Snap-drift cuckoo search: A novel cuckoo search optimization algorithm

Cuckoo search (CS) is one of the well-known evolutionary techniques in global optimization. Despite its efficiency and wide use, CS suffers from premature convergence and poor balance between exploration and exploitation. To address these issues, a new CS extension namely snap-drift cuckoo search (SDCS) is proposed in this study. The proposed algorithm first employs a learning strategy and then considers improved search operators. The learning strategy provides an online trade-off between local and global search via two snap and drift modes. In snap mode, SDCS tends to increase global search to prevent algorithm of being trapped in a local minima; and in drift mode, it reinforces the local search to enhance the convergence rate. Thereafter, SDCS improves search capability by employing new crossover and mutation search operators. The accuracy and performance of the proposed approach are evaluated by well-known benchmark functions. Statistical comparisons of experimental results show that SDCS is superior to CS, modified CS (MCS), and state-of-the-art optimization algorithms in terms of convergence speed and robustness.     

动态搜索空间的粒子群算法

为进一步缓解粒子群优化算法在其后期收敛速度慢、早熟等问题,提出了一种挂载式的、依赖自适应阀值和已知全局最优解的压缩搜索空间策略。并在此基础上对粒子重新分配初始位置、调整速度权值来提升算法的后期探索能力。实验表明,在使用相同的权重和学习因子策略时,比之原粒子群优化算法具有较好的表现,在对量子粒子群算法进行嵌入时依然具有一定效果。该策略可以有效避免早熟问题,提升算法在后期的寻优效果,具有较好的鲁棒性。     

A parallel boundary search particle swarm optimization algorithm for constrained optimization problems

During the past decade, considerable research has been conducted on constrained optimization problems (COPs) which are frequently encountered in practical engineering applications. By introducing resource limitations as constraints, the optimal solutions in COPs are generally located on boundaries of feasible design space, which leads to search difficulties when applying conventional optimization algorithms, especially for complex constraint problems. Even though penalty function method has been frequently used for handling the constraints, the adjustment of control parameters is often complicated and involves a trial-and-error approach. To overcome these difficulties, a modified particle swarm optimization (PSO) algorithm named parallel boundary search particle swarm optimization (PBSPSO) algorithm is proposed in this paper. Modified constrained PSO algorithm is adopted to conduct global search in one branch while Subset Constrained Boundary Narrower (SCBN) function and sequential quadratic programming (SQP) are applied to perform local boundary search in another branch. A cooperative mechanism of the two branches has been built in which locations of the particles near boundaries of constraints are selected as initial positions of local boundary search and the solutions of local boundary search will lead the global search direction to boundaries of active constraints. The cooperation behavior of the two branches effectively reinforces the optimization capability of the PSO algorithm. The optimization performance of PBSPSO algorithm is illustrated through 13 CEC06 test functions and 5 common engineering problems. The results are compared with other state-of-the-art algorithms and it is shown that the proposed algorithm possesses a competitive global search capability and is effective for constrained optimization problems in engineering applications.     

双层规划的改进混合布谷鸟搜索量子行为粒子群优化算法

为解决粒子群优化（PSO）算法求解双层规划问题时易陷入局部最优解的问题,提出了一种基于模拟退火（SA）Metropolis准则的改进混合布谷鸟搜索量子行为粒子群优化（ICSQPSO）算法。首先,该混合算法引入SA算法中的Metropolis准则,在求解过程中既能接受好解也能以一定的概率接受坏解,增强全局寻优能力;接着,为布谷鸟搜索算法设计一种改进动态步长Lévy飞行,以保持粒子群在优化过程中较高的多样性,保证搜索广度;最后,利用布谷鸟搜索算法中的偏好随机游走机制帮助粒子跳出局部最优解。通过对13个涵盖非线性规划、分式规划、多个下层规划的双层规划实例的数值实验,结果表明：ICSQPSO算法所得12个双层规划的目标函数最优值显著优于对比算法,只有1例的结果稍差,并且有半数实例的结果优于对比算法50%。由此可见,ICSQPSO算法对双层规划的寻优能力明显优于对比算法。     

A hybrid adaptive cuckoo search optimization algorithm for the problem of chaotic systems parameter estimation

This paper introduces a novel hybrid adaptive cuckoo search (HACS) algorithm to establish the parameters of chaotic systems. In order to balance and enhance the accuracy and convergence rate of the basic cuckoo search (CS) algorithm, the adaptive parameters adjusting operation is presented to tune the parameters properly. Besides, the exploitation capability of the CS algorithm is enhanced a lot by integrating the orthogonal design strategy. The functionality of the HACS algorithm is tested through the Lorenz system under the noise-free and noise-corrupted conditions, respectively. The numerical results demonstrate that the algorithm can estimate parameters efficiently and accurately, and the capability of noise immunity is also powerful. Compared with the basic CS algorithm, genetic algorithm, and particle swarm optimization algorithm, the HACS algorithm is energy efficient and superior.     

基于二次搜索的改进粒子群算法

针对标准粒子群优化（PSO）算法在求解复杂优化问题中出现的早熟收敛问题,提出一种结合梯度下降法的二次搜索粒子群算法。首先,当全局极值超过预设的最大不变迭代次数时,判断全局极值点处于极值陷阱中;然后,采用梯度下降法进行二次搜索,并以最优极值点为中心、某一具体半径设定禁忌区域,防止粒子重复搜索该区域;最后,依据种群多样性准则生成新粒子,替代被淘汰的粒子。将二次搜索粒子群算法及其他四种典型的改进粒子群算法分别应用于四种典型测试函数的优化,仿真结果表明,二次搜索粒子群算法收敛精度最高提升了10个数量级,并且收敛速度较快更容易寻找全局最优解。     

量子粒子群优化算法的控制参数分析

针对现有的量子粒子群优化算法（ QPSO）中收缩扩张系数α取固定值或线性变化时,不能很好地适应复杂的多维非线性优化搜索问题,提出了两种参数α控制策略：基于Logistic函数的动态非线性递减策略和自适应参数调整策略。在第一种策略中引入S型函数来描述α值在进化过程中的动态变化特性,第二种策略中引入反馈调节方式来控制α值的变化。几个典型函数的实验测试结果表明,两种改进后的参数调整策略对于复杂优化问题在收敛速度和平均最优值上都有所改善,明显优于取固定值或线性变化策略。     

Binary optimization using hybrid particle swarm optimization and gravitational search algorithm

The PSOGSA is a novel hybrid optimization algorithm, combining strengths of both particle swarm optimization (PSO) and gravitational search algorithm (GSA). It has been proven that this algorithm outperforms both PSO and GSA in terms of improved exploration and exploitation. The original version of this algorithm is well suited for problems with continuous search space. Some problems, however, have binary parameters. This paper proposes a binary version of hybrid PSOGSA called BPSOGSA to solve these kinds of optimization problems. The paper also considers integration of adaptive values to further balance exploration and exploitation of BPSOGSA. In order to evaluate the efficiencies of the proposed binary algorithm, 22 benchmark functions are employed and divided into three groups: unimodal, multimodal, and composite. The experimental results confirm better performance of BPSOGSA compared with binary gravitational search algorithm (BGSA), binary particle swarm optimization (BPSO), and genetic algorithm in terms of avoiding local minima and convergence rate.     

A coupled particle swarm and harmony search optimization algorithm for difficult geotechnical problems

Many solutions in geotechnical problems are the result of optimization analysis. There are many practical engineering problems where the objective function is non-convex, discontinuous with the presence of multiple strong local minima, and the classical optimization methods may sometime be trapped by the local minimum during the analysis. In this paper, a coupled optimization method is proposed for such difficult cases. The mixed optimization algorithm can takes the advantage of different algorithms, and the proposed algorithm is demonstrated to be effective and efficient in solving a very complicated hydropower problem with a high level of confidence. The proposed method can further be applied to different kinds of difficult engineering problems.     

求解函数优化问题的改进布谷鸟搜索算法

为了提高布谷鸟搜索算法求解函数优化问题的求精能力和收敛速度,提出了一种基于自适应机制的改进算法.自适应机制用于控制缩放因子和发现概率,以提高种群的多样性,避免早熟,从而使更多的个体参与演化,达到提高求精能力和收敛速度的效果.仿真实验结果表明,与标准的布谷鸟搜索算法相比,基于自适应机制缩放因子的改进算法(rCS)和基于自适应机制发现概率的改进算法(paCS)在求精能力和收敛速度上都有明显的提高;同时具有自适应缩放因子和自适应发现概率的改进算法(iCS)比rCS和paCS具有更优的求精能力和收敛速度.     

一种弹性粒子群优化算法

当某个粒子与最优粒子很接近时,其飞行速度将趋于零,这是粒子群优化算法容易陷入局部极小的主要原因.为此,提出一种弹性粒子群优化算法.算法中,粒子速度不依赖其与最优粒子之间距离的大小,而仅依赖于其方向信息,并采用一种自适应策略弹性地修正粒子速度的幅值.将弹性粒子群优化算法应用于几种典型测试函数的优化,数值仿真结果表明,弹性粒子群优化算法能有效地找出全局最优点.     

粒子群空间优化的端元提取算法

粒子群优化算法（Particle Swarm Optimization,PSO）应用于高光谱影像端元提取时,由于影像中存在端元的像元数所占比例极小且分布零散,导致粒子群的搜索空间破碎,存在收敛性能低、容易陷入局部最优解等缺陷。对粒子群的搜索空间进行优化,选择影像中纯净像元指数（Pixel Purity Index,PPI）较大的像元作为预选像元,然后对预选像元进行光谱聚类排序,将排序后的集合作为粒子群的搜索空间,优化了粒子的搜索空间。并在迭代过程中,充分利用粒子群的信息自适应地调整其系数,在缩小原始图像与反演图像的误差同时,增加体积约束,在提取端元时更好地保持其原有的形状。通过模拟数据和AVIRIS影像的实验表明该算法具有较好端元提取效果。     

受粒子群和差分进化启发的人工蜂群算法

针对基本人工蜂群算法搜索策略探索能力强而开发能力弱的特点,受粒子群和差分进化思想的启发,提出了两种新的搜索策略：PSO-DE-PABC和PSO-DE-GABC。前者在随机个体附近产生新的候选位置以提高算法的多样性;后者在最优解附近产生新的候选位置以提高算法的收敛速度,并加入差分进化中的差异向量来增加种群的多样性。在此基础上,引入维度因子来控制算法的收敛速度,并且使用一种利用当前种群信息的侦查策略来增强算法的局部搜索能力。通过对10组标准测试函数的实验仿真并与基本ABC、GABC和ABC/best算法相比,结果表明PSO-DE-GABC和PSO-DE-PABC对数值优化具有更高的收敛速度和收敛精度。     

粒子群算法的新改进思想—递进式搜索

针对传统粒子群算法的不足,提出了一种改进思想:分"初选"和"细搜"两个阶段搜索,分别设立评价能力递增的不同适应度函数和采用不同的惯性权重,进行递进式搜索;构造了一种新的网格构造图——中心对称标距图,并结合目前广泛研究的航迹规划问题,设定3种不同情况对改进算法进行了仿真验证。结果表明,改进后的粒子群算法寻优能力增强,收敛速度也有所提高。     

A perturbed particle swarm algorithm for numerical optimization

The canonical particle swarm optimization (PSO) has its own disadvantages, such as the high speed of convergence which often implies a rapid loss of diversity during the optimization process, which inevitably leads to undesirable premature convergence. In order to overcome the disadvantage of PSO, a perturbed particle swarm algorithm (pPSA) is presented based on the new particle updating strategy which is based upon the concept of perturbed global best to deal with the problem of premature convergence and diversity maintenance within the swarm. A linear model and a random model together with the initial max–min model are provided to understand and analyze the uncertainty of perturbed particle updating strategy. pPSA is validated using 12 standard test functions. The preliminary results indicate that pPSO performs much better than PSO both in quality of solutions and robustness and comparable with GCPSO. The experiments confirm us that the perturbed particle updating strategy is an encouraging strategy for stochastic heuristic algorithms and the max–min model is a promising model on the concept of possibility measure.     

A parameter selection strategy for particle swarm optimization based on particle positions

In this study, we found that engineering experience can be used to determine the parameters of an optimization algorithm. We came to this conclusion by analyzing the dynamic characteristics of PSO through a large number of experiments. We constructed a relationship between the dynamic process of particle swarm optimization and the transition process of a control system. A novel parameter strategy for PSO was proven in this paper using the overshoot and the peak time of a transition process. This strategy not only provides a series of flexible parameters for PSO but it also provides a new way to analyze particle trajectories that incorporates engineering practices. In order to validate the new strategy, we compared it with published results from three previous reports, which are consistent or approximately consistent with our new strategy, using a suite of well-known benchmark optimization functions. The experimental results show that the proposed strategy is effective and easy to implement. Moreover, the new strategy was applied to equally spaced linear array synthesis examples and compared with other optimization methods. Experimental results show that it performed well in pattern synthesis.     

一种用于多目标优化的混合粒子群优化算法

将粒子群算法与局部优化方法相结合,提出了一种混合粒子群多目标优化算法（HMOPSO）。该算法针对粒子群局部优化性能较差的缺点,引入多目标线搜索与粒子群算法相结合的策略,以增强粒子群算法的局部搜索能力。HMOPSO首先运行PSO算法,得到近似的Pareto最优解;然后启动多目标线搜索,发挥传统数值优化算法的优势,对其进行进一步的优化。数值实验表明,HMOPSO具有良好的全局优化性能和较强的局部搜索能力,同时HMOPSO所得的非劣解集在分散性、错误率和逼近程度等量化指标上优于MOPSO。