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Improved Particle Swarm optimization on Based Quantum Behaved Framework for big data optimization

NEURAL PROCESSING LETTERS 2023年第3期55卷 2551-2586页

作者： Bas, Emine Selcuk Univ Kulu Vocat Sch TR-42075 Konya Turkey

In recent times, big data has become an essential concern with the rapid increase of digitalization. The problems that find solutions to the problems of finding and evaluating the features of big data are called optimization problems. In this paper, data sets containing EEG signals have been studied. The goal is to detect actual EEG signals while eliminating additional brain activity patterns in the collected data, resulting in more accurate interpretation. In the study, to handle big data optimization (big(Opt)) difficulties, a novel swarm intelligence-based technique is developed. A Developed PSO-Q was proposed by updating the random walking phase of the Particle Swarm optimization on the combined quantum behaved method (PSO-Q) for big(Opt) problems. PSO-Q's local search capability has been improved. The success of PSO-Q and IPSO-Q has been thoroughly tested in various cycles (maximum iterations) (300, 400, 500, and 1000) and population sizes (10, 25, and 50) on six data sets. The outcomes of the PSO-Q and IPSO-Q were statistically evaluated with the Wilcoxon Signed-Rank Test. PSO-Q and IPSO-Q have been compared with newly developed swarm-based algorithms (BA, Jaya, AOA, etc.) in the literature in recent years. The success of IPSO-Q has been shown by evaluating the results obtained. The results showed that IPSO-Q can be used as an alternative algorithm in big(Opt) problems.

关键词： big data optimization EEG signals Quantum-behaved particle swarm

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Chaotic golden ratio guided local search for big data optimization

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2023年 41卷

作者： Kocer, Havva Gul Turkoglu, Bahaeddin Uymaz, Sait Ali Selcuk Univ Konya Turkiye Nigde Omer Halisdemir Univ Dept Comp Engn Nigde Turkiye Konya Tech Univ Dept Comp Engn Konya Turkiye

Biological systems where order arises from disorder inspires for many metaheuristic optimization techniques. Self-organization and evolution are the common behaviour of chaos and optimization algorithms. Chaos can be defined as an ordered state of disorder that is hypersensitive to initial conditions. Therefore, chaos can help create order out of disorder. In the scope of this work, Golden Ratio Guided Local Search method was improved with inspiration by chaos and named as Chaotic Golden Ratio Guided Local Search (CGRGLS). Chaos is used as a random number generator in the proposed method. The coefficient in the equation for determining adaptive step size was derived from the Singer Chaotic Map. Performance evaluation of the proposed method was done by using CGRGLS in the local search part of MLSHADE-SPA algorithm. The experimental studies carried out with the electroencephalographic signal decomposition based optimization problems, named as big data optimization problem (big-Opt), introduced at the Congress on Evolutionary Computing big data Competition (CEC'2015). Experimental results have shown that the local search method developed using chaotic maps has an effect that increases the performance of the algorithm.& COPY;2023 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： big data optimization Local search Golden ratio Chaotic map Memetic algorithm

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A multi-objective African vultures optimization algorithm with binary hierarchical structure and tree topology for big data optimization

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Journal of Advanced Research 2024年

作者： Liu, Bo Zhou, Yongquan Wei, Yuanfei Luo, Qifang College of Artificial Intelligence Guangxi University for Nationalities Nanning 530006 China Xiangsihu College Guangxi University for Nationalities Nanning 530225 China Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi 43600 Malaysia Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis Nanning 530006 China

Introduction: big data optimization (big-Opt) problems present unique challenges in effectively managing and optimizing the analytical properties inherent in large-scale datasets. The complexity and size of these problems render traditional data processing methods insufficient. Objectives: In this study, we propose a new multi-objective optimization algorithm called the multi-objective African vulture optimization algorithm with binary hierarchical structure and tree topology (MO_Tree_BHSAVOA) to solve big-Opt problem. Methods: In MO_Tree_BHSAVOA, a binary hierarchical structure (BHS) is incorporated to effectively balance exploration and exploitation capabilities within the algorithm;shift density estimation is introduced as a mechanism for providing selection pressure for population evolution;and a tree topology is employed to reinforce the algorithm's ability to escape local optima and preserve optimal non-dominated solutions. The performance of the proposed algorithm is evaluated using CEC 2020 multi-modal multi-objective benchmark functions and CEC 2021 real-world constrained multi-objective optimization problems and is applied to big-Opt problems. Results: The performance is analyzed by comparing the results obtained with other multi-objective optimization algorithms and using Friedman's statistical test. The results show that the proposed MO_Tree_BHSAVOA not only provides very competitive results, but also outperforms other algorithms. Conclusion: These findings validate the effectiveness and potential applicability of MO_Tree_BHSAVOA in addressing the optimization challenges associated with big data. © 2024

关键词： African vulture optimization algorithm (AVOA) big data optimization Binary hierarchical structure Metaheuristic Multi-objective African vultures optimization algorithm (MO_Tree_BHSAVOA) Tree topology

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A comparative study between artificial bee colony (ABC) algorithm and its variants on big data optimization

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MEMETIC COMPUTING 2020年第2期12卷 129-150页

作者： Aslan, Selcuk Ondokuz Mayis Univ Samsun Turkey

The big data term and its formal definition have changed the properties of some of the computational problems. One of the problems for which the fundamental properties change with the existence of the big data is the optimization problems. Artificial bee colony (ABC) algorithm inspired by the intelligent source search, consumption and communication characteristics of the real honey bees has proven its efficiency on solving different numerical and combinatorial optimization problems. In this study, the standard ABC algorithm and its well-known variants including the gbest-guided ABC algorithm, the differential evolution based ABC/best/1 and ABC/best/2 algorithms, crossover ABC algorithm, converge-onlookers ABC algorithm and quick ABC algorithm were assessed using the electroencephalographic signal decomposition based optimization problems introduced at the 2015 Congress on Evolutionary Computing big data Competition. The experimental studies on solving big data optimization problems showed that the phase-divided structure of the standard ABC algorithm still protects its advantageous sides when the candidate food sources or solutions are generated by referencing the global best solution in the onlooker bee phase.

关键词： ABC algorithm big data optimization Signal decomposition

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A genetic Artificial Bee Colony algorithm for signal reconstruction based big data optimization

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APPLIED SOFT COMPUTING 2020年第0期88卷 106053-000页

作者： Aslan, Selcuk Karaboga, Dervis Ondokuz Mayis Univ Dept Comp Engn Samsun Turkey Erciyes Univ Dept Comp Engn Kayseri Turkey King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Jeddah Saudi Arabia

In recent years, the researchers have witnessed the changes or transformations driven by the existence of the big data on the definitions, complexities and future directions of the real world optimization problems. Analyzing the capabilities of the previously introduced techniques, determining possible drawbacks of them and developing new methods by taking into consideration of the unique properties related with the big data are nowadays in urgent demands. Artificial Bee Colony (ABC) algorithm inspired by the clever foraging behaviors of the real honey bees is one of the most successful swarm intelligence based optimization algorithms. In this study, a novel ABC algorithm based big data optimization technique was proposed. For exploring the solving abilities of the proposed technique, a set of experimental studies has been carried out by using different signal decomposition based big data optimization problems presented at the Congress on Evolutionary Computation (CEC) 2015 big data optimization Competition. The results obtained from the experimental studies first were compared with the well-known variants of the standard ABC algorithm named gbest-guided ABC (GABC), ABC/best/1, ABC/best/2, crossover ABC (CABC), converge-onlookers ABC (COABC) and quick ABC (qABC). The results of the proposed ABC algorithm were also compared with the Differential Evolution (DE) algorithm, Genetic algorithm (GA), Firefly algorithm (FA), Fireworks algorithm (FW), Phase Base optimization (PBO) algorithm, Particle Swarm optimization (PSO) algorithm and Dragonfly algorithm (DA) based big data optimization techniques. From the experimental studies, it was understood that the newly introduced ABC algorithm based technique is capable of producing better or at least promising results compared to the mentioned big data optimization techniques for all of the benchmark instances. (C) 2019 Elsevier B.V. All rights reserved.

关键词： big data optimization Signal decomposition Artificial Bee Colony

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An optimal fog-cloud offloading framework for big data optimization in heterogeneous IoT networks

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Decision Analytics Journal 2023年 8卷

作者： Bebortta, Sujit Tripathy, Subhranshu Sekhar Modibbo, Umar Muhammad Ali, Irfan Department of Computer Science Ravenshaw University Odisha Cuttack 753003 India School of Computer Engineering KIIT Deemed to be University Campus 15 Rd Chandaka Industrial Estate Odisha Patia Bhubaneswar 751024 India Department of Operations Research Modibbo Adama University Yola P.M.B. 2076 Nigeria Department of Statistics and Operations Research Aligarh Muslim University Aligarh 202 002 India

Executing complex and time-sensitive operations has become difficult due to the increased acceptance of Internet of Things (IoT) devices and IoT-generated big data, which can result in problems with power consumption and lag time. The fog computing layer is a distributed computing option that can handle some of these issues. However, fog computing devices’ limited computing and power capabilities make it difficult to complete operations under time constraints while minimizing service latency and fog resource energy consumption in delay-sensitive IoT-Fog applications. This paper suggests a dynamic integer linear programming technique for facilitating optimal task offloading that distributes resources from the fog computing layer to IoT devices while considering the constraints on timely task execution and resource availability. The offloading challenge is modeled as an integer linear programming (ILP) problem to ease the burden on finite fog resources and speed up the completion of time-sensitive operations. Given the large dimensionality of tasks in a dynamic environment, a task prioritization method is adopted to minimize tasks’ latency and the energy consumption of fog nodes. The findings demonstrate that the suggested approach performs better than benchmark approaches regarding energy utilization and service latency. Overall, the proposed technique offers an efficient and practical response to the issues raised by IoT devices and fog computing while enhancing system efficiency regarding power consumption and latency. © 2023 The Author(s)

关键词： big data optimization Computation offloading Energy consumption Fog computing Integer linear programming Internet of Things

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An Artificial Bee Colony-Guided Approach for Electro-Encephalography Signal Decomposition-Based big data optimization

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2020年第2期19卷 561-600页

作者： Aslan, Selcuk Ondokuz Mayis Univ Samsun Dept Comp Engn Samsun Turkey

The digital age has added a new term to the literature of information and computer sciences called as the big data in recent years. Because of the individual properties of the newly introduced term, the definitions of the data-intensive problems including optimization problems have been substantially changed and investigations about the solving capabilities of the existing techniques and then developing their specialized variants for big data optimizations have become important research topic. Artificial Bee Colony (ABC) algorithm inspired by the clever foraging characteristics of the real honey bees is one of the most successful swarm intelligence-based metaheuristics. in this study, a new ABC algorithm-based technique that is named source-linked ABC (slinkABC) was proposed by considering the properties of the optimization problems related with the big data. The slinkABC algorithm was tested on the big data optimization problems presented at the Congress on Evolutionary Computation (CEC) 2015 big data optimization Competition. The results obtained from the experimental studies were compared with the different variants of the ABC algorithm including gbest-guided ABC (GABC), ABC/best/1, ABC/best/2, crossover ABC (CABC), converge-onlookers ABC (COABC), quick ABC (qABC) and modified gbest-guided ABC (MGABC) algorithms. In addition to these, the results of the proposed ABC algorithm were also compared with the results of the Differential Evolution (DE) algorithm, Genetic algorithm (GA), Firefly algorithm (FA), Phase-Based optimization (PBO) algorithm and Particle Swarm optimization (PSO) algorithm-based approaches. From the experimental studies, it was understood that the ABC algorithm modified by considering the unique properties of the big data optimization problems as in the slinkABC produces better solutions for most of the tested instances compared to the mentioned optimization techniques.

关键词： big data optimization EEG signal and Artificial bee colony

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Hybrid multi-objective evolutionary algorithm based on Search Manager framework for big data optimization problems

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APPLIED SOFT COMPUTING 2020年 87卷 105991-000页

作者： Abdi, Yousef Feizi-Derakhshi, Mohammad-Reza Univ Tabriz Fac Elect & Comp Engn Computerized Intelligence Syst Lab Tabriz Iran

big data optimization (big-Opt) refers to optimization problems which require to manage the properties of big data analytics. In the present paper, the Search Manager (SM), a recently proposed framework for hybridizing metaheuristics to improve the performance of optimization algorithms, is extended for multi-objective problems (MOSM), and then five configurations of it by combination of different search strategies are proposed to solve the EEG signal analysis problem which is a member of the big data optimization problems class. Experimental results demonstrate that the proposed configurations of MOSM are efficient in this kind of problems. The configurations are also compared with NSGA-III with uniform crossover and adaptive mutation operators (NSGA-III UCAM), which is a recently proposed method for big-Opt problems. (C) 2019 Elsevier B.V. All rights reserved.

关键词： big data optimization Hybrid multi-objective evolutionary algorithm Search Manager framework Evolutionary operators

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Recent Advances in Randomized Methods for big data optimization

Recent Advances in Randomized Methods for Big Data Optimizat...

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作者： Jie Liu Lehigh University

学位级别：博士

In this thesis, we discuss and develop randomized algorithms for big data problems. In particular, we study the finite-sum optimization with newly emerged variance- reduction optimization methods (Chapter 2), explore the efficiency of second-order information applied to both convex and non-convex finite-sum objectives (Chapter 3) and employ the fast first-order method in power system problems (Chapter 4). In Chapter 2, we propose two variance-reduced gradient algorithms – mS2GD and SARAH. mS2GD incorporates a mini-batching scheme for improving the theoretical complexity and practical performance of SVRG/S2GD, aiming to minimize a strongly convex function represented as the sum of an average of a large number of smooth con- vex functions and a simple non-smooth convex regularizer. While SARAH, short for StochAstic Recursive grAdient algoritHm and using a stochastic recursive gradient, targets at minimizing the average of a large number of smooth functions for both con- vex and non-convex cases. Both methods fall into the category of variance-reduction optimization, and obtain a total complexity of O ((n + κ)log(1/ε)) to achieve an ε -accuracy solution for strongly convex objectives, while SARAH also maintains a sub-linear convergence for non-convex problems. Meanwhile, SARAH has a practical variant SARAH+ due to its linear convergence of the expected stochastic gradients in inner loops. In Chapter 3, we declare that randomized batches can be applied with second- order information, as to improve upon convergence in both theory and practice, with a framework of L-BFGS as a novel approach to finite-sum optimization problems. We provide theoretical analyses for both convex and non-convex objectives. Meanwhile, we propose LBFGS-F as a variant where Fisher information matrix is used instead of Hessian information, and prove it applicable to a distributed environment within the popular applications of least-square and cross-entropy losses. In Chapter 4, we develop fast rand

关键词： big data optimization Large-scale optimization Machine Learning Power Systems Randomized Methods

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An improved NSGA-III algorithm with adaptive mutation operator for big data optimization problems

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2018年 88卷 571-585页

作者： Yi, Jiao-Hong Deb, Suash Dong, Junyu Alavi, Amir H. Wang, Gai-Ge Qingdao Univ Technol Sch Informat & Control Engn Qingdao 266520 Peoples R China Victoria Univ Decis Sci & Modeling Program Melbourne Vic 8001 Australia Ocean Univ China Dept Comp Sci & Technol Qingdao 266100 Peoples R China Univ Missouri Dept Civil & Environm Engn Columbia MO 65211 USA Jiangsu Normal Univ Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Northeast Normal Univ Inst Algorithm & Big Data Anal Changchun 130117 Jilin Peoples R China Northeast Normal Univ Sch Comp Sci & Informat Technol Changchun 130117 Jilin Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Jilin Peoples R China

One of the major challenges of solving big data optimization problems via traditional multi-objective evolutionary algorithms (MOEAs) is their high computational costs. This issue has been efficiently tackled by non -dominated sorting genetic algorithm, the third version, (NSGA-111). On the other hand, a concern about the NSGA-Ill algorithm is that it uses a fixed rate for mutation operator. To cope with this issue, this study introduces an adaptive mutation operator to enhance the performance of the standard NSGA-111 algorithm. The proposed adaptive mutation operator strategy is evaluated using three crossover operators of NSGA-111 including simulated binary crossover (SBX), uniform crossover (UC) and single point crossover (SI). Subsequently, three improved NSGA-111 algorithms (NSGA-111 SBXAM, NSGA-III SIAM, and NSGAIII UCAM) are developed. These enhanced algorithms are then implemented to solve a number of big data optimization problems. Experimental results indicate that NSGA-III with UC and adaptive mutation operator outperforms the other NSGA-111 algorithms. (C) 2018 Elsevier B.V. All rights reserved.

关键词： big data optimization Evolutionary multi-objective optimization NSGA-III Mutation operator Adaptive operators

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