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JournalISSN: 0941-0643

Neural Computing and Applications

About: Neural Computing and Applications is an academic journal. The journal publishes majorly in the area(s): Artificial neural network & Fuzzy logic. It has an ISSN identifier of 0941-0643. Over the lifetime, 6855 publication(s) have been published receiving 111203 citation(s). more


Journal ArticleDOI: 10.1007/S00521-015-1920-1
Seyedali Mirjalili1Institutions (1)
Abstract: A novel swarm intelligence optimization technique is proposed called dragonfly algorithm (DA). The main inspiration of the DA algorithm originates from the static and dynamic swarming behaviours of dragonflies in nature. Two essential phases of optimization, exploration and exploitation, are designed by modelling the social interaction of dragonflies in navigating, searching for foods, and avoiding enemies when swarming dynamically or statistically. The paper also considers the proposal of binary and multi-objective versions of DA called binary DA (BDA) and multi-objective DA (MODA), respectively. The proposed algorithms are benchmarked by several mathematical test functions and one real case study qualitatively and quantitatively. The results of DA and BDA prove that the proposed algorithms are able to improve the initial random population for a given problem, converge towards the global optimum, and provide very competitive results compared to other well-known algorithms in the literature. The results of MODA also show that this algorithm tends to find very accurate approximations of Pareto optimal solutions with high uniform distribution for multi-objective problems. The set of designs obtained for the submarine propeller design problem demonstrate the merits of MODA in solving challenging real problems with unknown true Pareto optimal front as well. Note that the source codes of the DA, BDA, and MODA algorithms are publicly available at more

Topics: Metaheuristic (57%), Multi-objective optimization (56%), Multi-swarm optimization (55%) more

1,187 Citations

Journal ArticleDOI: 10.1007/S00521-015-1870-7
Abstract: This paper proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search, respectively. The MVO algorithm is first benchmarked on 19 challenging test problems. It is then applied to five real engineering problems to further confirm its performance. To validate the results, MVO is compared with four well-known algorithms: Grey Wolf Optimizer, Particle Swarm Optimization, Genetic Algorithm, and Gravitational Search Algorithm. The results prove that the proposed algorithm is able to provide very competitive results and outperforms the best algorithms in the literature on the majority of the test beds. The results of the real case studies also demonstrate the potential of MVO in solving real problems with unknown search spaces. Note that the source codes of the proposed MVO algorithm are publicly available at more

949 Citations

Journal ArticleDOI: 10.1007/S00521-013-1362-6
Shiliang Sun1Institutions (1)
Abstract: Multi-view learning or learning with multiple distinct feature sets is a rapidly growing direction in machine learning with well theoretical underpinnings and great practical success. This paper reviews theories developed to understand the properties and behaviors of multi-view learning and gives a taxonomy of approaches according to the machine learning mechanisms involved and the fashions in which multiple views are exploited. This survey aims to provide an insightful organization of current developments in the field of multi-view learning, identify their limitations, and give suggestions for further research. One feature of this survey is that we attempt to point out specific open problems which can hopefully be useful to promote the research of multi-view machine learning. more

663 Citations

Open accessJournal ArticleDOI: 10.1007/S00521-013-1367-1
Xin-She Yang1, Suash Deb2Institutions (2)
Abstract: Cuckoo search (CS) is a relatively new algorithm, developed by Yang and Deb in 2009, and the same has been found to be efficient in solving global optimization problems. In this paper, we review the fundamental ideas of cuckoo search and the latest developments as well as its applications. We analyze the algorithm and gain insight into its search mechanisms and find out why it is efficient. We also discuss the essence of algorithms and its link to self-organizing systems, and finally, we propose some important topics for further research. more

Topics: Cuckoo search (71%), Metaheuristic (61%)

582 Citations

Journal ArticleDOI: 10.1007/S00521-009-0295-6
Abstract: Pattern classification has been successfully applied in many problem domains, such as biometric recognition, document classification or medical diagnosis. Missing or unknown data are a common drawback that pattern recognition techniques need to deal with when solving real-life classification tasks. Machine learning approaches and methods imported from statistical learning theory have been most intensively studied and used in this subject. The aim of this work is to analyze the missing data problem in pattern classification tasks, and to summarize and compare some of the well-known methods used for handling missing values. more

Topics: Missing data (64%), Feature (machine learning) (63%), Document classification (59%) more

481 Citations

No. of papers from the Journal in previous years

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Journal's top 5 most impactful authors

Muhammad Asif Zahoor Raja

29 papers, 791 citations

Ahmad Taher Azar

19 papers, 1.2K citations

Mansour Sheikhan

17 papers, 485 citations

Chuandong Li

17 papers, 284 citations

Ahmed El-Shafie

14 papers, 468 citations

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