Non-parametric particle swarm optimization for global optimization

In recent years, particle swarm optimization (PSO) has extensively applied in various optimization problems because of its simple structure. Although the PSO may find local optima or exhibit slow convergence speed when solving complex multimodal problems. Also, the algorithm requires setting several parameters, and tuning the parameters is a challenging for some optimization problems. To address these issues, an improved PSO scheme is proposed in this study. The algorithm, called non-parametric particle swarm optimization (NP-PSO) enhances the global exploration and the local exploitation in PSO without tuning any algorithmic parameter. NP-PSO combines local and global topologies with two quadratic interpolation operations to increase the search ability. Nineteen (19) unimodal and multimodal nonlinear benchmark functions are selected to compare the performance of NP-PSO with several well-known PSO algorithms. The experimental results showed that the proposed method considerably enhances the efficiency of PSO algorithm in terms of solution accuracy, convergence speed, global optimality, and algorithm reliability.     

基于迁出地动态选择与自适应迁入策略的BBO算法

Dan Simon用生物地理学的方法和机制来解决工程优化问题,提出了生物地理学优化算法(Biogeography-Based Optimization,BBO)。该算法因其独特的搜索机制和较好的性能在智能优化算法领域得到了广泛的关注。为了进一步提高生物地理学优化算法的全局和局部收索能力,提出了一种基于动态选择迁出地与混合自适应迁入的优化策略,对生物地理学优化算法进行改进,形成一种新的改进型BBO算法。该算法根据进化阶段动态选择待迁出地,并综合当前迁出地和随机迁出地优化迁入策略;同时,设计与适应度相关的变异机制,以增加算法的全局搜索能力。仿真实验结果表明,该算法在全局搜索、收敛速度和收敛精度上均优于对比算法。     

基于衰减因子和动态学习的改进樽海鞘群算法

樽海鞘群算法是一种新型的群智能优化算法.与其他智能优化算法相比,樽海鞘群算法的优化求解策略仍有待改进,以进一步提高该算法的求解精度和寻优效率.本文提出一种基于衰减因子和动态学习的改进樽海鞘群算法,通过在领导者更新阶段添加衰减因子,提高算法的局部开发能力,在跟随者更新阶段引入动态学习策略,提高算法的全局搜索能力.本文对16个测试函数进行实验,将提出的改进算法与其他智能优化算法比较,实验结果表明,本文提出的改进算法在收敛精度和收敛速度方面有较大提升,具有良好的优化性能.     

融合动态反向学习的阿奎拉鹰与哈里斯鹰混合优化算法

阿奎拉鹰优化算法(Aquila optimizer, AO)和哈里斯鹰优化算法(Harris hawks optimization, HHO)是近年提出的优化算法。AO算法全局寻优能力强,但收敛精度低,容易陷入局部最优,而HHO算法具有较强的局部开发能力,但存在全局探索能力弱,收敛速度慢的缺陷。针对原始算法存在的局限性,本文将两种算法混合并引入动态反向学习策略,提出一种融合动态反向学习的阿奎拉鹰与哈里斯鹰混合优化算法。首先,在初始化阶段引入动态反向学习策略提升混合算法初始化性能与收敛速度。此外,混合算法分别保留了AO的探索机制与HHO的开发机制,提高算法的寻优能力。仿真实验采用23个基准测试函数和2个工程设计问题测试混合算法优化性能,并对比了几种经典反向学习策略,结果表明引入动态反向学习的混合算法收敛性能更佳,能够有效求解工程设计问题。     

引入莱维飞行与动态权重的改进灰狼算法

针对求解复杂优化问题时,灰狼（GWO）算法存在全局搜索能力不足、容易陷入局部最优值等问题,提出一种引入莱维飞行与动态权重策略的改进灰狼算法（LGWO）。基于Singer混沌映射初始化灰狼个体位置,增加种群多样性;收敛因子采用新的非线性更新策略,在种群迭代全期平衡全局搜索与局部搜索能力;在种群位置更新公式引入莱维飞行与动态权重策略,增加种群跳出局部最优值的概率,提升寻优准确度。通过8个基准函数的测试,并与其他优化算法和改进算法进行对比,LGWO取得了最优的收敛速度与预测精度,并验证了LGWO算法优化高维复杂问题的有效性。     

基于局部搜索的反向学习竞争粒子群优化算法

为提升粒子群优化算法在复杂优化问题,特别是高维优化问题上的优化性能,提出一种基于Solis&Wets局部搜索的反向学习竞争粒子群优化算法(solis and wets-opposition based learning competitive particle swarm optimizer with local search, SW-OBLCSO). SW-OBLCSO算法采用竞争学习和反向学习两种学习机制,并设计了基于个体的局部搜索算子.利用10个常用基准测试函数和12个带有偏移旋转的复杂测试函数,在不同维度情况下将SW-OBLCSO算法与多种优化算法进行对比.实验结果表明,所提出算法在收敛速度和全局搜索能力上表现出突出的性能.对模糊认知图(fuzzy cognitive maps)学习问题的测试表明, SW-OBLCSO算法在处理实际问题时同样具有出色的性能.     

多阶段动态扰动和动态惯性权重的布谷鸟算法

针对布谷鸟仿生智能优化算法存在着的易陷入局部最优、求解精度低以及收敛速度慢等问题,提出了基于多阶段动态扰动和动态惯性权重的布谷鸟搜索算法（MACS）。利用多阶段动态扰动策略对布谷鸟算法的全局位置的最优鸟巢位置根据方差可调的正态随机分布进行扰动,有利于增加种群的多样性和鸟窝位置的灵活性,提高算法全局搜索能力。在局部位置处引入动态惯性权重,使得算法有效克服易陷入局部最优的缺陷,提高局部寻优搜索能力。引入了动态切换概率[p]代替固定概率,可以动态平衡全局搜索和局部搜索。通过与4种算法相比和11个测试函数的仿真结果表明：改进布谷鸟算法（MACS）的寻优性能明显提高,收敛速度更快,求解精度更高,具有更强的全局搜索能力和跳出局部最优能力。     

Efficient linear discriminant analysis with locality preserving for face recognition

Linear discriminant analysis (LDA) is one of the most popular techniques for extracting features in face recognition. LDA captures the global geometric structure. However, local geometric structure has recently been shown to be effective for face recognition. In this paper, we propose a novel feature extraction algorithm which integrates both global and local geometric structures. We first cast LDA as a least square problem based on the spectral regression, then regularization technique is used to model the global and local geometric structures. Furthermore, we impose penalty on parameters to tackle the singularity problem and design an efficient model selection algorithm to choose the optimal tuning parameter which balances the tradeoff between the global and local structures. Experimental results on four well-known face data sets show that the proposed integration framework is competitive with traditional face recognition algorithms, which use either global or local structure only.     

Flexible job-shop scheduling with tolerated time interval and limited starting time interval based on hybrid discrete PSO-SA: An application from a casting workshop

In this paper, we addressed two significant characteristics in practical casting production, namely tolerated time interval (TTI) and limited starting time interval (LimSTI). With the consideration of TTI and LimSTI, a multi-objective flexible job-shop scheduling model is constructed to minimize total overtime of TTI, total tardiness and maximum completion time. To solve this model, we present a hybrid discrete particle swarm optimization integrated with simulated annealing (HDPSO-SA) algorithm which is decomposed into global and local search phases. The global search engine based on discrete particle swarm optimization includes two enhancements: a new initialization method to improve the quality of initial population and a novel gBest selection approach based on extreme difference to speed up the convergence of algorithm. The local search engine is based on simulated annealing algorithm, where four neighborhood structures are designed under two different local search strategies to help the proposed algorithm jump over the trap of local optimal solution. Finally, computational results of a real-world case and simulation data expanded from benchmark problems indicate that our proposed algorithm is significant in terms of the quality of non-dominated solutions compared to other algorithms.     

一种求解动态优化问题的免疫文化基因算法

针对传统免疫网络动态优化算法局部寻优能力弱、寻优精度低及易早熟收敛的缺点,提出一种求解动态优化问题的免疫文化基因算法。基于文化基因算法基本框架,将人工免疫网络算法作为全局搜索算法,采用禁忌搜索算法作为局部搜索算子;同时引入柯西变异加强算法的全局搜索能力,并有效防止早熟收敛。通过对经典动态优化函数测试集在相同条件下的实验表明,该免疫文化基因算法相较于其他同类算法具有较好的搜索精度和收敛速度。     

Dynamic deployment optimization of near space communication system using a novel estimation of distribution algorithm

A robust deployment of the airship platforms is crucial to the performance of the Near Space Communication System (NSCS) in the dynamic environment. In this paper, a multiobjective NSCS deployment optimization model with multi-phased periodic user distribution is proposed. To optimize this model, we propose a local incremental estimation of distribution algorithm with an asymmetrical domination relationship within the multiobjective evolutionary algorithm based on decomposition framework. The conflict between the selection mechanism and the domination relationship is also analyzed theoretically for the first time. To obtain robust solutions under this conflict, the local distribution information of a certain subproblem within several generations is encompassed into a local incremental distribution model. As a generalized form of the existing domination relationship, an asymmetrical domination relationship (ADR), which treats the current and past objective values differently, is proposed to select robust solutions. The proposed algorithm is also tested on four designed problems compared with another four popular algorithms and proves its superiority. Some important parameters are also investigated in the experiments and some guidelines on tuning these parameters are given as well.     

提高海鸥优化算法寻优能力的改进策略及其应用

针对海鸥优化算法(SOA)收敛速度慢、容易陷入局部最优等问题,提出3种提高SOA算法寻优能力的改进策略：对非线性收敛因子与螺旋系数进行改进,以改善全局与局部搜索的协调能力,加快收敛速度;通过拓展攻击行为与攻击角度,以并行搜索的方式提升局部寻优性能;引入动态反向学习,使算法快速跳出局部最优,优化全局搜索。基于马尔可夫过程分析了改进海鸥优化算法(ISOA)的收敛性。通过16个基准函数测试了ISOA算法的寻优性能,并将其应用于PID(proportional-integral-derivative)参数整定中,结果表明,提出的改进策略能显著提高SOA算法的收敛速度与求解精度,ISOA算法在参数优化领域具有较好的应用效果。     

A diversity control mechanism in many objective optimizations

Multi-objective evolutionary optimization algorithms are among the best optimizers for solving problems in control systems, engineering and industrial planning. The performance of these algorithms degrades severely due to the loss of selection pressure exerted by the Pareto dominance relation which will cause the algorithm to act randomly. Various recent methods tried to provide more selection pressure but this would cause the population to converge to a specific region which is not desirable. Diversity reduction in high dimensional problems which decreases the capabilities of these approaches is a decisive factor in the overall performance of these algorithms. The novelty of this paper is to propose a new diversity measure and a diversity control mechanism which can be used in combination to remedy the mentioned problem. This measure is based on shortest Hamiltonian path for capturing an order of the population in any dimension. In order to control the diversity of population, we designed an adaptive framework which adjusts the selection operator according to diversity variation in the population using different diversity measures as well as our proposed one. This study incorporates the proposed framework in MOEA/D, an efficient widely used evolutionary algorithm. The obtained results validate the motivation on the basis of diversity and performance measures in comparison with the state-of-the-art algorithms and demonstrate the applicability of our algorithm/method in handling many-objective problems. Moreover, an extensive comparison with several diversity measure algorithms reveals the competitiveness of our proposed measure.     

自适应动态邻域布谷鸟混合算法求解TSP问题

针对离散布谷鸟算法求解旅行商问题时邻域搜索效率低和易陷入局部最优解等问题,提出了一种自适应动态邻域布谷鸟混合算法（Adaptive Dynamic Neighborhood Hybrid Cuckoo Search algorithm,ADNHCS）。为了提升邻域搜索效率,设计了一种圆限定突变的动态邻域结构来降低经典算法的随机性;此外,提出了可根据迭代过程进行自适应参数调整的策略,并结合禁忌搜索算法来提升全局寻优的能力。使用MATLAB和标准TSPLIB数据库中的若干经典算例对算法性能进行了实验仿真,结果表明与其他基于布谷鸟算法、经典和新型群智能优化算法相比,ADNHCS算法在全局寻优能力以及稳定性方面表现更优。     

基于混合行为的蚁群算法

针对基本蚁群算法易出现停滞、收敛速度慢的问题,在最大最小蚁群算法的基础上提出了一种基于混合行为的蚁群(HBAC)算法,通过引入停止蚂蚁来构造局部路线方式和增加全局调优策略,提高了算法的搜索能力和收敛速度,同时将蚂蚁所寻找的各条路径的信息素限定在一个可动态调整的范围之内,避免了算法过早陷于局部最优解.通过HBAC算法同其他蚁群算法在求解旅行商问题上的实验比较,发现该算法拥有较快的收敛速度,提高了全局最优解搜索能力,在性能上有了较大的提高.     

A new effective operator for the hybrid algorithm for solving global optimisation problems

Hybrid algorithms have been recently used to solve complex single-objective optimisation problems. The ultimate goal is to find an optimised global solution by using these algorithms. Based on the existing algorithms (HP_CRO, PSO, RCCRO), this study proposes a new hybrid algorithm called MPC (Mean-PSO-CRO), which utilises a new Mean-Search Operator. By employing this new operator, the proposed algorithm improves the search ability on areas of the solution space that the other operators of previous algorithms do not explore. Specifically, the Mean-Search Operator helps find the better solutions in comparison with other algorithms. Moreover, the authors have proposed two parameters for balancing local and global search and between various types of local search, as well. In addition, three versions of this operator, which use different constraints, are introduced. The experimental results on 23 benchmark functions, which are used in previous works, show that our framework can find better optimal or close-to-optimal solutions with faster convergence speed for most of the benchmark functions, especially the high-dimensional functions. Thus, the proposed algorithm is more effective in solving single-objective optimisation problems than the other existing algorithms.     

A space search optimization algorithm with accelerated convergence strategies

Evolutionary algorithms (EAs), which have been widely used to solve various scientific and engineering optimization problems, are essentially stochastic search algorithms operating in the overall solution space. However, such random search mechanism may lead to some disadvantages such as a long computing time and premature convergence. In this study, we propose a space search optimization algorithm (SSOA) with accelerated convergence strategies to alleviate the drawbacks of the purely random search mechanism. The overall framework of the SSOA involves three main search mechanisms: local space search, global space search, and opposition-based search. The local space search that aims to form new solutions approaching the local optimum is realized based on the concept of augmented simplex method, which exhibits significant search abilities realized in some local space. The global space search is completed by Cauchy searching, where the approach itself is based on the Cauchy mutation. This operation can help the method avoid of being trapped in local optima and in this way alleviate premature convergence. An opposition-based search is exploited to accelerate the convergence of space search. This operator can effectively reduce a substantial computational overhead encountered in evolutionary algorithms (EAs). With the use of them SSOA realizes an effective search process. To evaluate the performance of the method, the proposed SSOA is contrasted with a method of differential evolution (DE), which is a well-known space concept-based evolutionary algorithm. When tested against benchmark functions, the SSOA exhibits a competitive performance vis-a-vis performance of some other competitive schemes of differential evolution in terms of accuracy and speed of convergence, especially in case of high-dimensional continuous optimization problems.     

免疫克隆多目标优化算法求解约束优化问题

针对现有的约束处理技术的一些不足之处,提出一种用于求解约束优化问题的算法——免疫克隆多目标优化算法(immune clonal multi-objective optimization algorithm,简称ICMOA).算法的主要特点是通过将约束条件转化为一个目标,从而将问题转化为两个目标的多目标优化问题.引入多目标优化中的Pareto-支配的概念,每一个个体根据其被支配的程度进行克隆、变异及选择等操作.克隆操作实现了全局择优,有利于得到高质量的解;变异操作提高算法的局部搜索能力,有利于所得解的多样性;选择操作有利于算法向着最优搜索,而且加快了收敛速度.基于抗体群的随机状态转移过程,证明该算法具有全局收敛性.通过对13个标准测试问题的测试,并与已有算法进行比较。结果表明,该算法在收敛速度和求解精度上均具有一定的优势.     

基于改进混合蜂群算法的非线性电路谐波平衡分析

针对射频电路非线性分析中谐波平衡方程求解问题,提出一种基于高斯扰动、锦标赛选择策略以及拟牛顿局部寻优算子的改进混合蜂群算法,该算法在搜索方程中引入基于当前全局最优解的高斯扰动,能有效防止算法陷入局部最优并加快算法收敛;跟随蜂采用锦标赛选择策略在一定程度上避免了算法的早熟现象;采用拟牛顿算子进行局部寻优,可使算法快速收敛。实验结果表明,改进混合蜂群算法成功应用于谐波平衡方程求解,与其他求解算法对比,收敛时间较短,性能较优。     

Biogeography-based optimisation with chaos

The biogeography-based optimisation (BBO) algorithm is a novel evolutionary algorithm inspired by biogeography. Similarly, to other evolutionary algorithms, entrapment in local optima and slow convergence speed are two probable problems it encounters in solving challenging real problems. Due to the novelty of this algorithm, however, there is little in the literature regarding alleviating these two problems. Chaotic maps are one of the best methods to improve the performance of evolutionary algorithms in terms of both local optima avoidance and convergence speed. In this study, we utilise ten chaotic maps to enhance the performance of the BBO algorithm. The chaotic maps are employed to define selection, emigration, and mutation probabilities. The proposed chaotic BBO algorithms are benchmarked on ten test functions. The results demonstrate that the chaotic maps (especially Gauss/mouse map) are able to significantly boost the performance of BBO. In addition, the results show that the combination of chaotic selection and emigration operators results in the highest performance.