Metaheuristic research: a comprehensive survey

Hussain, Kashif; Salleh, Mohd Najib Mohd; Cheng, Shi; Shi, Yuhui

doi:10.1007/s10462-017-9605-z

Cited by 628 publications

(286 citation statements)

References 153 publications

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“…Biologists have invested a great effort to better understand the mechanisms that govern the behaviour of social insects at the individual level and that allow the emergence of complex behaviours at the colony level. On the other hand, computer scientists and engineers have long used the collective behaviour of social insects as inspiration to design algorithms to solve complex problems and currently focus on the use of different biological metaphors as inspiration and the improvement of current approaches to solve several real-world problems (Bonabeau et al, 2000;De Castro, 2007;Hussain et al, 2018). This must be a two-way street, where both biology and computer science benefit from each other.…”

Section: Resultsmentioning

confidence: 99%

The Role of Information Acquisition and Processing in Decision‐Making by Individual within Insects Colonies

et al. 2018

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Insects usually present simple behaviours, but their information processing abilities result in complex collective behaviours, allowing them to perform task allocation and solve difficult problems. Biologists have invested efforts to better understand the mechanisms that govern the behaviour of social insects at the individual level and that allow the emergence of complex behaviours at the colony level. Based on biological researches, we identify the main mechanisms used to acquire different types of information and how this information is processed and used in decision‐making. We present Information Acquisition as an essential stage for Information Processing, focusing on external and internal information sources and exploring examples of information processing performed by insects. A better understanding of information processing and collective behavior in nature is the basis for the understanding of how computing is realized in insect societies, as well for new insights to develop more effective computational approaches inspired by social insects. © 2018 John Wiley & Sons, Ltd.

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Section: Resultsmentioning

confidence: 99%

The Role of Information Acquisition and Processing in Decision‐Making by Individual within Insects Colonies

et al. 2018

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“…These iterative optimization algorithms are usually inspired by natural phenomena. They usually imitate natural patterns including biology, swarm intelligence, or physical processes to search the space of solutions . Evolutionary algorithms (EAs) imitates the continuous evolutionary process in genetics to improve the solutions over successive generations, which are the most popular and well‐known category of metaheuristics, for instance, evolutionary strategies that consider the mutation and selection as the searching operators to imitate the evolutionary process and also the GAs that are inspired by the Darwin's “natural selection” theory.…”

Section: Optimization Methodsmentioning

confidence: 99%

“…They usually imitate natural patterns including biology, swarm intelligence, or physical processes to search the space of solutions. [47][48][49][50][51] Evolutionary algorithms (EAs) imitates the continuous evolutionary process in genetics to improve the solutions over successive generations, which are the most popular and well-known category of metaheuristics, for instance, evolutionary strategies that consider the mutation and selection as the searching operators to imitate the evolutionary process and also the GAs that are inspired by the Darwin's "natural selection" theory. Moreover, other variants of EAs are also developed that are based on similar concepts, for example, differential evolutionary algorithm that utilizes differences of randomly sampled pairs of solutions in the population or QEA that simulates concepts from quantum computing.…”

Section: Optimization Methodsmentioning

confidence: 99%

An efficient two-stage method for optimal sensor placement using graph-theoretical partitioning and evolutionary algorithms

Kaveh

Eslamlou

2019

Struct Control Health Monit

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Summary This paper presents a two‐stage optimal sensor placement method for modal identification of structures. At the first stage, using a graph theoretical technique, the structure is partitioned into equal substructures. At the second stage, a preset number of triaxial sensors are proportionately allocated to the substructures. The location of sensors is determined using an evolutionary optimization algorithm, which optimizes the triaxial modal assurance criterion of the structure. The first stage leads to the even distribution of the sensors. This stage not only improves the mode shape visualization as the secondary criterion but also accelerates the optimization process by space reduction. Here, various graph‐theoretical methods including k‐means, k‐means++, and spectral partitioning are examined as the partitioning techniques. In addition, a dynamic version for quantum‐inspired evolutionary optimization algorithm (DQEA) is proposed and applied to find the placement of triaxial sensors, along with the standard version of quantum‐inspired evolutionary algorithm and genetic algorithm. In order to examine the efficiency of the methods, the bridge model of the University of Central Florida, USA, is considered as the benchmark structure. The results show that the proposed method efficiently satisfies both criteria. Moreover, the introduced optimization algorithm (DQEA) outperforms other algorithms.

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“…Recently, the adoption of any Meta-Heuristic Search Techniques (or MHST) in software testing researches has been demonstrated as one of the alternatives to effectively derive and generate an adequate and optimal test data for testing propose, and particularly to resolve issues dealing with combinatorial problems. Metaheuristics are commonly used to resolve optimization problems through the method of examining optimum solutions to a specific problem of importance (15). Moreover, the utilization of MHST would reason a better and promising solution because of the most optimum set of test data can guarantee the testing procedure can be executed effectively.…”

Section: Introductionmentioning

confidence: 99%

An Analysis on the Applicability of Meta-Heuristic Searching Techniques for Automated Test Data Generation in Automatic Programming Assessment

MUSA¹

2019

Baghdad Sci.J

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Automatic Programming Assessment (APA) has been gaining lots of attention among researchers mainly to support automated grading and marking of students' programming assignments or exercises systematically. APA is commonly identified as a method that can enhance accuracy, efficiency and consistency as well as providing instant feedback on students' programming solutions. In achieving APA, test data generation process is very important so as to perform a dynamic testing on students' assignment. In software testing field, many researches that focus on test data generation have demonstrated the successful of adoption of Meta-Heuristic Search Techniques (MHST) so as to enhance the procedure of deriving adequate test data for efficient testing. Nonetheless, thus far the researches on APA have not yet usefully exploited the techniques accordingly to include a better quality program testing coverage. Therefore, this study has conducted a comparative evaluation to identify any applicable MHST to support efficient Automated Test Data Generation (ATDG) in executing a dynamic-functional testing in APA. Several recent MHST are included in the comparative evaluation combining both the local and global search algorithms ranging from the year of 2000 until 2018. Result of this study suggests that the hybridization of Cuckoo Search with Tabu Search and lévy flight as one of promising MHST to be applied, as it's outperforms other MHST with regards to number of iterations and range of inputs.

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Metaheuristic research: a comprehensive survey

Abstract: His interests are metaheuristics, optimization, fuzzy neural networks.

Cited by 628 publications

References 153 publications

The Role of Information Acquisition and Processing in Decision‐Making by Individual within Insects Colonies

The Role of Information Acquisition and Processing in Decision‐Making by Individual within Insects Colonies

An efficient two-stage method for optimal sensor placement using graph-theoretical partitioning and evolutionary algorithms

An Analysis on the Applicability of Meta-Heuristic Searching Techniques for Automated Test Data Generation in Automatic Programming Assessment

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