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FOA: fireworks optimization algorithm

MULTIMEDIA TOOLS AND APPLICATIONS 2022年第23期81卷 33151-33170页

作者： Ehsaeyan, Ehsan Zolghadrasli, Alireza Sirjan Univ Technol Elect Engn Dept Sirjan Iran Shiraz Univ Dept Commun & Elect Engn Shiraz Iran

In this paper, a novel meta-heuristic algorithm called Fireworks Optimization algorithm (FOA) is introduced with few control parameters for discrete and continuous optimization problems. This algorithm is inspired from explosion pyrotechnic devices producing colorful spikes like red, blue and silver. By modelling the explosion behavior of the Fireworks in the sky, the search space can be swept efficiently to find the global optima. To improve the balance between the exploration and exploitation of individuals, three categories are defined to avoid local optimal traps and applied to the search agents. Each category has a different task and predefined updating position rules. A grouping strategy is considered to prevent the algorithm from premature convergence. The performance of FOA is demonstrated over 15 standard benchmarks in the continuous version and 30 images thresholding problems in the discrete version. The obtained results reveal the superiority of the proposed algorithm with fewer input parameters over other state-of-the-art optimization methods in most cases.

关键词： Fireworks optimization algorithm bio-inspired algorithm Benchmarked functions meta-heuristic technique Image segmentation Engineering applications

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A comprehensive survey on the chicken swarm optimization algorithm and its applications: state-of-the-art and research challenges

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第7期57卷 170-170页

作者： Chen, Binhe Cao, Li Chen, Changzu Chen, Yaodan Yue, Yinggao Wenzhou Univ Technol Sch Intelligent Mfg & Elect Engn Wenzhou 325035 Peoples R China Wenzhou Univ Intelligent Informat Syst Inst Wenzhou 325035 Peoples R China

The application of optimization theory and the algorithms that are generated from it has increased along with science and technology's continued advancement. Numerous issues in daily life can be categorized as combinatorial optimization issues. Swarm intelligence optimization algorithms have been successful in machine learning, process control, and engineering prediction throughout the years and have been shown to be efficient in handling combinatorial optimization issues. An intelligent optimization system called the chicken swarm optimization algorithm (CSO) mimics the organic behavior of flocks of chickens. In the benchmark problem's optimization process as the objective function, it outperforms several popular intelligent optimization methods like PSO. The concept and advancement of the flock optimization algorithm, the comparison with other meta-heuristic algorithms, and the development trend are reviewed in order to further enhance the search performance of the algorithm and quicken the research and application process of the algorithm. The fundamental algorithm model is first described, and the enhanced chicken swarm optimization algorithm based on algorithm parameters, chaos and quantum optimization, learning strategy, and population diversity is then categorized and summarized using both domestic and international literature. The use of group optimization algorithms in the areas of feature extraction, image processing, robotic engineering, wireless sensor networks, and power. Second, it is evaluated in terms of benefits, drawbacks, and application in comparison to other meta-heuristic algorithms. Finally, the direction of flock optimization algorithm research and development is anticipated.

关键词： Swarm intelligence Evolutionary computation bio-inspired algorithm Chicken swarm optimization algorithm

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A novel metaheuristic inspired by horned lizard defense tactics

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第3期57卷 59-59页

作者： Peraza-Vazquez, Hernan Pena-Delgado, Adrian Merino-Trevino, Marco Morales-Cepeda, Ana Beatriz Sinha, Neha Inst Politecn Nacl CICATA Altamira Km 14-5 Carretera Tampico Puerto Ind Altamira Mexico City 89120 Tamaulipas Mexico Univ Tecnol Altamira Puerto Ind Altamira Blvd Rios Km 3 100Puerto Ind Altamira Mexico City 89601 Tamaulipas Mexico TecNM Inst Tecnol Ciudad Madero Juventino Rosas & Jesus Urueta s-nCol Los Mangos Ciudad Madero 89318 Tamaulipas Mexico Indian Inst Informat Technol Bhagalpur Dept Comp Sci & Engn Bhagalpur 813210 Bihar India

This paper introduces HLOA, a novel metaheuristic optimization algorithm that mathematically mimics crypsis, skin darkening or lightening, blood-squirting, and move-to-escape defense methods. In crypsis behavior, the lizard changes its color by becoming translucent to avoid detection by its predators. The horned lizard can lighten or darken its skin, depending on whether or not it needs to decrease or increase its solar thermal gain. The skin darkening or lightening strategy is modeled by including the stimulating hormone melanophore rate( alpha\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}-MHS) that influences these skin color changes. Further, the move-to-evasion strategy is also mathematically described. The horned lizard's shooting blood defense mechanism, described as a projectile motion, is also modeled. These strategies balance exploitation and exploration mechanisms for local and global search over the solution space. HLOA performance is benchmarked with sixty-three optimization problems from the literature, testbench problems provided in IEEE CEC- 2017 "Constrained Real-Parameter Optimization", analyzed for dimensions 10, 30, 50, and 100, as well as testbench functions from IEEE CEC-06 2019 "100-Digit Challenge". Moreover, three real-world constraint optimization applications from IEEE CEC2020 and two engineering problems, the multiple gravity assist optimization and the optimal power flow problem, are also studied. Wilcoxon and Friedman statistics tests compare the HLOA algorithm results against ten recent bio-inspired algorithms. Wilcoxon shows that HLOA provides the optimal solution for most testbench functions more effectively than competing algorithms. At the same time, the Friedman statistics test ranks the HLOA first, and the n-dimensional analysis

关键词： Optimization bio-inspired algorithm Metaheuristics Horned Lizard Crypsis

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Integrated Deep Auto-Encoder and Q-Learning-Based Scheme to Detect Anomalies and Supporting Forensics in Cloud Computing Environments

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WIRELESS PERSONAL COMMUNICATIONS 2022年第3期127卷 2247-2265页

作者： Savaridassan, P. Maragatham, G. SRM Inst Sci & Technol Sch Comp Dept Informat Technol Chennai Tamil Nadu India

The cloud computing environment when deployed correctly is responsible for delivering scalability, cost efficiency, reliability, security and interoperability to the end users. Log analysis is considered to be an indispensable component of security regulations and framework, since these computer-generated records help the organizations, businesses and networks to respond to different kinds of risks that are possible to cloud environment in a reactive and proactive manner. In this paper, an Integrated Deep Auto-Encoder and Q-learning-based Deep Learning (IDEA-QLDL) Scheme is proposed for attaining maximum prediction accuracy during the process of exploring log data and classifying them into genuine and anomalous. It initiates the process of acceptance or denial based on the continuous investigation of behavioral patterns that are highly applicable for classification. The results of the proposed IDEA-QLDL Scheme confirmed its predominance in improving the classification accuracy, precision, recall and detection time compared to the benchmarked schemes considered for investigation.

关键词： bio-inspired algorithm Q-Learning Deep auto-encoder Anomaly detection

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Novel yellow saddle goatfish algorithm for improving performance and efficiency of PV system under partial shading conditions

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SOLAR ENERGY 2022年 247卷 295-307页

作者： Azli, Hadjer Titri, Sabrina Larbes, Cherif Kaced, Karim Femmam, Karima Ecole Natl Polytech Dept Elect El Harrach Algeria Ctr Dev Technol Avancees CDTA Baba Hassen Alger Algeria Mohamed Khider Univ Dept Math Biskra Algeria

This paper proposes a novel maximum power point tracking (MPPT) technique based on a new optimization algorithm called yellow saddle goatfish algorithm (YSGA). The photovoltaic system loses a significant amount of power under partial shading conditions (PSC). Because of the presence of bypass diodes, several maximums appear in the Power-Voltage characteristic, making tracking the global maximum power point (GMPP) extremely difficult. A yellow saddle goatfish-based MPPT algorithm is proposed and implemented to address this issue and improve the efficiency of the PV system. This algorithm employs a subgroup research methodology and a very interesting strategy of particle collaboration during GMPP tracking, providing a balance between the exploration and exploitation phases of the research process. To investigate its performance, tests were conducted using six different partial shading patterns and a fast irradiance variation. The simulation was carried out in MATLAB/Simulink using a series of MSX60 solar panels. The results were compared to well-known and efficient algorithms such as the Grey wolf optimization algorithm (GWO), the Bat algorithm (BAT), particle swarm optimization (PSO), the Seagull optimization algorithm (SOA), and the Perturb and observe (P&O) algorithm. The findings demonstrate the proposed method's high robustness, as well as its high dynamic and accuracy in tracking the GMPP. Furthermore, a quantitative and statistical analysis of 100 tests revealed that it outperformed the other algorithms in terms of efficiency, success rate, convergence time, and solution dispersion.

关键词： Yellow saddle goatfish algorithm (YSGA) Maximum power point tracking (MPPT) Photovoltaic (PV) system Partial shading condition (PSC) bio-inspired algorithm

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Deep Learning Based Optimal Multimodal Fusion Framework for Intrusion Detection Systems for Healthcare Data

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Computers, Materials & Continua 2021年第3期66卷 2555-2571页

作者： Phong Thanh Nguyen Vy Dang Bich Huynh Khoa Dang Vo Phuong Thanh Phan Mohamed Elhoseny Dac-Nhuong Le Department of Project Management Ho Chi Minh City Open UniversityHo Chi Minh City700000Vietnam Department of Learning Material Ho Chi Minh City Open UniversityHo Chi Minh City700000Vietnam Faculty of Computers and Information Mansoura UniversityDakahlia Governorate35516Egypt Institute of Research and Development Duy Tan UniversityDanang550000Vietnam Faculty of Information Technology Duy Tan UniversityDanang550000Vietnam

Data fusion is a multidisciplinary research area that involves different *** is used to attain minimum detection error probability and maximum reliability with the help of data retrieved from multiple healthcare *** generation of huge quantity of data from medical devices resulted in the formation of big data during which data fusion techniques become *** medical data is a crucial issue of exponentially-pacing computing world and can be achieved by Intrusion Detection Systems(IDS).In this regard,since singularmodality is not adequate to attain high detection rate,there is a need exists to merge diverse techniques using decision-based multimodal fusion *** this view,this research article presents a new multimodal fusion-based IDS to secure the healthcare data using *** proposed model involves decision-based fusion model which has different processes such as initialization,pre-processing,Feature Selection(FS)and multimodal classification for effective detection of *** FS process,a chaotic Butterfly Optimization(BO)algorithmcalled CBOA is *** the classic BO algorithm offers effective exploration,it fails in achieving faster *** order to overcome this,i.e.,to improve the convergence rate,this research work modifies the required parameters of BO algorithm using chaos ***,to detect intrusions,multimodal classifier is applied by incorporating three Deep Learning(DL)-based classification ***,the concepts like Hadoop MapReduce and Spark were also utilized in this study to achieve faster computation of big data in parallel computation *** validate the outcome of the presented model,a series of experimentations was performed using the benchmark NSLKDDCup99 Dataset *** proposed model demonstrated its effective results on the applied dataset by offering the maximum accuracy of 99.21%,precision of 98.93%and detection rate of 99.59%.The results assured the betterm

关键词： Big data data fusion deep learning intrusion detection bio-inspired algorithm spark

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Self-Synchronization in Duty-Cycled Internet of Things (IoT) Applications

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IEEE INTERNET OF THINGS JOURNAL 2017年第6期4卷 2058-2069页

作者： Yadav, Poonam McCann, Julie A. Pereira, Tiago Univ Cambridge Comp Lab Cambridge CB3 9DD England Imperial Coll London Dept Comp London SW7 2AZ England Univ Sao Paulo Inst Math & Comp Sci BR-05508010 Sao Paulo Brazil

In recent years, the networks of low-power devices have gained popularity. Typically, these devices are wireless and interact to form large networks such as the machine to machine networks, Internet of Things, wearable computing, and wireless sensor networks. The collaboration among these devices is a key to achieving the full potential of these networks. A major problem in this field is to guarantee robust communication between elements while keeping the whole network energy efficient. In this paper, we introduce an extended and improved emergent broadcast slot (EBS) scheme, which facilitates collaboration for robust communication and is energy efficient. In the EBS, nodes communication unit remains in sleeping mode and are awake just to communicate. The EBS scheme is fully decentralized, that is, nodes coordinate their wake-up window in a partially overlapped manner within each duty-cycle to avoid message collisions. We show the theoretical convergence behavior of the scheme, which is confirmed through real test-bed experimentation.

关键词： bio-inspired algorithm broadcast messaging firefly based self-synchronization Internet of Things (IoT) machine to machine (M2M) networks media access control (MAC) radio interference

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A new fuzzy bee colony optimization with dynamic adaptation of parameters using interval type-2 fuzzy logic for tuning fuzzy controllers

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SOFT COMPUTING 2018年第2期22卷 571-594页

作者： Amador-Angulo, Leticia Castillo, Oscar Tijuana Inst Technol Div Grad Studies Tijuana 22500 BC Mexico

This paper presents a new fuzzy bee colony optimization method to find the optimal distribution of the membership functions in the design of fuzzy controllers for complex nonlinear plants. We used interval type-2 and type-1 fuzzy logic systems in dynamically adapting the alpha and beta parameter values of the bee colony optimization algorithm (BCO). Simulation results with a type-1 fuzzy logic controller for benchmark control plants are presented. The advantage of using interval type-2 fuzzy logic systems for dynamic adjustment of parameters in BCO applied in fuzzy controller design is verified with two benchmark problems. We considered different levels and types of noise in the simulations to analyze the approach of interval type-2 fuzzy logic systems to find the best values of alpha and beta for BCO when applied in the design of fuzzy controllers.

关键词： Interval type-2 fuzzy logic system Fuzzy controller bio-inspired algorithm Footprint uncertainty Bee colony optimization

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A Novel Variant of Moth Flame Optimizer for Higher Dimensional Optimization Problems

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Journal of bionic Engineering 2023年第5期20卷 2389-2415页

作者： Saroj Kumar Sahoo Sushmita Sharma Apu Kumar Saha Department of Mathematics National Institute of Technology AgartalaJiraniaTripura799046India

Moth Flame Optimization(MFO)is a nature-inspired optimization algorithm,based on the principle of navigation technique of moth toward *** to less parameter and easy implementation,MFO is used in various field to solve optimization ***,for the complex higher dimensional problems,MFO is unable to make a good trade-off between global and local *** overcome these drawbacks of MFO,in this work,an enhanced MFO,namely WF-MFO,is introduced to solve higher dimensional optimization *** a more optimal balance between global and local search,the original MFO’s exploration ability is improved by an exploration operator,namely,Weibull flight *** addition,the local optimal solutions have been avoided and the convergence speed has been increased using a Fibonacci search process-based technique that improves the quality of the solutions ***-nine benchmark functions of varying complexity with 1000 and 2000 dimensions have been utilized to verify the projected *** popular algorithms and MFO versions have been compared to the achieved *** addition,the robustness of the proposed WF-MFO method has been evaluated using the Friedman rank test,the Wilcoxon rank test,and convergence *** to other methods,the proposed WF-MFO algorithm provides higher quality solutions and converges more quickly,as shown by the ***,the proposed WF-MFO has been used to the solution of two engineering design issues,with striking *** improved performance of the proposed WF-MFO algorithm for addressing larger dimensional optimization problems is guaranteed by analyses of numerical data,statistical tests,and convergence performance.

关键词： Moth Flame Optimization(MFO)algorithm bio-inspired algorithm Fibonacci search method Weibull distribution Higher dimensional functions

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Two-stage eagle strategy with differential evolution

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INTERNATIONAL JOURNAL OF bio-inspired COMPUTATION 2012年第1期4卷 1-5页

作者： Yang, Xin-She Deb, Suash Natl Phys Lab Teddington TW11 0LW Middx England CV Raman Coll Engn Dept Comp Sci & Engn Bhubaneswar 752054 Orissa India

Efficiency of an optimisation process is largely determined by the search algorithm and its fundamental characteristics. In a given optimisation, a single type of algorithm is used in most applications. In this paper, we will investigate the eagle strategy recently developed for global optimisation, which uses a two-stage strategy by combing two different algorithms to improve the overall search efficiency. We will discuss this strategy with differential evolution and then evaluate their performance by solving real-world optimisation problems such as pressure vessel and speed reducer design. Results suggest that we can reduce the computing effort by a factor of up to ten in many applications.

关键词： bat algorithm Cuckoo Search eagle strategy ES bio-inspired algorithm differential evolution DE optimisation

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