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A comprehensive survey of Crow Search algorithm and its applications

ARTIFICIAL INTELLIGENCE REVIEW 2021年第4期54卷 2669-2716页

作者： Meraihi, Yassine Gabis, Asma Benmessaoud Ramdane-Cherif, Amar Acheli, Dalila Univ MHamed Bougara Boumerdes LIST Lab Ave Independence Boumerdes 35000 Algeria Ecole Natl Super Informat Lab Methodes Concept Syst Algiers Algeria Univ Versailles St Quentin En Yvelines LISV Lab 10-12 Ave Europe F-78140 Velizy Villacoublay France Univ MHamed Bougara Boumerdes Automat Dept Ave Independence Boumerdes 35000 Algeria

Crow Search algorithm (CSA) is a recent swarm intelligence optimization algorithm inspired by the social intelligent behavior of crows for hiding food. It has been widely used to solve a large variety of optimization problems in several fields and areas of research and has proved its efficiency compared to several state-of-the-art optimization algorithms available in the literature. This paper presents a comprehensive overview of Crow Search algorithm and its new variants categorized into modified and hybridized versions. It also describes the several applications of CSA in various domains such as feature selection, image processing, scheduling, economic dispatch, distributed generation, and other engineering problems. In addition, the paper suggests some interesting research areas related to CSA enhancement, CSA hybridization, and possible new applications.

关键词： Crow Search algorithm Optimization nature-inspired algorithm Swarm intelligence Meta-heuristics

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A framework for self-tuning optimization algorithm

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NEURAL COMPUTING & APPLICATIONS 2013年第7-8期23卷 2051-2057页

作者： Yang, Xin-She Deb, Suash Loomes, Martin Karamanoglu, Mehmet Middlesex Univ Sch Sci & Technol London NW4 4BT England Cambridge Inst Technol Ranchi 835103 Jharkhand India

The performance of any algorithm will largely depend on the setting of its algorithm-dependent parameters. The optimal setting should allow the algorithm to achieve the best performance for solving a range of optimization problems. However, such parameter tuning itself is a tough optimization problem. In this paper, we present a framework for self-tuning algorithms so that an algorithm to be tuned can be used to tune the algorithm itself. Using the firefly algorithm as an example, we show that this framework works well. It is also found that different parameters may have different sensitivities and thus require different degrees of tuning. Parameters with high sensitivities require fine-tuning to achieve optimality.

关键词： algorithm Firefly algorithm Parameter tuning Optimization Metaheuristic nature-inspired algorithm

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A Modified Sine Cosine algorithm for Solving Optimization Problems

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IEEE ACCESS 2021年 9卷 27434-27450页

作者： Wang, Meng Lu, Guizhen Commun Univ China Sch Informat & Commun Engn Beijing 100024 Peoples R China

The sine cosine algorithm (SCA) is a newly emerging optimization algorithm. It is easy for sine cosine algorithm (SCA) to sink into premature of the algorithm and obtain a slower convergence rate when solving the complicated optimization problems, especially highly ill-posed problems. A novel modified sine cosine algorithm (MSCA) is put forward for solving the optimization problems. To our limited knowledge, the linear searching path and empirical parameter have not been applied to the related improved sine cosine algorithm. The proposed MSCA improves the search path of original SCA by introducing linear searching path and empirical parameter, effectively avoiding sinking into the local optimal. In addition, the proposed algorithm changes the definition of convergence factor. Two kinds of tests, including 23 benchmark functions test and actual engineering problem tests are adopted to prove the performance of the MSCA. In addition, the performance of the proposed MSCA is compared with SCA by using benchmark functions on different dimensional (D = 30, 50,100 and 500). As expected, the result of comparisons show that the proposed MSCA can better avoid the local optima than both the SCA and other population-based algorithms. And MSCA can obtain the faster convergence than SCA on different dimensions.

关键词： Optimization Birds Genetic algorithms Convergence Benchmark testing Stochastic processes Search problems Sine cosine algorithm linear searching path nature-inspired algorithm optimization

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CRYSTAL ENERGY OPTIMIZATION algorithm

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COMPUTATIONAL INTELLIGENCE 2016年第2期32卷 284-322页

作者： Feng, Xiang Ma, Meiyi Yu, Huiqun East China Univ Sci & Technol Dept Comp Sci & Engn Meilong Rd 130 Shanghai 200237 Peoples R China

nature has always been a muse for those who dream in art or science. As it goes, optimization algorithms inspired by nature have been widely used to solve various scientific and engineering problems because of their intelligence and simplicity. As a novel nature-inspired algorithm, the crystal energy optimizer (CEO) is proposed in this article. The proposed CEO is motivated by the following general observation on lake freezing in nature: the dynamics of crystals have possession of parallelism, openness, local interactivity, and self-organization. It stimulates us to extend a crystal dynamic model in physics to a generalized crystal energy optimizer for traveling salesman problems, so as to exploit the advantages of crystal dynamic system and to realize the aforementioned purposes. The proposed CEO has these advantages: (1) it has the ability to perform large-scale distributed parallel optimization;(2) it can converge and avoid local optimum;and (3) it is flexible and easy to adapt to a wide range of optimization problems.

关键词： crystal energy optimizer (CEO) computational intelligence parallel algorithm nature-inspired algorithm traveling salesman problem (TSP)

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Dragonfly algorithm: a comprehensive review and applications

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NEURAL COMPUTING & APPLICATIONS 2020年第21期32卷 16625-16646页

作者： Meraihi, Yassine Ramdane-Cherif, Amar Acheli, Dalila Mahseur, Mohammed Univ MHamed Bougara Boumerdes LIST Lab Ave Independence Boumerdes 35000 Algeria Univ Versailles St Quentin En Yvelines LISV Lab 10-12 Ave Europe F-78140 Velizy Villacoublay France Univ MHamed Bougara Boumerdes LAA Lab Ave Independence Boumerdes 35000 Algeria Univ Sci & Technol Houari Boumediene Fac Elect & Informat Algiers 16025 Algeria

Dragonfly algorithm (DA) is a novel swarm intelligence meta-heuristic optimization algorithm inspired by the dynamic and static swarming behaviors of artificial dragonflies in nature. It has proved its effectiveness and superiority compared to several well-known meta-heuristics available in the literature. This paper presents a comprehensive review of DA and its new variants classified into modified and hybrid versions. It also describes the main diverse applications of DA in several fields and areas such as machine learning, neural network, image processing, robotics, and engineering. Finally, the paper suggests some possible interesting research on the applications and hybridizations of DA for future works.

关键词： Dragonfly algorithm nature-inspired algorithm Swarm intelligence Meta-heuristics

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GWOVina: A grey wolf optimization approach to rigid and flexible receptor docking

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CHEMICAL BIOLOGY & DRUG DESIGN 2021年第1期97卷 97-110页

作者： Wong, Kin Meng Tai, Hio Kuan Siu, Shirley W. I. Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Ave Univ Taipa Macau Peoples R China

Protein-ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein. In this paper, we introduce an efficient flexible docking method,GWOVina, which is a variant of the Vina implementation using the grey wolf optimizer (GWO) and random walk for the global search, and the Dunbrack rotamer library for side-chain sampling. The new method was validated for rigid and flexible-receptor docking using four independent datasets. In rigid docking,GWOVinashowed comparable docking performance to Vina in terms of ligand pose RMSD, success rate, and affinity prediction. In flexible-receptor docking,GWOVinahas improved success rate compared to Vina and AutoDockFR. It ran 2 to 7 times faster than Vina and 40 to 100 times faster than AutoDockFR. Therefore,GWOVinacan play a role in solving the complex flexible-receptor docking cases and is suitable for virtual screening of compound *** freely available at for testing.

关键词： AutoDock Vina GWOVina nature-inspired algorithm protein-ligand docking side-chain flexibility structure-based drug design swarm intelligence

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Child Drawing Development Optimization algorithm Based on Child's Cognitive Development

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第2期47卷 1337-1351页

作者： Abdulhameed, Sabat Rashid, Tarik A. Univ Kurdistan Hewler Comp Sci & Engn Dept Erbil Krg Iraq

This paper proposes a novel metaheuristic Child Drawing Development Optimization (CDDO) algorithm inspired by the child's learning behavior and cognitive development using the golden ratio to optimize the beauty behind their art. The golden ratio was first introduced by the famous mathematician Fibonacci. The ratio of two consecutive numbers in the Fibonacci sequence is similar, and it is called the golden ratio, which is prevalent in nature, art, architecture, and design. CDDO uses golden ratio and mimics cognitive learning and child's drawing development stages starting from the scribbling stage to the advanced pattern-based stage. Hand pressure width, length and golden ratio of the child's drawing are tuned to attain better results. This helps children with evolving, improving their intelligence and collectively achieving shared goals. CDDO shows superior performance in finding the global optimum solution for the optimization problems tested by 19 benchmark functions. Its results are evaluated against more than one state-of-art algorithms such as PSO, DE, WOA, GSA, and FEP. The performance of the CDDO is assessed, and the test result shows that CDDO is relatively competitive through scoring 2.8 ranks. This displays that the CDDO is outstandingly robust in exploring a new solution. Also, it reveals the competency of the algorithm to evade local minima as it covers promising regions extensively within the design space and exploits the best solution.

关键词： nature-inspired algorithm Metaheuristic algorithms Optimization algorithms Golden ratio Mathematical models Child drawing development optimization CDDO

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Advances in spam detection for email spam, web spam, social network spam, and review spam: ML-based and nature-inspired-based techniques

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JOURNAL OF COMPUTER SECURITY 2021年第5期29卷 473-529页

作者： Akinyelu, Andronicus A. Univ Free State Dept Comp Sci & Informat ZA-9301 Bloemfontein South Africa

Despite the great advances in spam detection, spam remains a major problem that has affected the global economy enormously. Spam attacks are popularly perpetrated through different digital platforms with a large electronic audience, such as emails, microblogging websites (e.g. Twitter), social networks (e.g. Facebook), and review sites (e.g. Amazon). Different spam detection solutions have been proposed in the literature, however, Machine Learning (ML) based solutions are one of the most effective. Nevertheless, most ML algorithms have computational complexity problem, thus some studies introduced nature inspired (NI) algorithms to further improve the speed and generalization performance of ML algorithms. This study presents a survey of recent ML-based and NI-based spam detection techniques to empower the research community with information that is suitable for designing effective spam filtering systems for emails, social networks, microblogging, and review websites. The recent success and prevalence of deep learning show that it can be used to solve spam detection problems. Moreover, the availability of large-scale spam datasets makes deep learning and big data solutions (such as Mahout) very suitable for spam detection. Few studies explored deep learning algorithms and big data solutions for spam detection. Besides, most of the datasets used in the literature are either small or synthetically created. Therefore, future studies can consider exploring big data solutions, big datasets, and deep learning algorithms for building efficient spam detection techniques.

关键词： Spam detection nature-inspired algorithm machine learning spam email web spam social network spam review spam

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Machine learning approaches to modeling and optimization of biodiesel production systems: State of art and future outlook

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ENERGY CONVERSION AND MANAGEMENT-X 2024年 23卷

作者： Ishola, Niyi B. Epelle, Emmanuel I. Betiku, Eriola Obafemi Awolowo Univ Dept Chem Engn Biochem Engn Lab Ife 220005 Osun State Nigeria Univ Edinburgh Inst Infrastruct & Environm Sch Engn Edinburgh EH9 3JL Scotland Florida A&M Univ Dept Biol Sci Tallahassee FL 32307 USA

One of the main limitations to the economic sustainability of biodiesel production remains the high feedstock cost. Modeling and optimization are crucial steps to determine if processes (esterification and transesterification) involved in biodiesel production are economically viable. Phenomenological or mechanistic models can simulate the processes. These methods have been used to simulate and manage the processes, but their broad use has been constrained by computational complexity and numerical difficulties. Therefore, it is necessary to use quick, effective, accurate, and resilient modeling methodologies to simulate and regulate such complex systems. Datadriven computational and machine-learning (ML) techniques offer a potential replacement for conventional modeling methodologies to deal with the nonlinear, unpredictable, complex, and multivariate nature of biodiesel systems. Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) are the most often utilized ML tools in biodiesel research. To effectively attain maximum biodiesel yield, suitable optimization techniques based on nature-inspired optimization algorithms need to be integrated with these tools to obtain the best possible combination of various operating variables. Future research should focus on utilizing ML approaches for monitoring and managing biodiesel production systems to increase their effectiveness and promote commercial feasibility. Thus, the review discusses the various ML techniques used in modeling and optimizing biodiesel production systems.

关键词： Biodiesel Machine learning tools Modeling Optimization Esterification Transesterification nature-inspired algorithm Global optimization Simulation Computation Soft computing

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A Conceptual Comparison of Dragonfly algorithm Variants for CEC-2021 Global Optimization Problems

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第2期48卷 1563-1593页

作者： Joshi, Milan Kalita, Kanak Jangir, Pradeep Ahmadianfar, Iman Chakraborty, Shankar SVKMs Narsee Monjee Inst Management Studies Mukesh Patel Sch Technol Management & Engn Shirpur Campus Dhule Maharashtra India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Mech Engn Avadi India Rajasthan Rajya Vidyut Prasaran Nigam Sikar Rajasthan India Behbahan Khatam Alanbia Univ Technol Dept Civil Engn Behbahan Iran Jadavpur Univ Dept Prod Engn Kolkata India

Since the past few years, several metaheuristic algorithms, inspired by the natural processes, have been introduced to solve different complex optimization problems. Studying and comparing the convergence, computational burden and statistical significance of those metaheuristics are helpful for future algorithmic development and their applications. This paper focuses on comparing the optimization performance of classical dragonfly algorithm (DA) and its seven different variants, i.e., hybrid memory-based dragonfly algorithm with differential evolution (DADE), quantum-behaved and Gaussian mutational dragonfly algorithm (QGDA), memory-based hybrid dragonfly algorithm (MHDA), chaotic dragonfly algorithm (CDA), biogeography-based Mexican hat wavelet dragonfly algorithm (BMDA), hybrid Nelder-Mead algorithm and dragonfly algorithm (INMDA) and hybridization of dragonfly algorithm and artificial bee colony (HDA) while solving 80 CEC-2021 benchmark problems. It is observed that the convergence rates of different variants of DA algorithm vary, and the corresponding computational times for such variations are also evaluated. This paper finally ranks DA and its variants according to their convergence efficiency and Friedman test. The DADE, QGDA, BMDA and DA evolve out as the most efficient algorithms for solving the considered CEC-2021 benchmark problems.

关键词： Dragonfly algorithm Metaheuristic algorithm Optimization nature-inspired algorithm Swarm intelligence

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