改进海洋捕食者算法和插值平滑的机器人路径规划

鉴于传统的智能优化算法（intelligent optimization algorithm,IOA）不能很好解决插值平滑的机器人路径规划（robot path planning,RPP）问题,以及最近提出的海洋捕食者算法（marine predators algorithm,MPA）的优势和不足,提出了一种改进的MPA,即提升信息交流的MPA（interchange enhanced MPA,IEMPA）,用于解决RPP。首先提出一种融合趋向全局最优的反向学习策略用于随机选择的一个捕食者的位置更新,以便降低陷于局部最优的概率;然后提出了一种三阶段最优引导最差策略来强化最差个体以便提升整个群体和提高搜索能力;随后,提出一种信息共享策略用于捕食前期以进一步提高算法的搜索能力;最后将IEMPA用于插值平滑的RPP中。大量的多场景RPP问题的优化实验结果表明,与MPA等优秀算法相比,IEMPA搜索能力更强、精度更高、收敛速度更快,能更好地处理RPP,可应用到在其他复杂优化问题上。     

改进的混合蛙跳算法及其在多阈值图像分割中的应用

针对混合蛙跳算法(Shuffled Frog Leaping Algorithm,SFLA)存在的计算复杂度高、优化效率不理想等问题,提出了一种改进的混合蛙跳算法(Improved Shuffled Frog Leaping Algorithm,ISFLA)。在原始 SFLA的基础上进行如下改进:首先,将其中每次只更新组内最差青蛙的方式改为更新组内所有青蛙的方式,这既增大了获得优质解的概率,又省去了调整组内迭代次数的步骤,从而提升了优化效率和可操作性;其次,将基于局部最优更新的方法和基于全局最优更新的方法融合为一种混合扰动更新方法,从而避免了复杂条件的选择步骤,进一步提升了优化效率;最后,去掉随机更新方式,以免优质解被破坏,从而提高了整体的优化性能。将 ISFLA 用于 CEC2005和CEC2015连续基准函数的优化测试和基于Renyi 熵的灰度和彩色图像分割的多阈值选择实验中,结果表明,与 SFLA 和state-of-the-art的LSFLA 相比,ISFLA 具有更高的优化效率,更适用于多阈值图像分割的阈值选择。     

离散Jaya算法的复杂网络社区发现

社区结构是复杂网络的重要特性之一, 基于模块度的复杂网络社区发现问题是一个NP难度的组合优化问题, 常用启发式算法求解. 最近出现的Jaya算法是求解连续优化问题的一种简单有效的元启发式方法. 本文在遵循Jaya算法按靠近最好解、远离最差解的方式更新种群个体的基础上, 针对复杂网络社区发现问题给出了Jaya算法离散化的策略, 提出一种复杂网络社区发现的离散Jaya算法. 实验表明, 在几个典型真实网络实例和一类人造网络实例上, 与几个经典算法和元启发式算法相比, 本文算法具有求解精度高、能自动确定社区数目等优点.     

A fast technique for image segmentation based on two Meta-heuristic algorithms

Image segmentation is a primary task in image processing which is widely used in object detection and recognition. Multilevel thresholding is one of the prominent technique in the field of image segmentation. However, the computational cost of multilevel thresholding increases exponentially as the number of threshold value increases, which leads to use of meta-heuristic optimization to find the optimal number of threshold. To overcome this problem, this paper investigates the ability of two nature-inspired algorithms namely: antlion optimisation (ALO) and multiverse optimization (MVO). ALO is a population-based method and mimics the hunting behaviour of antlions in nature. Whereas, MVO is based on the multiverse theory which depicts that there is over one universe exist. These two metaheuristic algorithms are used to find the optimal threshold values using Kapur’s entropy and Otsu’s between class variance function. They examine the outcomes of the proposed algorithm with other evolutionary algorithms based on cost value, stability analysis, feature similarity index (FSIM), structural similarity index (SSIM), peak signal to noise ratio (PSNR), computational time. We also provide Wilcoxon test which justify the response of these parameters. The experimental results showed that the proposed algorithm gives better results than other existing methods. It is noticed that MVO is faster than other algorithms. The proposed method is also tested on medical images to detect the tumor from MRI T1-weighted contrast-enhanced brain images.

     

基于改进布谷鸟优化的模糊聚类图像分割

模糊C均值聚类算法(FCM)是一种应用非常广泛的聚类算法,但是它受初始聚类中心影响较大,容易陷入局部最优。 在标准布谷鸟算法(CS)的基础上 提出改进布谷鸟优化算法(ICS),将发现概率P由固定值转变成随迭代次数逐渐减小的变量,这样不仅可以提高搜索种群的质量,而且保证了算法的收敛。因此,可以将改进布谷鸟优化算法用于FCM算法聚类中心生成的过程(ICS_FCM),从而有效地避免FCM陷入局部最优。改进的算法具有良好的聚类效果和运行速度。实现基于改进布谷鸟优化的FCM图像分割,并与基于模拟退火的FCM算法(SA_FCM)进行对比。由实验结果可知,该算法(ICS_FCM)不仅取得了较好的分割效果,效率上也有明显的提高。     

Hybrid whale optimization algorithm enhanced with Lévy flight and differential evolution for job shop scheduling problems

The job shop scheduling problem (JSSP) has been a hot issue in manufacturing. For the past few decades, scholars have been attracted to research JSSP and proposed many novel meta-heuristic algorithms to solve it. Whale optimization algorithm (WOA) is such a novel meta-heuristic algorithm and has been proven to be efficient in solving real-world optimization problems in the literature. This paper proposes a hybrid WOA enhanced with Lévy flight and differential evolution (WOA-LFDE) to solve JSSP. By changing the expression of Lévy flight and DE search strategy, Lévy flight enhances the abilities of global search and convergence of WOA in iteration, while DE algorithm improves the exploitation and local search capabilities of WOA and keeps the diversity of solutions to escape local optima. It is then applied to solve 88 JSSP benchmark instances and compared with other state-of-art algorithms. The experimental results and statistical analysis show that the proposed algorithm has superior performance over contesting algorithms.     

群智能算法优化的结合熵的最大类间方差法与脉冲耦合神经网络融合的图像分割算法

针对最大类间方差准则下的图像分割结果携带原图信息量不足、实时性差和脉冲耦合神经网络（PCNN）模型中循环迭代次数难以确定的问题,提出了群智能算法优化的结合熵的最大类间方差法（OTSU-H）与PCNN融合的自动图像分割算法。首先,充分利用图像的灰度分布信息和相关信息,将图像信息中冗余度、竞争性以及互补性有效地融合,构造二维和三维观测空间,提出了OTSU-H准则的快速递归算法;其次,将快速递推算法的目标函数分别作为布谷鸟搜索（CS）算法、萤火虫算法（FA）、粒子群优化（PSO）算法和遗传算法（GA）四种群智能算法的适应度函数;最后,将优化之后的OTSU-H引入PCNN模型中自动获取循环迭代次数。实验结果表明,与原始的最大类间方差法（OTSU）、最大熵准则以及基于图论分割、像素的聚类分割和候选区域语义分割的图像分割算法相比,所提算法具有较好的图像分割效果,同时降低了计算复杂度,节约了计算机的存储空间,具有较强的抗噪能力。所提算法时间损耗少、不需要训练的特性使得算法的运用范围较广。     

基于动态邻居拓扑结构的PSO算法

粒子群优化(PSO)算法在求解复杂的多峰问题时极易陷入局部最优解,通过分析种群多样性与局部最优解间的关系,提出一种基于动态邻居拓扑结构的粒子群算法。该算法在运行过程中,每间隔若干代,根据粒子间的距离更新每个粒子的邻居,该策略增加种群的多样性,进而提升粒子跳出局部最优解的能力。实验结果表明,该算法比其他PSO算法具有更好的性能。     

Adaptive computational chemotaxis based on field in bacterial foraging optimization

Bacterial foraging optimization (BFO) is predominately used to find solutions for real-world problems. One of the major characteristics of BFO is the chemotactic movement of a virtual bacterium that models a trial solution of the problems. It is pointed out that the chemotaxis employed by classical BFO usually results in sustained oscillation, especially on rough fitness landscapes, when a bacterium cell is close to the optima. In this paper we propose a novel adaptive computational chemotaxis based on the concept of field, in order to accelerate the convergence speed of the group of bacteria near the tolerance. Firstly, a simple scheme is designed for adapting the chemotactic step size of each field. Then, the scheme chooses the fields which perform better to boost further the convergence speed. Empirical simulations over several numerical benchmarks demonstrate that BFO with adaptive chemotactic operators based on field has better convergence behavior, as compared against other meta-heuristic algorithms.     

Probabilistic stopping rules for GRASP heuristics and extensions

The main drawback of most metaheuristics is the absence of effective stopping criteria. Most implementations of such algorithms stop after performing a given maximum number of iterations or a given maximum number of consecutive iterations without improvement in the best‐known solution value, or after the stabilization of the set of elite solutions found along the search. We propose effective probabilistic stopping rules for randomized metaheuristics such as GRASP (Greedy Randomized Adaptive Search Procedures). We show how the probability density function of the solution values obtained along the iterations of such algorithms can be used to implement stopping rules based on the tradeoff between solution quality and the time needed to find a solution that might improve the best solution found. We show experimentally that, in the particular case of GRASP heuristics, the solution values obtained along its iterations fit a normal distribution that may be used to give an online estimation of the number of solutions obtained in forthcoming iterations that might be at least as good as the incumbent. This estimation is used to validate the stopping rule based on the tradeoff between solution quality and the time needed to find a solution that might improve the incumbent. The robustness of this strategy is illustrated and validated by a thorough computational study reporting results obtained with GRASP implementations to four different combinatorial optimization problems.     

一种采用完全学习策略的量子行为粒子群优化算法

为了进一步提高量子行为粒子群优化（QPSO）算法的全局收敛性能,有效改善算法中存在的粒子早熟问题提出一种基于完全学习策略的改进QPSO算法（CLQPSO）.该学习策略改变了QPSO中局部吸引子的更新方式,充分利用了种群的社会信息.采用8个测试函数对算法性能进行比较分析.实验结果表明,所提出的改进算法不仅收敛速度快,而且全局收敛能力好,收敛精度优于PSO算法和QPSO算法.     

差分扰动的堆优化算法

针对堆优化算法（HBO）在解决复杂问题时存在搜索能力不足和搜索效率低等缺陷,提出一种差分扰动的HBO——DDHBO。首先,提出一种随机差分扰动策略更新最优个体的位置,以解决HBO没有对其更新从而导致的搜索效率低的问题;其次,使用一种最优最差差分扰动策略更新最差个体的位置,以强化其搜索能力;然后,采用一种多层差分扰动策略更新一般个体的位置,以强化多层个体之间的信息交流,并提高搜索能力;最后,针对原更新模型在搜索初期获得有效解概率低的问题,提出一种基于维的差分扰动策略更新其他个体的位置。在大量CEC2017复杂函数上的实验结果表明,与HBO相比,DDHBO在96.67%的函数上具有更好的优化性能,更少的平均运行时间（3.445 0 s）;与WRBBO（Worst opposition learning and Random-scaled differential mutation Biogeography-Based Optimization）、DEBBO（Differential Evolution and Biogeography-Based Optimization）和HGWOP（Hybrid PSO and Grey Wolf Optimizer）等先进算法相比,DDHBO也具有显著的优势。     

动态权重的蝙蝠算法在图像分割中的应用研究

传统的最小交叉熵阈值分割法（MCET）采用穷举的搜索形式，存在计算复杂度大、分割效率低的缺点，在很大程度上限制了该方法的应用。针对最小交叉熵分割法存在的不足，提出采用改进蝙蝠算法（BA）来搜索阈值的最优解。对BA算法中的权重参数做自适应调整，将随着迭代次数变化而变化的时变惯性权重策略应用于BA算法更新公式，给出三种不同的改进策略解决原始BA算法在靠近最优解时收敛速度下降的问题。将改进后的最优BA算法（IBA）应用于最小交叉熵多阈值图像分割中，与基本BA算法、改进的粒子群优化算法（IPSO）、模糊聚类方法（FC）三种方法进行对比性实验。实验结果表明，提出的IBA算法运算速度和分割精度效果明显提升。     

Multi-objective and multi-level image thresholding based on dominance and diversity criteria

The Kapur and Otsu methods are widely used image thresholding approaches and they are very efficient in bi-level thresholding applications. Evolutionary algorithms have been developed to extend the Kapur and Otsu methods to the multi-level thresholding case. However, there remains an unsolved argument that neither Kapur nor Otsu objective can optimally fit diverse content contained in different kinds of images. This paper proposes a multi-objective model which seeks to find the Pareto-optimal set with respect to Kapur and Otsu objectives. Based on dominance and diversity criteria, we developed a hybrid multi-objective particle swarm optimization (MOPSO) method by incorporating several intelligent search strategies. The ensemble strategy is also applied to automatically select the best search strategy to perform at various algorithm stages according to its historic performances. The experimental result shows that the solutions to our multi-objective model consistently produce equal or better segmentation results than those by the optimal solutions to the original Kapur and Otsu models, and that the proposed hybrid algorithm with and without the ensemble strategy produces a better approximation to the ideal Pareto front than those obtained by two other MOPSO variants and the MOEA/D. In comparison with the most recent multilevel thresholding methods, our approach also consistently obtains better performance in the segmentation result for several benchmark images.     

Non-linear diffusion of image noise with minimal iterativity

Non-linear diffusion (ND) is an iterative difference equation used in several image processing applications such as denoising, segmentation, or compression. The number of iterations required to achieve optimal processing can be very high, making ND not suitable for real-time requirements. In this paper, we study how to reduce complexity of ND so as to achieve minimal number of iterations for real-time image denoising. To do this, we first study the relations between parameters of the iterative equation: the number of iterations, the time step, and the edge strength. We then proceed by estimating the minimally required number of iterations to achieve effective denoising. Then, we relate the edge strength to the number of iterations, to noise, and to the image structure. The resulted minimal iterativity ND is very fast, while still achieves similar or better noise reduction compared to related ND work. This paper also shows how the proposed spatial filter is suitable for structure-sensitive object segmentation and temporal noise reduction.     

求解非线性方程组的量子行为粒子群算法*

介绍了利用量子行为粒子群算法解决非线性方程组的问题。求方程组的解归结为一个最优化问题,当方程组有多个解时,它的适应值函数就是具有多个最优解的多峰函数。为此,引进一种物种形成原理算法,该算法根据群体微粒的相似度并行地分成子群体。每个子群体是围绕一个群体种子而建立的。对每个子群体进行QPSO最优搜索,从而保证方程组中每个可能的解都能被搜索到,具有良好的局部寻优特性。对几个重要的测试函数进行仿真实验,结果证明了所用算法可以保证找到方程组所有的解,并且具有很好的精确度。     

Multimodal optimization using a bi-objective evolutionary algorithm

In a multimodal optimization task, the main purpose is to find multiple optimal solutions (global and local), so that the user can have better knowledge about different optimal solutions in the search space and as and when needed, the current solution may be switched to another suitable optimum solution. To this end, evolutionary optimization algorithms (EA) stand as viable methodologies mainly due to their ability to find and capture multiple solutions within a population in a single simulation run. With the preselection method suggested in 1970, there has been a steady suggestion of new algorithms. Most of these methodologies employed a niching scheme in an existing single-objective evolutionary algorithm framework so that similar solutions in a population are deemphasized in order to focus and maintain multiple distant yet near-optimal solutions. In this paper, we use a completely different strategy in which the single-objective multimodal optimization problem is converted into a suitable bi-objective optimization problem so that all optimal solutions become members of the resulting weak Pareto-optimal set. With the modified definitions of domination and different formulations of an artificially created additional objective function, we present successful results on problems with as large as 500 optima. Most past multimodal EA studies considered problems having only a few variables. In this paper, we have solved up to 16-variable test problems having as many as 48 optimal solutions and for the first time suggested multimodal constrained test problems which are scalable in terms of number of optima, constraints, and variables. The concept of using bi-objective optimization for solving single-objective multimodal optimization problems seems novel and interesting, and more importantly opens up further avenues for research and application.     

求解非线性方程组的量子行为粒子群算法

介绍了利用量子行为粒子群算法解决非线性方程组的问题.求方程组的解归结为一个最优化问题,当方程组有多个解时,它的适应值函数就是具有多个最优解的多峰函数.为此,引进一种物种形成原理算法,该算法根据群体微粒的相似度并行地分成子群体.每个子群体是围绕一个群体种子而建立的.对每个子群体进行QPSO最优搜索,从而保证方程组中每个可能的解都能被搜索到,具有良好的局部寻优特性.对几个重要的测试函数进行仿真实验,结果证明了所用算法可以保证找到方程组所有的解,并且具有很好的精确度.     

Vision,application scenarios,and key technology trends for 6G mobile communications

Multiuser detection based on the message passing algorithm (MPA) has been considered for sparse code multiple access (SCMA) systems. Recently, some complexity-reduced MPA detectors have been proposed, among which the MPA detector based on dynamic factor graph (DFG-MPA) has been shown to outperform other MPA detectors with comparable complexities. However, all these MPA detectors are somehow not very flexible in terms of performance-complexity tradeoff, i.e., the granularities of computational complexity reduction are relatively large. In this paper, a generalized scheme of DFG-MPA, termed as GDFG-MPA, is proposed to make a better and more flexible performance-complexity tradeoff. The proposed scheme features two aspects: (1) instead of banning a message update forever, a banned message update at some iteration is allowed to be updated at later iterations; (2) different numbers of message updates are banned from updating at different iterations. Optimization of GDFG-MPA can be made by allocating banned message updates among iterations. Numerical results have demonstrated that compared to DFG-MPA the proposed GDFG-MPA can achieve much better performance at the same computational complexity or achieve the same performance with much lower complexity. Moreover, the proposed GDFG-MPA is more flexible in tuning the performance and complexity tradeoff.

     

基于分数阶达尔文粒子群FODPSO算法的图像分割

图像分割主要用于提取用户感兴趣的目标,是图像分类和识别的基础。采用一种基于分数阶达尔文粒子群算法的图像分割方法,该算法采用分数阶微积分控制系统收敛性,能够对n尺度图像进行n-1个阈值寻优计算。实验结果表明,对比于APSO、CFPSO算法,该算法具有收敛速度快、稳定性强、精度高、全局寻优等特点,有效地克服了传统算法易陷入局部最优和收敛速度慢等缺陷,可满足实际工程需求。