Optimization algorithm based on kinetic-molecular theory

Traditionally, the optimization algorithm based on physics principles has some shortcomings such as low population diversity and susceptibility to local extrema. A new optimization algorithm based on kinetic-molecular theory(KMTOA) is proposed. In the KMTOA three operators are designed: attraction, repulsion and wave. The attraction operator simulates the molecular attraction, with the molecules moving towards the optimal ones, which makes possible the optimization. The repulsion operator simulates the molecular repulsion, with the molecules diverging from the optimal ones. The wave operator simulates the thermal molecules moving irregularly, which enlarges the searching spaces and increases the population diversity and global searching ability. Experimental results indicate that KMTOA prevails over other algorithms in the robustness, solution quality, population diversity and convergence speed.

Time Complexity of Evolutionary Algorithms for Combinatorial Optimization:A Decade of Results

Computational time complexity analyzes of evolutionary algorithms （EAs） have been performed since the mid-nineties. The first results were related to very simple algorithms, such as the （1＋1）-EA, on toy problems. These efforts produced a deeper understanding of how EAs perform on different kinds of fitness landscapes and general mathematical tools that may be extended to the analysis of more complicated EAs on more realistic problems. In fact, in recent years, it has been possible to analyze the （1＋1）-EA on combinatorial optimization problems with practical applications and more realistic population-based EAs on structured toy problems. This paper presents a survey of the results obtained in the last decade along these two research lines. The most common mathematical techniques are introduced, the basic ideas behind them are discussed and their elective applications are highlighted. Solved problems that were still open are enumerated as are those still awaiting for a solution. New questions and problems arisen in the meantime are also considered.

Optimization of HMM Parameters Based on Chaos and Genetic Algorithm for Hand Gesture Recognition

In order to prevent standard genetic algorithm (SGA) from being premature, chaos is introduced into GA, thus forming chaotic anneal genetic algorithm (CAGA). Chaos ergodicity is used to initialize the population, and chaotic anneal mutation operator is used as the substitute for the mutation operator in SGA. CAGA is a unified framework of the existing chaotic mutation methods. To validate the proposed algorithm, three algorithms, i. e. Baum-Welch, SGA and CAGA, are compared on training hidden Markov model (HMM) to recognize the hand gestures. Experiments on twenty-six alphabetical gestures show the CAGA validity.

Method of Fire Image Identification Based on Optimization Theory

In view of some distinctive characteristics of the early-stage flame image, a corresponding method of characteristic extraction is presented. Also introduced is the application of the improved BP algorithm based on the optimization theory to identifying fire image characteristics. First the optimization of BP neural network adopting Levenberg-Marquardt algorithm with the property of quadratic convergence is discussed, and then a new system of fire image identification is devised. Plenty of experiments and field tests have proved that this system can detect the early-stage fire flame quickly and reliably.

Optimization Algorithm for Reduction the Size of Dixon Resultant Matrix:A Case Study on Mechanical Application

In the process of eliminating variables in a symbolic polynomial system,the extraneous factors are referred to the unwanted parameters of resulting polynomial.This paper aims at reducing the number of these factors via optimizing the size of Dixon matrix.An optimal configuration of Dixon matrix would lead to the enhancement of the process of computing the resultant which uses for solving polynomial systems.To do so,an optimization algorithm along with a number of new polynomials is introduced to replace the polynomials and implement a complexity analysis.Moreover,the monomial multipliers are optimally positioned to multiply each of the polynomials.Furthermore,through practical implementation and considering standard and mechanical examples the efficiency of the method is evaluated.

Optimization of Sentiment Analysis Using Teaching-Learning Based Algorithm

Feature selection and sentiment analysis are two common studies that are currently being conducted;consistent with the advancements in computing and growing the use of social media.High dimensional or large feature sets is a key issue in sentiment analysis as it can decrease the accuracy of sentiment classification and make it difficult to obtain the optimal subset of the features.Furthermore,most reviews from social media carry a lot of noise and irrelevant information.Therefore,this study proposes a new text-feature selection method that uses a combination of rough set theory(RST)and teaching-learning based optimization(TLBO),which is known as RSTLBO.The framework to develop the proposed RSTLBO includes numerous stages:(1)acquiring the standard datasets(user reviews of six major U.S.airlines)which are used to validate search result feature selection methods,(2)preprocessing of the dataset using text processing methods.This involves applying text processing methods from natural language processing techniques,combined with linguistic processing techniques to produce high classification results,(3)employing the RSTLBO method,and(4)using the selected features from the previous process for sentiment classification using the Support Vector Machine(SVM)technique.Results show an improvement in sentiment analysis when combining natural language processing with linguistic processing for text processing.More importantly,the proposed RSTLBO feature selection algorithm is able to produce an improved sentiment analysis.

Improving mobile mass monitoring in the IoT environment based on Fog computing using an improved forest optimization algorithm

In the IoT-based users monitor tasks in the network environment by participating in the data collection process by smart devices.Users monitor their data in the form of fog computing(mobile mass monitoring).Service providers are required to pay user rewards without increasing platform costs.One of the NP-Hard methods to maximise the coverage rate and reduce the platform costs(reward)is the Cooperative Based Method for Smart Sensing Tasks(CMST).This article uses chaos theory and fuzzy parameter setting in the forest optimisation algorithm.The proposed method is implemented with MATLAB.The average findings show that the network coverage rate is 31%and the monitoring cost is 11%optimised compared to the CMST scheme and the mapping of the mobile mass monitoring problem to meta-heuristic algorithms.And using the improved forest optimisation algorithm can reduce the costs of the mobile crowd monitoring platform and has a better coverage rate.

GENETIC ALGORITHMS AND GAME THEORY FOR HIGH LIFT DESIGN PROBLEMS IN AERODYNAMICS

A multi-objective evolutionary optimization method (combining genetic algorithms(GAs)and game theory(GT))is presented for high lift multi-airfoil systems in aerospace engineering.Due to large dimension global op-timization problems and the increasing importance of low cost distributed parallel environments,it is a natural idea to replace a globar optimization by decentralized local sub-optimizations using GT which introduces the notion of games associated to an optimization problem.The GT/GAs combined optimization method is used for recon-struction and optimization problems by high lift multi-air-foil desing.Numerical results are favorably compared with single global GAs.The method shows teh promising robustness and efficient parallel properties of coupled GAs with different game scenarios for future advanced multi-disciplinary aerospace techmologies.

Randomized Algorithms for Probabilistic Optimal Robust Performance Controller Design

Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach was given. The randomized algorithms here were based on a property from statistical learning theory known as (uniform) convergence of empirical means (UCEM). It is argued that in order to assess the performance of a controller as the plant varies over a pre-specified family, it is better to use the average performance of the controller as the objective function to be optimized, rather than its worst-case performance. The approach is illustrated to be efficient through an example.

Optimization of a Route Network in Dakar Airspace: Surface Navigation

In this paper, the map of a network of air routes was updated by removing the non-optimal routes and replacing them with the best ones. An integer linear programming model was developed. The aim was to find optimal routes in superspace based on performance-based navigation. The optimal routes were found from a DIJKSTRA algorithm that calculates the shortest path in a graph. Simulations with python language on real traffic areas showed the improvements brought by surface navigation. In this work, the conceptual phase and the upper airspace were studied.

Auto-optimization of production-injection rate for reservoirs with strong natural aquifer at ultra-high water cut stage

Based on the optimal control theory and taking the production law of reservoirs with strong natural aquifer as the basic constraint, a mathematical model of liquid production for such reservoirs in the later stage of development is established. The model is solved by improved simultaneous perturbation stochastic approximation algorithm(SPSA), and an automatic optimization software for liquid production is developed. This model avoids the disadvantage of traditional optimization methods that only focus on the maximum value of mathematics but ignore the production law of oilfield. It has the advantages of high efficiency of calculation, short period and automatic optimization. It can satisfy the automatic optimization of liquid production in later stage of oilfield development. The software was applied in the oilfield development of D oilfield, Ecuador in South America, and realized the automatic optimization of liquid production in the later stage of oilfield development.

Vertex Cover Optimization Using a Novel Graph Decomposition Approach

The minimum vertex cover problem(MVCP)is a well-known combinatorial optimization problem of graph theory.The MVCP is an NP(nondeterministic polynomial)complete problem and it has an exponential growing complexity with respect to the size of a graph.No algorithm exits till date that can exactly solve the problem in a deterministic polynomial time scale.However,several algorithms are proposed that solve the problem approximately in a short polynomial time scale.Such algorithms are useful for large size graphs,for which exact solution of MVCP is impossible with current computational resources.The MVCP has a wide range of applications in the fields like bioinformatics,biochemistry,circuit design,electrical engineering,data aggregation,networking,internet traffic monitoring,pattern recognition,marketing and franchising etc.This work aims to solve the MVCP approximately by a novel graph decomposition approach.The decomposition of the graph yields a subgraph that contains edges shared by triangular edge structures.A subgraph is covered to yield a subgraph that forms one or more Hamiltonian cycles or paths.In order to reduce complexity of the algorithm a new strategy is also proposed.The reduction strategy can be used for any algorithm solving MVCP.Based on the graph decomposition and the reduction strategy,two algorithms are formulated to approximately solve the MVCP.These algorithms are tested using well known standard benchmark graphs.The key feature of the results is a good approximate error ratio and improvement in optimum vertex cover values for few graphs.

Web-Based Synthetic Optimization Design System of Micro-Components

In order to meet the requirement of network synthesis optimization design for a micro component, a three-level information frame and functional module based on web was proposed. Firstly, the finite element method （FEM） was used to analyze the dynamic property of coupled-energy-domain of virtual prototype instances and to obtain some optimal information data. Secondly, the rough set theory （RST） and the genetic algorithm （GA） were used to work out the reduction of attributes and the acquisition of principle of optimality and to confirm key variable and restriction condition in the synthesis optimization design. Finally, the regression analysis （RA） and GA were used to establish the synthesis optimization design model and carry on the optimization design. A corresponding prototype system was also developed and the synthesis optimization design of a thermal actuated micro-pump was carded out as a demonstration in this paper.

Multi-objective optimization of crimping of large-diameter welding pipe

Crimping is widely adopted in the production of large-diameter submerged-arc welding pipes. Traditionally, designers obtain the technical parameters for crimping from experience or by trial and error through experiments and the finite element(FE) method. However, it is difficult to achieve ideal crimping quality by these approaches. To resolve this issue, crimping parameter design was investigated by multi-objective optimization. Crimping was simulated using the FE code ABAQUS and the FE model was validated experimentally. A welding pipe made of X80 high-strength pipeline steel was considered as a target object and the optimization problem for its crimping was formulated as a mathematical model and crimping was optimized. A response surface method based on the radial basis function was used to construct a surrogate model; the genetic algorithm NSGA-II was adopted to search for Pareto solutions; grey relational analysis was used to determine the most satisfactory solution from the Pareto solutions. The obtained optimal design of parameters shows good agreement with the initial design and remarkably improves the crimping quality. Thus, the results provide an effective approach for improving crimping quality and reducing design times.

新能源汽车电池回收网点竞争选址模型及算法

针对考虑排队论的新能源汽车电池回收网点竞争设施选址问题,提出一种改进的人类学习优化(IHLO)算法。首先,构建包含排队时间约束、容量约束和门槛约束等条件的新能源汽车电池回收网点竞争设施选址模型;然后,考虑到该问题属于NP-hard问题,针对人类学习优化(HLO)算法前期收敛速度较慢、寻优精度不够高、求解稳定性不够高的不足,通过引入精英种群反向学习策略、团队互助学习算子和调和参数自适应策略提出IHLO算法;最后,以上海市和长江三角洲为例进行数值实验,并将IHLO算法和改进二进制灰狼(IBGWO)算法、改进二进制粒子群(IBPSO)算法、HLO算法和融合学习心理学的人类学习优化(LPHLO)算法进行比较。大、中、小三种不同规模的实验结果表明,IHLO算法在15个指标中的14个指标上表现最优,IHLO算法比IBGWO算法求解精度至少提高了0.13%,求解稳定性至少提高了10.05%,求解速度至少提高了17.48%。所提算法具有较高的计算精度和优化速度,可有效解决竞争设施选址问题。

高拱坝时序多属性施工方案随机智能优化方法

为了解决当前施工方案优化方法大多忽略各关键节点施工进度指标的随机性对施工方案优化的影响,同时属性权重确定方法难以实现对高维度多层次的多属性决策问题进行整体优化的问题,提出了基于前景随机理论和麻雀搜索算法的高拱坝时序多属性施工方案随机智能优化方法。针对高拱坝工程建设特点,基于前景随机理论建立时序多属性随机智能优化模型,提出了基于阶段发展特征的动态参考点设置方法;基于麻雀搜索算法建立最优属性权重及时间权重搜索模型,并以差异最大化思想构造适应度函数,实现模型的求解。工程实例验证结果表明该优化方法具有合理性,优化结果与前景随机占优-CRITIC方法、随机占优方法优化结果一致,且具有更好的方案区分度。

基于d-q变换及WOA-LSTM的异步电机定子匝间短路故障诊断方法

为了实现对异步电机定子绕组匝间短路故障的可靠在线诊断,提出一种基于d-q变换及鲸鱼优化算法(WOA)优化的长短期记忆网络(LSTM)的故障诊断方法。通过理论推导可知,d-q变换可有效提取定子电流中的特征频谱数据。采用鲸鱼优化算法对长短期记忆网络中的3个关键参数进行优化,建立WOA-LSTM故障分类模型。为了验证基于d-q变换和WOA-LSTM故障诊断方法的有效性,分别以小波变换、快速傅里叶变换及d-q变换提取电流频谱数据作为输入数据集,以一台YE2-100L1-4型异步电机为实验对象进行实验验证。研究结果表明:相比于小波变换及快速傅里叶变换,采用d-q变换能更准确的提取出定子电流中的故障特征,更精确地反映电机故障状态,有助于提高故障分类准确率;相比于传统的LSTM算法,经WOA优化后的LSTM算法分类准确率可达98.3%,能可靠地实现不同程度匝间短路故障的诊断。

基于心理学理论的多策略生活选择算法

生活选择算法(Life Choice-Based Optimizer,LCBO)是根据人们在日常生活中做出不同的决策而设计的一种智能优化算法。但LCBO在解决高维函数优化问题时容易陷入局部最优,且收敛速度慢。因此,本文基于心理学理论提出一种多策略生活选择算法(Multi-Strategy Life Choice-Based Optimizer,MSLCBO)。首先,基于“贝勃规律”提出有策略的向优秀组学习,提高算法的局部搜索能力;其次,在算法迭代后期受“关系场”理论启发,提出精英交流机制对质量较好的解进行搜索,进一步增强算法局部开发能力,提高算法的优化速度;最后,为避免“投射效应”的心理学效应影响,引入基于折射反向学习策略,从而提升算法的全局搜索能力。对改进后的算法进行2次对比实验:将MSLCBO与其他7种智能优化算法在16个基准测试函数上进行了对比,结果表明MSLCBO性能优势显著;并采用工程实际应用问题中的三杆桁架设计问题进行测试,同样验证了MSLCBO的有效性。

基于正向解析式和多目标博弈优化算法的复杂装备体系优化设计方法

针对复杂装备体系(Complex Equipment System-of-systems,CES)优化设计中指标变量多、仿真依赖性强、易陷入局部最优的问题,提出一种基于正向解析式和多目标博弈理论(Multi-Objective Game Theory,MOGT)优化算法的CES优化设计方法。为提升CES优化设计的可解释性,构建任务级—能力级—装备级的评估指标体系;在此基础上,基于装备机理和效用函数表征装备评估指标与作战能力之间的正向映射关系,并利用相邻优属度熵权法计算各指标权重;通过正向解析式与约束条件建立多目标优化模型,并采用MOGT优化算法获得最佳优化结果。以某作战推演平台中防空攻防想定为例,开展算例评估与验证分析。研究结果表明,该方法能够实现CES中最优设计方案的求解,可显著提高设计效率和降低设计成本,为下一代装备发展论证、设计评估和作战试验提供了基础性工作。