Optimization of web search engine and its application to web mining

Chen

Zou

Bian

2009

Wuhan Univ. J. Nat. Sci.

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With the explosive growth of information sources available on the World Wide Web, how to combine the results of multiple search engines has become a valuable problem. In this paper, a search strategy based on genetic simulated annealing for search engines in Web mining is proposed. According to the proposed strategy, there exists some important relationship among Web statistical studies, search engines and optimization techniques. We have proven experimentally the relevance of our approach to the presented queries by comparing the qualities of output pages with those of the original downloaded pages, as the number of iterations increases better results are obtained with reasonable execution time.

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A parallel genetic simulated annealing hybrid algorithm for task scheduling

Cited by 8 publications

References 1 publication

Picking Path Optimization of Agaricus bisporus Picking Robot

Picking Path Optimization of Agaricus bisporus Picking Robot

A Survey on ANN Based Task Scheduling Strategies in Heterogeneous Distributed Computing Systems

Optimization of web search engine and its application to web mining

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