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A survey of intelligent optimization algorithms for solving satisfiability problems

JOURNAL OF intelligent & FUZZY SYSTEMS 2023年第1期45卷 445-461页

作者： Yang, Lan Wang, Xiaofeng Ding, Hongsheng Yang, Yi Zhao, Xingyu Pang, Lichao North Minzu Univ Sch Comp Sci & Engn Yinchuan Peoples R China North Minzu Univ Key Lab Image & Graph Intelligent Proc State Ethn Affairs Commiss Yinchuan Peoples R China

Constraint satisfaction problems have a wide range of applications in areas such as basic computer theory research and artificial intelligence, and many major studies in industry are not solved directly, but converted into instances of satisfiability problems for solution. Therefore, the solution of the satisfiability problem is a central problem in many important areas in the future. A large number of solution algorithms for this problem are mainly based on completeness algorithms and heuristic algorithms. intelligent optimization algorithms with heuristic policies run significantly more efficiently on large-scale instances compared to completeness algorithms. This paper compares the principles, implementation steps, and applications of several major intelligent optimization algorithms in satisfiability problems, analyzes the characteristics of these algorithms, and focuses on the performance in solving satisfiability problems under different constraints. In terms of algorithms, evolutionary algorithms and swarm intelligence algorithms are introduced;in terms of applications, the solution to the satisfiability problem is studied. At the same time, the performance of the listed intelligent optimization algorithms in applications is analyzed in detail in terms of the direction of improvement of the algorithms, advantages and disadvantages and comparison algorithms, respectively, and the future application of intelligent optimization algorithms in satisfiability problems is prospected.

关键词： Constraint satisfaction problem satisfiability problem completeness algorithm heuristic algorithm intelligent optimization algorithms

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An overview of high utility itemsets mining methods based on intelligent optimization algorithms

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KNOWLEDGE AND INFORMATION SYSTEMS 2022年第11期64卷 2945-2984页

作者： Han, Meng Gao, Zhihui Li, Ang Liu, Shujuan Mu, Dongliang North Minzu Univ Sch Comp Sci & Engn Yinchuan 750021 Ningxia Peoples R China

Mining high utility itemsets from massive data is one of the most active research directions in data mining at present. intelligent optimization algorithms have been applied to the high utility itemsets mining because of their flexibility and intelligence, and have achieved good results. In this paper, high utility itemsets mining strategies based on swarm intelligence optimization algorithms are mainly analyzed and summarized comprehensively, and the strategies based on the evolutionary algorithms and other intelligence optimization algorithms are introduced in detail. The method based on swarm intelligence optimization algorithm is summarized and compared from the aspects of update strategy, pruning strategy, comparison algorithms, dataset, parameter settings, advantages, disadvantages, etc. The methods based on particle swarm optimization are classified in terms of particle update, which are traditional update strategies, sigmoid function-based strategies, greed-based strategies, roulette mechanism-based strategies, and set-based strategies. The experimental comparative analysis of the algorithms is carried out in terms of the operational efficiency of the algorithms and the number of high utility itemsets mined by the algorithms under the conditions of the same dataset. The experimental analysis shows that the strategy based on the swarm intelligence optimization algorithm is optimal, especially the high utility itemsets mining algorithm based on the bionic algorithm, which has a shorter running time and less number of high utility itemsets lost, and the least efficient strategy based on the genetic algorithm, which will lose a large number of itemsets.

关键词： Overview Data mining High utility itemsets intelligent optimization algorithms High utility itemsets mining

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A General Framework for intelligent optimization algorithms Based on Multilevel Evolutions 13th

A General Framework for Intelligent Optimization Algorithms ...

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13th International Conference on Swarm Intelligence (ICSI)

作者： Wang, Chenchen Chen, Caifeng Lun, Ziru Ye, Zhanyu Liu, Qunfeng Dongguan Univ Technol Sch Comp Sci & Technol Dongguan 523808 Peoples R China

ISBN: (纸本)9783031096778;9783031096761

intelligent optimization is a kind of global optimization algorithms based on simulating biological intelligent behaviors such as evolution and foraging. Currently, there are numerous intelligent optimization algorithms have been proposed based on a large mount of animals' or plants' behaviors. This phenomenon shows the prosperity of this field, but bring issues about these algorithms' analysis and applications. We believe an extensive development stage has passed in the field of intelligent optimization, and more theoretical analysis and deep understanding about these algorithms become favorite. In this paper, we try to build a general framework for all population-based global optimization algorithms. This framework employs the idea of multilevel evolution, and therefore it can include not only the traditional bio-inspired evolution algorithms which often only evolute in a single level of search space, but also those population-based algorithms adopt data-driven strategies or cultural evolutions. By the help of the proposed framework, we can classify all population-based global optimization algorithms into three types, and improve the traditional algorithms. In this paper, this framework is then applied to the popular particle swarm optimization, and a modified particle swarm optimization with three-level of evolutions is proposed. Numerical results show that the modified algorithm improves the original one significantly.

关键词： intelligent optimization algorithms Population-based optimization algorithms General framework Multilevel evolutions

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Research Review of Ship Power Load Forecasting Based on intelligent optimization algorithms

Research Review of Ship Power Load Forecasting Based on Inte...

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2024 IEEE International Conference on Cognitive Computing and Complex Data

作者： Meng, Ningjia Mei, Jiahao Shen, Kang Liang, Tianqin Zhang, Yuanyuan Xie, Jijin Beibu Gulf Univ Coll Mech & Marine Engn Qinzhou Peoples R China Beibu Gulf Univ Eastern Michigan Joint Coll Engn Qinzhou Peoples R China

ISBN: (纸本)9798350352900;9798350352894

Amidst the swift advancements of the global shipping industry and the escalating demand for energy conservation and emission reduction, ship power load forecasting has emerged as a pivotal research domain to guarantee the stable operation of ship power systems. Precise power load forecasting not only optimizes the scheduling and management of ship power systems but also markedly enhances fuel utilization efficiency and mitigates emissions. In recent years, intelligent optimization algorithms, renowned for their notable advantages in addressing complex nonlinear problems, have been increasingly applied to the study of ship power load forecasting. This paper delves into ship power load forecasting methodologies predicated on intelligent optimization algorithms, with a primary focus on the application of genetic algorithms, particle swarm optimization algorithms, and artificial neural networks. These algorithms emulate natural evolution, swarm intelligence, and neural system mechanisms, thereby effectively addressing complex power load forecasting challenges. In conclusion, ship power load forecasting founded on intelligent optimization algorithms holds substantial theoretical significance and extensive application prospects. With the advancement and application of related technologies, the operational efficiency and reliability of ship power systems will be further augmented, thereby furnishing robust support for attaining green shipping and sustainable development.

关键词： intelligent optimization algorithms ship power load forecasting genetic algorithm particle swarm optimization algorithm

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An Adaptive Surrogate-Assisted Endmember Extraction Framework Based on intelligent optimization algorithms for Hyperspectral Remote Sensing Images

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REMOTE SENSING 2022年第4期14卷 892页

作者： Wang, Zhao Li, Jianzhao Liu, Yiting Xie, Fei Li, Peng Xidian Univ Key Lab Elect Informat Countermeasure & Simulat T Minist Educ 2 South Taibai Rd Xian 710075 Peoples R China Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China 2 South Taibai Rd Xian 710071 Peoples R China Xidian Univ Acad Adv Interdisciplinary Res 2 South Taibai Rd Xian 710068 Peoples R China

As the foremost step of spectral unmixing, endmember extraction has been one of the most challenging techniques in the spectral unmixing processing due to the mixing of pixels and the complexity of hyperspectral remote sensing images. The existing geometrial-based endmember extraction algorithms have achieved the ideal results, but most of these algorithms perform poorly when they do not meet the assumption of simplex structure. Recently, many intelligent optimization algorithms have been employed to solve the problem of endmember extraction. Although they achieved the better performance than the geometrial-based algorithms in different complex scenarios, they also suffer from the time-consuming problem. In order to alleviate the above problems, balance the two key indicators of accuracy and running time, an adaptive surrogate-assisted endmember extraction (ASAEE) framework based on intelligent optimization algorithms is proposed for hyperspectral remote sensing images in this paper. In the proposed framework, the surrogate-assisted model is established to reduce the expensive time cost of the intelligent algorithms by fitting the fully constrained evaluation value with the low-cost estimated value. In more detail, three commonly used intelligent algorithms, namely genetic algorithm, particle swarm optimization algorithm and differential evolution algorithm, are specifically designed into the ASAEE framework to verify the effectiveness and robustness. In addition, an adaptive weight surrogate-assisted model selection strategy is proposed, which can automatically adjust the weights of different surrogate models according to the characteristics of different intelligent algorithms. Experimental results on three data sets (including two simulated data sets and one real data set) show the effectiveness and the excellent performance of the proposed ASAEE framework.

关键词： hyperspectral remote sensing intelligent optimization algorithms endmember extraction surrogate-assisted model

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Comparative Analysis for Evaluating Wind Energy Resources Using intelligent optimization algorithms and Numerical Methods

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Computer Systems Science & Engineering 2023年第10期47卷 491-513页

作者： Musaed Alrashidi Department of Electrical Engineering College of EngineeringQassim UniversityBuraidahSaudi Arabia

Statistical distributions are used to model wind speed,and the twoparameters Weibull distribution has proven its effectiveness at characterizing wind *** estimation of Weibull parameters,the scale(c)and shape(k),is crucial in describing the actual wind speed data and evaluating the wind energy ***,this study compares the most common conventional numerical(CN)estimation methods and the recent intelligent optimization algorithms(IOA)to show how precise estimation of c and k affects the wind energy resource *** addition,this study conducts technical and economic feasibility studies for five sites in the northern part of Saudi Arabia,namely Aljouf,Rafha,Tabuk,Turaif,and *** exhibit that IOAs have better performance in attaining optimal Weibull parameters and provided an adequate description of the observed wind speed ***,with six wind turbine technologies rating between 1 and 3MW,the technical and economic assessment results reveal that the CN methods tend to overestimate the energy output and underestimate the cost of energy($/kWh)compared to the assessments by *** energy cost analyses show that Turaif is the windiest site,with an electricity cost of$0.016906/*** highest wind energy output is obtained with the wind turbine having a rated power of 2.5 MW at all considered sites with electricity costs not exceeding$0.02739/***,the outcomes of this study exhibit the potential of wind energy in Saudi Arabia,and its environmental goals can be acquired by harvesting wind energy.

关键词： Weibull distribution conventional numerical methods intelligent optimization algorithms wind resource exploration and exploitation cost of energy($/kWh)

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A Single State Sliding Mode Controller Design Using intelligent optimization algorithms for the Chaotic WINDMI System

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2022年第2期29卷 504-510页

作者： Cevher, Baris Gumus, Talha Enes Turan, Mustafa Emiroglu, Selcuk Yalcin, Mehmet Ali Sakarya Univ Fac Engn Elect & Elect Engn Esentepe Campus Serdivan Sakarya Turkey

In this study, a new single-state sliding mode controller is proposed that can be used in multi-input dynamic systems that require the system dynamics to be controlled by a single state variable. The proposed controller is compared with the single state sliding mode controller, which is effective according to a linear feedback controller and a passive controller. To demonstrate the effectiveness of the controllers, the model WINDMI, which is defined as a complex driven damped dynamic system, is preferred as an exemplary model. For the system's dynamics to reach reference in the shortest settling time with minimum overshoot, the sliding surface coefficient and the controller coefficients are optimized according to the sliding surface using Particle Swarm optimization, Genetic Algorithm, and Pattern Search algorithm. The best values of optimization coefficients are determined by comparing the obtained results. Finally, verifying the effectiveness and reliability of the proposed controller is demonstrated with simulations and numerical analysis.

关键词： Chaos Chaotic WINDMI System intelligent optimization algorithms Sliding Mode Controller

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Teaching Research and Practice of intelligent optimization algorithms nder the Background of ＂Artificial Intelligence + X＂

International Journal of Social Science and Education Resear...

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International Journal of Social Science and Education Research 2022年第9期5卷 325-329页

作者： Zheng Li Jinlei Qin

Based on the ＂Artificial Intelligence + X＂ compound professional talent training mode proposed by the Ministry of education, this paper focuses on the construction of knowledge system and the innovation of teaching method to the intelligent optimization algorithms course for relevant undergraduate majors. Specific solutions are proposed to some problems existing in the process of curriculum teaching, which will be beneficial to the construction of curriculum systematization from multiple angles.

关键词： Artificial Intelligence + X intelligent optimization algorithms Computational thinking Knowledge system reconstruction

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Hypothesis test based intelligent optimization algorithms and comparisons

Hypothesis test based intelligent optimization algorithms an...

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16th Chinese Control and Decision Conference

作者： Zhang Liang Wang Ling Zheng Da-zhang Tsing Hua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9787810545006

By incorporating hypothesis test into intelligent optimization algorithms, it can not only assure the exploration and exploitation of the search space, but also avoid repeated search to enhance efficiency by taking advantage of the statistical information obtained during evaluation process. Hypothesis test based SA and GA algorithms are reviewed and compared through simulation results on classic stochastic scheduling problems.

关键词： hypothesis test intelligent optimization algorithms stochastic optimization problems

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Applications Research of intelligent optimization algorithms in Traveling Sales Problem

Applications Research of Intelligent Optimization Algorithms...

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2018 International Conference on Data Processing,Artificial Intelligence,and Communications(DPAIC 2018)

作者： Guozhen Sang School of Network Security and Informatization Weinan Normal University

TSP has a strong engineering background and practical application value. However, the traditional solutions of TSP have many disadvantages. The common intelligent optimization algorithms include neural network algorithm, genetic algorithm, simulated annealing algorithm, ant colony algorithm and so on, which can be used to solve TSP problems. In this paper, the basic principles of the above algorithms to solve TSP problems are elaborated, and their advantages and disadvantages are analyzed to provide some references for relevant researchers.

关键词： intelligent optimization algorithms Traveling sales problem TSP

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