An Ant Colony Optimization Algorithm for Shop Scheduling Problems

Blum, Christian; Sampels, Michaël

doi:10.1023/b:jmma.0000038614.39977.6f

Cited by 212 publications

(93 citation statements)

References 32 publications

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“…Examples include the applications to assignment problems (Costa and Hertz, 1997;Stützle and Hoos, 2000;Socha et al, 2003), scheduling problems (Merkle et al, 2002;Gagné et al, 2002;Blum and Sampels, 2004), and vehicle routing problems Reimann et al, 2004).…”

Section: Aco Applicationsmentioning

confidence: 99%

Ant colony optimization for continuous domains

Socha

Dorigo

2008

European Journal of Operational Research

1,185

681

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Section: Aco Applicationsmentioning

confidence: 99%

Ant colony optimization for continuous domains

Socha

Dorigo

2008

European Journal of Operational Research

1,185

681

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“…The direction is then determined according to the obtained results. ACO has been used in following areas before: various subfields of data mining [16], real world problems [17], mathematical modeling [18], network routing problems [19], prediction of energy consumption [20], and software testing [21].…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

A bat-inspired algorithm for prioritizing test cases

Öztürk

2017

Vietnam J Comput Sci

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By ordering test cases, early fault detection is focused on test case prioritization. In this field, it is widely known that algorithm and coverage criteria focused works are common. Previous works, which are related to test case prioritization, showed that practitioners need a novel method that optimizes test cases according to the cost of each test case instead of regarding the total cost of a test suite. In this work, by utilizing local and global search properties of a bat algorithm, a new bat-inspired test cases prioritization algorithm (BITCP) is proposed. In order to develop BITCP, test case execution time and the number of faults were adapted to the distance from the prey and loudness, respectively. The proposed method is then compared with four methods which are commonly used in this field. According to the results of the experiment, BITCP is superior to the conventional methods. In addition, as the complexity of the code of test cases increases, the decline in average percentage of fault detection is less in BITCP than the other four comparison algorithms produced.

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“…Having given the set of neighborhood N of the given point i, amount of pheromone on the path τ and the quality of passage from point i to point j as an element of the table η you can present the probability of passage from point i to j as [6,7]:…”

Section: Adaptation Of Aco To Solve the Problems Of Schedulingmentioning

confidence: 99%