Random Search Algorithms

Zabinsky, Zelda B.

doi:10.1002/9780470400531.eorms0704

Cited by 86 publications

(44 citation statements)

References 74 publications

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“…It also has optimization options developed on the basis of stochastic algorithms. This research uses Random Search Algorithms (RSA) [13] to calculate energy cost in pumping stations, using equation (5):…”

Section: Methodsmentioning

confidence: 99%

Optimization of energy cost in water supply system

Zimoch

Bartkiewicz

2017

E3S Web Conf.

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Abstract. The decreasing amount of fossil fuels and deteriorating air quality forces the governments to introduce a rational energy management in all sectors of economy. At the beginning of the twenty-first century many water supply systems (WSSs) were oversized because of the reduction of water consumption, especially in industry. This resulted in high energy consumption in the pumping stations. Improving pumps operation will decrease energy consumption and also the water prices. The purpose of this paper is to present a method of energy optimization in WSSs. This paper presents an analysis of energy consumption in a selected water supply system. In this study pumps located in water treatment plants and pumping station cooperating with the tanks are analyzed. The study used hydraulic model of the WSS created in MOSKAN-W, which defines pumps' parameters such as flow, head, and efficiency. Using optimization options of calculation software several scenarios of energy costs were prepared.

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Section: Methodsmentioning

confidence: 99%

Optimization of energy cost in water supply system

Zimoch

Bartkiewicz

2017

E3S Web Conf.

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“…In solving illstructured global optimization problems with many potential stationary points, a random search ensures convergence to a global optimum in terms of probability. Essentially, if the random selection does not ignore any part of the search space, then the algorithm is guaranteed to converge with a probability one [25]. As it follows a geometric distribution, the number of expected iterations until near-optimal convergence (within distance from the optimum) is as follows:…”

Section: How To Label Training Data?mentioning

confidence: 99%

Improved Neighborhood Search for Collaborative Filtering

Chung¹,

Kim²,

Park³

et al. 2018

IJFIS

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k-Nearest Neighbor (k-NN) and other user-based collaborative filtering (CF) algorithms have gained popularity because of the simplicity of their algorithms and performance. As the performance of such algorithms largely depends on neighborhood selection, it is important to select the most suitable neighborhood for each active user. Previous user-based CF simply relies on similar users or common experts in this regard; however, because users have different tastes as well as different expectations for expert advice, similar users or common experts may not always be the best neighborhood for CF. In search of a more suitable neighborhood, so-called personalized experts develop personalized expert features. Through experimentation, we show that personalized experts are different from similar users, common experts, or similar common experts. The personalized, expert-based CF algorithm outperforms k-NN and other user-based CF algorithms.

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“…Random Search iteratively moves in the search space randomly to locate candidate solutions in the search space [48,49]. Pure (unguided) Random Search techniques usually fail to find globally optimal solutions [36].…”

Section: -Random Searchmentioning

confidence: 99%

Constructing subtle higher order mutants for Java and AspectJ programs

Omar

Ghosh

Whitley

2013

2013 IEEE 24th International Symposium on Software Reliability Engineering (ISSRE)

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CONSTRUCTING SUBTLE HIGHER ORDER MUTANTS FROM JAVA AND ASPECTJ PROGRAMSMutation testing is a fault-based testing technique that helps testers measure and improve the fault-detection effectiveness of their test suites. However, a majority of traditional First Order Mutants (FOMs), which are created by making a single syntactic change to the source code, represent trivial faults that are often easily detected (i.e. killed). Research has shown that the majority of real faults not detected during testing are complex faults that cannot be simulated with FOMs because fixing these faults requires making multiple changes to the source code. Higher Order Mutants (HOMs), which are created by making multiple syntactic changes to the source code, can be used to simulate such faults.The majority of HOMs of a given program are killed by any test suite that kills all the FOMs.We refer to HOMs that are not killed as subtle HOMs. They represent cases where single faults interact by masking each other with respect to the given test suite and produce complex faulty behavior that cannot be simulated with FOMs. The fault-detection effectiveness of the given test suite can be improved by adding test cases that detect the faults denoted by subtle HOMs.Because subtle HOMs are rare in the exponentially large space of candidate HOMs, the cost of finding them can be high even for small programs. A brute force approach that evaluates every HOM in the search space by constructing, compiling, and executing the HOM against the given test suite is unrealistic.We developed a set of search techniques for finding subtle HOMs in the context of Java and AspectJ programming languages. We chose Java because of its popularity, and the availability of experimental tools and open source programs. We chose AspectJ because of its unique concepts and constructs and their consequent testing challenges.We developed four search-based software engineering techniques: (1) Genetic Algorithm, (2) Local Search, (3) Test-Case Guided Local Search, (4) Data-Interaction Guided Local Search. We also developed a Restricted Random Search technique and a Restricted Enumeration Search technique. Each search technique explores the search space in a different way and that affectsii the type of subtle HOMs that can be found by each technique. Each of the guided local search techniques uses a heuristic to improve the ability of Local Search to find subtle HOMs.Due to the unavailability of higher order mutation testing tools for AspectJ and Java programs, we developed HOMAJ, a Higher Order Mutation Testing tool for AspectJ and Java programs for finding subtle HOMs. HOMAJ implements the developed search techniques and automates the process of creating, compiling, and executing both FOMs and HOMs.The results of our empirical studies show that all of the search techniques were able to find subtle HOMs. However, Local Search and both the Guided Local Search techniques were more effective than the other techniques in terms of their ability to find subtle HOMs.The search tec...

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Random Search Algorithms

Cited by 86 publications

References 74 publications

Optimization of energy cost in water supply system

Optimization of energy cost in water supply system

Improved Neighborhood Search for Collaborative Filtering

Constructing subtle higher order mutants for Java and AspectJ programs

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