Adaptive comprehensive learning particle swarm optimization with cooperative archive

Comprehensive learning particle swarm optimization (CLPSO) enhances its exploration capability by exploiting all other particles’ historical information to update each particle’s velocity. However, CLPSO adopts a set of fixed comprehensive learning (CL) probabilities to learn from other particles, which may impair its performance on complex optimization problems. To improve the performance and adaptability of CLPSO, an adaptive mechanism for adjusting CL probability and a cooperative archive (CA) are combined with CLPSO, and the resultant algorithm is referred to as adaptive comprehensive learning particle swarm optimization with cooperative archive (ACLPSO-CA). The adaptive mechanism dividing the CL probability into three levels and adjusting the individual particle’s CL probability level dynamically according to the performance of the particles during the optimization process. The cooperative archive is employed to provide additional promising information for ACLPO-CA and itself is updated by the cooperative operation of the current swarm and archive. To evaluate the performance of ACLPSO-CA, ACLPSO-CA is tested on CEC2013 test suite and CEC2017 test suite and compared with seven popular PSO variants. The test results show that ACLPSO-CA outperforms other comparative PSO variants on the two CEC test suites. ACLPSO-CA achieves high performance on different types of benchmark functions and exhibits high adaptability as well. In the end, ACLPSO-CA is further applied to a radar system design problem to demonstrate its potential in real-life optimization.     

Discrete cooperative particle swarm optimization for FPGA placement

Particle swarm optimization (PSO) is a stochastic optimization technique that has been inspired by the movement of birds. On the other hand, the placement problem in field programmable gate arrays (FPGAs) is crucial to achieve the best performance. Simulated annealing algorithms have been widely used to solve the FPGA placement problem. In this paper, a discrete PSO (DPSO) version is applied to the FPGA placement problem to find the optimum logic blocks and IO pins locations in order to minimize the total wire-length. Moreover, a co-operative version of the DPSO (DCPSO) is also proposed for the FPGA placement problem. The problem is entirely solved in the discrete search space and the proposed implementation is applied to several well-known FPGA benchmarks with different dimensionalities. The results are compared to those obtained by the academic versatile place and route (VPR) placement tool, which is based on simulated annealing. Results show that both the DPSO and DCPSO outperform the VPR tool for small and medium-sized problems, with DCPSO having a slight edge over the DPSO technique. For higher-dimensionality problems, the algorithms proposed provide very close results to those achieved by VPR.     

一种基于粒子群优化的可能性C均值聚类改进方法

提出了一种基于拉子群优化的可能性c均值(Possibilistic Gmeans, PCM)聚类改进方法。该方法首先通过 改进PCM算法的目标函数来计算数据模式的隶属度矩阵和聚类中心完成粒子编码,从而降低算法对初始中心的敏 感,提高聚类的精度;其次,通过粒子群优化(Particle Swarm Optimization, PSO)算法对编码进行优化,以有效地克服 PCM聚类算法容易导致聚类一致性和陷入局部最优解的缺点,减少算法的迭代次数。通过人造数据集和UCI数据 集上的实验,表明该算法在计算复杂度、聚类精度和全局寻优能力方面表现得较为突出。     

基于群能量恒定的粒子群优化算法

针对标准粒子群优化(PSO)算法在寻优过程中容易出现早熟的情况,提出一种群能量恒定的粒子群优化(SEC-PSO)算法.算法根据粒子内能进行动态分群,对较优群体采取引入最差粒子的速度更新策略,对较差群体采取带有惩罚机制的速度更新策略,由其分担由于较优群体速度降低而产生的整群能量损失,从而有效地避免了PSO算法的早熟.典型优化问题的仿真结果表明,该算法具有较强的全局搜索能力和较快的收敛速度,优化性能得到显著的提高.     

Particle swarm optimization technique based short-term hydrothermal scheduling

Particle swarm optimization is applied to determine the optimal hourly schedule of power generation in a hydrothermal power system. A multi-reservoir cascaded hydroelectric system with a nonlinear relationship between water discharge rate, net head and power generation is considered. The water transport delay between connected reservoirs is taken into account. In the present work, the effects of valve point loading in the fuel cost function of the thermal plants are also taken into consideration. The developed algorithm is illustrated for a test system consisting of four hydro plants and three thermal plants. Cost characteristics of individual thermal units are considered. The test results are compared with those obtained using evolutionary programming and simulated annealing technique. It is found that the convergence characteristic is excellent and the results obtained by the proposed method are superior in terms of fuel cost and computation time.     

Particle swarm optimization with preference order ranking for multi-objective optimization

A new optimality criterion based on preference order (PO) scheme is used to identify the best compromise in multi-objective particle swarm optimization (MOPSO). This scheme is more efficient than Pareto ranking scheme, especially when the number of objectives is very large. Meanwhile, a novel updating formula for the particle’s velocity is introduced to improve the search ability of the algorithm. The proposed algorithm has been compared with NSGA-II and other two MOPSO algorithms. The experimental results indicate that the proposed approach is effective on the highly complex multi-objective optimization problems.     

Particle swarm optimization with query-based learning for multi-objective power contract problem

Particle swarm optimization (PSO) is one of the most important research topics on swarm intelligence. Existing PSO techniques, however, still contain some significant disadvantages. In this paper, we present a new QBL-PSO algorithm that uses QBL (query-based learning) to improve both the exploratory and exploitable capabilities of PSO. Here, we apply a QBL method proposed in our previous research to PSO, and then test this new algorithm on a real case study on problems of power conservation. Our algorithm not only broadens the search diversity of PSO, but also improves its precision. Conventional PSO often snag on local solutions when performing queries, instead of finding better global solutions. To resolve this limitation, when particles converge in nature, we direct some of them into an “ambiguous solution space” defined by our algorithm. This paper introduces two ways to invoke this QBL algorithm. Our experimental results confirm that the proposed method attains better convergence to the global best solution. Finally, we present a new PSO model for solving multi-objective power conservation problems. Overall, this model successfully reduces power consumption, and to our knowledge, this paper represents the first attempt within the literature to apply the QBL concept to PSO.     

具有综合学习机制的粒子群算法

基本粒子群算法在模拟生物群体智能时,只有信息的单一传递和强迫学习机制,导致群体迅速收敛和种群的多样性降低。为此,提出一种具有综合学习机制的粒子群算法,将所有粒子的个体极值的平均值取代每一粒子的个体极值,并以自适应概率定向地随机变异全局极值。仿真实验表明,新算法解精度高,收敛速度快,能有效抑制过早收敛。     

Particle swarm optimization with grey evolutionary analysis

Based on grey relational analysis, this study attempts to propose a grey evolutionary analysis (GEA) to analyze the population distribution of particle swarm optimization (PSO) during the evolutionary process. Then two GEA-based parameter automation approaches are developed. One is for the inertia weight and the other is for the acceleration coefficients. With the help of the GEA technique, the proposed parameter automation approaches would enable the inertia weight and acceleration coefficients to adapt to the evolutionary state. Such parameter automation behaviour also makes an attempt on the GEA-based PSO to perform a global search over the search space with faster convergence speed. In addition, the proposed PSO is applied to solve the optimization problems of twelve unimodal and multimodal benchmark functions for illustration. Simulation results show that the proposed GEA-based PSO could outperform the adaptive PSO, the grey PSO, and two well-known PSO variants on most of the test functions.     

Particle swarm optimization for time-optimal control design

In this paper,a particle swarm optimization(PSO)based method is proposed to obtain the time-optimal bang-bang control law for both linear and nonlinear systems.By introducing a penalty function,the method can be modified to deal with systems with constraints.Compared with existing computational methods,the proposed method can be implemented in a straightforward manner.The convergent solutions can be achieved by selecting suitable PSO parameters regardless of the initial guess of the switching times.A double integrator and a third-order nonlinear system are used to demonstrate the effectiveness and robustness of the proposed method.The method is applied to obtain the time-optimal control law for a high performance linear motion positioning system.The results show the practicality of the proposed algorithm.     

改进的多种群协同进化微粒群优化算法

提出一种改进的基于多种群协同进化的微粒群优化算法（ＰＳＯ）．该算法首先利用免疫算法实现解空间的均匀划分,增加了算法稳定性和全局搜索能力．在运行过程中,通过种群进化信息生成解优胜区域,指导变异生成的微粒群向最优解子空间逼近,提高算法逃出局部最优的能力．将此算法与ＰＳＯ 算法和多种群协同进化微粒群算法进行比较,数据实验证明,该算法不仅能有效地克服其他算法易陷入局部极小值的缺点,而且全局收敛能力和稳定性均有显著提高．     

粒子群算法求解任务可拆分项目调度问题

首先针对任务可拆分的项目调度问题,提出一种带有局部搜索的粒子群算法LSPSO;然后采用基于任务排列的粒子表示方法,将遗传算法中的定位交叉引入粒子的更新过程中,并采用局部搜索技术对更新后的粒子进行改进;最后对Patterson测试集中110个问题实例进行了测试,实验结果表明,算法LSPSO具有较快的速度,所给出的调度方案较优.     

带扰动因子的布尔型粒子群优化算法

对二进制布尔型粒子群优化算法提出改进,通过在其速度更新公式中引入扰动因子避免粒子过早的陷入局部极值,提出两种调整惯性权重和学习因子取1的概率的策略以平衡算法的收敛和发散,分别是按照粒子相似性自适应调整和线性调整,由此得到两种带扰动因子的布尔型粒子群优化算法。4个基准测试函数的对比,实验结果表明了两种改进算法的有效性和优良性能。     

新型多群体协同进化粒子群优化算法

在基本的MCPSO算法中除了主群与从群的信息交流,从群之间没有信息交流。为了解决这一问题,提出了一种具有中心交流机制的改进MCPSO算法,该策略可以实现各个从群之间的信息交流,从而加快算法收敛。仿真实验结果表明改进后的算法具有较好的求解精度和较快的收敛速度。     

Particle swarm optimization for determining fuzzy measures from data

Fuzzy measures and fuzzy integrals have been successfully used in many real applications. How to determine fuzzy measures is the most difficult problem in these applications. Though there have existed some methodologies for solving this problem, such as genetic algorithms, gradient descent algorithms and neural networks, it is hard to say which one is more appropriate and more feasible. Each method has its advantages and limitations. Therefore it is necessary to develop new methods or techniques to learn distinct fuzzy measures. In this paper, we make the first attempt to design a special particle swarm algorithm to determine a type of general fuzzy measures from data, and demonstrate that the algorithm is effective and efficient. Furthermore we extend this algorithm to identify and revise other types of fuzzy measures. To test our algorithms, we compare them with the basic particle swarm algorithms, gradient descent algorithms and genetic algorithms in literatures. In addition, for verifying whether our algorithms are robust in noisy-situations, a number of numerical experiments are conducted. Theoretical analysis and experimental results show that, for determining fuzzy measures, the particle swarm optimization is feasible and has a better performance than the existing genetic algorithms and gradient descent algorithms.     

Particle swarm optimization for ensembling generation for evidential k-nearest-neighbour classifier

The problem addressed in this paper concerns the ensembling generation for evidential k-nearest-neighbour classifier. An efficient method based on particle swarm optimization (PSO) is here proposed. We improve the performance of the evidential k-nearest-neighbour (EkNN) classifier using a random subspace based ensembling method. Given a set of random subspace EkNN classifier, a PSO is used for obtaining the best parameters of the set of evidential k-nearest-neighbour classifiers, finally these classifiers are combined by the “vote rule”. The performance improvement with respect to the state-of-the-art approaches is validated through experiments with several benchmark datasets.

Particle swarm optimization based clustering algorithm with mobile sink for WSNs

Wireless sensor networks with fixed sink node often suffer from hot spots problem since sensor nodes close to the sink usually have more traffic burden to forward during transmission process. Utilizing mobile sink has been shown as an effective technique to enhance the network performance such as energy efficiency, network lifetime, and latency, etc. In this paper, we propose a particle swarm optimization based clustering algorithm with mobile sink for wireless sensor network. In this algorithm, the virtual clustering technique is performed during routing process which makes use of the particle swarm optimization algorithm. The residual energy and position of the nodes are the primary parameters to select cluster head. The control strategy for mobile sink to collect data from cluster head is well designed. Extensive simulation results show that the energy consumption is much reduced, the network lifetime is prolonged, and the transmission delay is reduced in our proposed routing algorithm than some other popular routing algorithms.     

Dynamic clustering using particle swarm optimization with application in image segmentation

A new dynamic clustering approach (DCPSO), based on particle swarm optimization, is proposed. This approach is applied to image segmentation. The proposed approach automatically determines the “optimum” number of clusters and simultaneously clusters the data set with minimal user interference. The algorithm starts by partitioning the data set into a relatively large number of clusters to reduce the effects of initial conditions. Using binary particle swarm optimization the “best” number of clusters is selected. The centers of the chosen clusters is then refined via the K-means clustering algorithm. The proposed approach was applied on both synthetic and natural images. The experiments conducted show that the proposed approach generally found the “optimum” number of clusters on the tested images. A genetic algorithm and random search version of dynamic clustering is presented and compared to the particle swarm version.     

约束松弛机制和反向搜索的粒子群算法

提出了一种基于约束松弛机制和反向搜索的粒子群算法.约束松弛机制通过度量不可行解与可行解的距离,使边界位置的不可行解得以概率性存活,缓解了约束条件的不利影响;反向搜索机制则使不可行解迅速转向某一对称区域,提高了可行域的发掘效率.同时,利用局部最优位置的概念,使个体依据约束违反情况执行不同的搜索算子,克服了函数形貌的影响.通过六组标准约束优化问题的测试结果表明,所提出算法总体优于几种对比算法,最优解与理论值的相对误差由0～25.214％降低到了0～0.752％.     

Cellular particle swarm optimization

This paper proposes a cellular particle swarm optimization (CPSO), hybridizing cellular automata (CA) and particle swarm optimization (PSO) for function optimization. In the proposed CPSO, a mechanism of CA is integrated in the velocity update to modify the trajectories of particles to avoid being trapped in the local optimum. With two different ways of integration of CA and PSO, two versions of CPSO, i.e. CPSO-inner and CPSO-outer, have been discussed. For the former, we devised three typical lattice structures of CA used as neighborhood, enabling particles to interact inside the swarm; and for the latter, a novel CA strategy based on “smart-cell” is designed, and particles employ the information from outside the swarm. Theoretical studies are made to analyze the convergence of CPSO, and numerical experiments are conducted to compare the proposed algorithm with different variants of PSO. According to the experimental results, the proposed method performs better than other variants of PSO on benchmark test functions.