A critical review of discrete filled function methods in solving nonlinear discrete optimization problems

Woon, Siew Fang; Rehbock, Volker

doi:10.1016/j.amc.2010.05.009

Cited by 17 publications

(8 citation statements)

References 36 publications

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“…Prior discrete filled function methods are reviewed in [26], and we know from the comparison that the Algorithm of [7] is the most efficient method, so we will compare our results with that of [7] and present the comparison in Table 2. Table 1 Result (1, · · · , 1) 0 Table 1 continued …”

Section: An Algorithm and Numerical Experimentsmentioning

confidence: 93%

Discrete Global Optimization Problems with a Modified Discrete Filled Function

Yue

Gao

2015

J. Oper. Res. Soc. China

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This paper considers discrete global optimization problems. The traditional definition of the discrete filled function is modified in this paper. Based on the modified definition, a new discrete filled function is presented and an algorithm for discrete global optimization is developed from the discrete filled function. Numerical experiments reported in this paper on several test problems with up to 200 variables have demonstrated the efficiency of the algorithm.

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Section: An Algorithm and Numerical Experimentsmentioning

confidence: 93%

Discrete Global Optimization Problems with a Modified Discrete Filled Function

Yue

Gao

2015

J. Oper. Res. Soc. China

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“…The better points are located on the filled function's basin location. To find the basin region is difficult with conventional methods, and if the step distance is not very small, the area can't be detected by passing [39]. Also, this approach costs lots of time.…”

Section: Filled Function Methodsmentioning

confidence: 99%

Filled Fonksiyonunda Yeni Bir Stokastik Arama Yöntemi

Pençe

Çeşmeli

2020

El-Cezeri Fen Ve Mühendislik Dergisi

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Deterministic methods are used for optimum solution of many engineering and scientific problems. Filled function method, which is a deterministic method, is not trapped to local minimums with its ability to bypass energy barriers. In order to achieve this, the basin regions of the filled function should be located, and the filled function should be constructed in that region. However, classical search strategies used for finding the basin regions don't yield effective results. In this study, a new stochastic search approach is presented as a faster and more efficient alternative to classic filled function search strategy. An unconstrained global optimization method based on clustering and parabolic approximation (GOBC-PA) has been used as a stochastic method for accelerating the L type filled function as a deterministic method. The developed method has been tested against classical filled function using 11 benchmark functions. When the obtained results are examined, it is seen that the stochastic search approach has superiority over the mean error, standard deviation and elapsed time values according to the classical approach. These results show that the combination of deterministic and stochastic methods can be more successful in finding the global minimum against the classic deterministic method.

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“…The path planning problem has been studied actively since the 1970s [27]. These algorithms can be divided into two main categories: accurate methods and heuristic methods [28,29]. Accurate methods find the globally optimal solution in a limited time and also provides the assurance of its optimality [30].…”

Section: Applicationmentioning

confidence: 99%

Efficient Chaotic Imperialist Competitive Algorithm with Dropout Strategy for Global Optimization

et al. 2020

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The imperialist competitive algorithm combined with chaos theory (CICA) demonstrates excellent performance in global optimization problems. However, its computational complexity increases with the introduction of chaotic maps. To address this, we integrate CICA with a dropout strategy that randomly samples the dimensions of each solution at each iteration of the computation. We investigate the potential of the proposed algorithm with different chaotic maps through six symmetric and six asymmetric benchmark functions. We also apply the proposed algorithm to AUVs’ path planning application showing its performance and effectiveness in solving real problems. The simulation results show that the proposed algorithm not only has low computational complexity but also enhances local search capability near the globally optimal solution with an insignificant loss in the success rate.

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A critical review of discrete filled function methods in solving nonlinear discrete optimization problems

Cited by 17 publications

References 36 publications

Discrete Global Optimization Problems with a Modified Discrete Filled Function

Discrete Global Optimization Problems with a Modified Discrete Filled Function

Filled Fonksiyonunda Yeni Bir Stokastik Arama Yöntemi

Efficient Chaotic Imperialist Competitive Algorithm with Dropout Strategy for Global Optimization

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