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A Hybrid of Modified Simplex and Steepest Ascent Methods with Signal to Noise Ratio for Optimal Parameter Settings of ACO.

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Title: A Hybrid of Modified Simplex and Steepest Ascent Methods with Signal to Noise Ratio for Optimal Parameter Settings of ACO.
Authors: Ratanaphanyarat, J.1 pongch@engr.tu.ac.th, Chunothaisawat, S.1, Rungruengsattaya, N.1, Tunsiriroongruang, S.1, Sriwannusorn, K.1, Luangpaiboon, P.2
Source: International MultiConference of Engineers & Computer Scientists 2009. 2009, p2019-2025. 7p. 1 Diagram, 5 Charts, 8 Graphs.
Subjects: Heuristic programming, Sequential processing (Computer science), Regression analysis, Signal-to-noise ratio, Algorithms
Abstract: Metaheuristics are sequential processes that perform exploration and exploitation in the solution space aiming to efficiently find near optimal solutions with natural intelligence as a source of inspiration. One of the most well-known metaheuristics is called Ant Colony Optimisation, ACO. This paper is conducted to give an aid in complicatedness of using ACO in terms of its parameters: number of iterations, ants and moves. Proper levels of these parameters are analysed on eight noisy continuous non-linear continuous response surfaces. Considering the solution space in a specified region, some surfaces contain global optimum and multiple local optimums and some are with a curved ridge. ACO parameters are determined through Modified Simplex, MSM and Steepest Ascent methods, SAM, including their hybridisation. SAM was introduced to enhance a performance of MSM via the statistically significant regression analysis and Taguchi's signal to noise, S/N, ratio to recommend preferable levels of parameters. A series of computational experiments using each algorithm were conducted. Experimental results were analysed in terms of design points, best so far solutions, mean and standard deviation including S/N ratio. It was found that the results obtained from hybridisation were better than those using single algorithm itself. However, the average execution time of experimental run and number of design points using hybridisation were longer than those using a single method. Finally they stated a recommendation of proper level settings of ACO parameters for all eight functions that can be used as a guideline for future applications of ACO. This is to promote ease of use of ACO in real life problems. [ABSTRACT FROM AUTHOR]
Copyright of International MultiConference of Engineers & Computer Scientists 2009 is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A Hybrid of Modified Simplex and Steepest Ascent Methods with Signal to Noise Ratio for Optimal Parameter Settings of ACO.
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  Data: Metaheuristics are sequential processes that perform exploration and exploitation in the solution space aiming to efficiently find near optimal solutions with natural intelligence as a source of inspiration. One of the most well-known metaheuristics is called Ant Colony Optimisation, ACO. This paper is conducted to give an aid in complicatedness of using ACO in terms of its parameters: number of iterations, ants and moves. Proper levels of these parameters are analysed on eight noisy continuous non-linear continuous response surfaces. Considering the solution space in a specified region, some surfaces contain global optimum and multiple local optimums and some are with a curved ridge. ACO parameters are determined through Modified Simplex, MSM and Steepest Ascent methods, SAM, including their hybridisation. SAM was introduced to enhance a performance of MSM via the statistically significant regression analysis and Taguchi's signal to noise, S/N, ratio to recommend preferable levels of parameters. A series of computational experiments using each algorithm were conducted. Experimental results were analysed in terms of design points, best so far solutions, mean and standard deviation including S/N ratio. It was found that the results obtained from hybridisation were better than those using single algorithm itself. However, the average execution time of experimental run and number of design points using hybridisation were longer than those using a single method. Finally they stated a recommendation of proper level settings of ACO parameters for all eight functions that can be used as a guideline for future applications of ACO. This is to promote ease of use of ACO in real life problems. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International MultiConference of Engineers & Computer Scientists 2009 is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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        PageCount: 7
        StartPage: 2019
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      – SubjectFull: Heuristic programming
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      – SubjectFull: Sequential processing (Computer science)
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      – SubjectFull: Regression analysis
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      – SubjectFull: Signal-to-noise ratio
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      – SubjectFull: Algorithms
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      – TitleFull: A Hybrid of Modified Simplex and Steepest Ascent Methods with Signal to Noise Ratio for Optimal Parameter Settings of ACO.
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              Text: 2009
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