A Surface-Simplex Swarm Evolution Algorithm

In the paper,a particle surface-simplex search（PSSS） is designed based on particle surface-simplex and particle surface-simplex neighborhood.Using PSSS and an evolutionary strategy of multi-states swarm,a surface-simplex swarm evolution（SSSE） algorithm for numerical optimization is proposed.In the experiments,SSSE is applied to solve 17 benchmark problems and compared with the other intelligent optimization algorithms.In the application,SSSE is used to analyze the three intrinsic independent components of gravity earth tide.The results demonstrate that SSSE can accurately find optima or close-to-optimal solutions of the complex functions with high-dimension.The performance of SSSE is stable and efficient.

Hybrid particle swarm optimization with differential evolution and chaotic local search to solve reliability-redundancy allocation problems

In order to solve reliability-redundancy allocation problems more effectively, a new hybrid algorithm named CDEPSO is proposed in this work, which combines particle swarm optimization （PSO） with differential evolution （DE） and a new chaotic local search. In the CDEPSO algorithm, DE provides its best solution to PSO if the best solution obtained by DE is better than that by PSO, while the best solution in the PSO is performed by chaotic local search. To investigate the performance of CDEPSO, four typical reliability-redundancy allocation problems were solved and the results indicate that the convergence speed and robustness of CDEPSO is better than those of PSO and CPSO （a hybrid algorithm which only combines PSO with chaotic local search）. And, compared with the other six improved meta-heuristics, CDEPSO also exhibits more robust performance. In addition, a new performance was proposed to more fairly compare CDEPSO with the same six improved recta-heuristics, and CDEPSO algorithm is the best in solving these problems.

Automatic relational database compression scheme design based on swarm evolution

Compression is an intuitive way to boost the performance of a database system. However, compared with other physical database design techniques, compression consumes large amount of CPU power. There is a trade-off between the re- duction of disk access and the overhead of CPU processing. Automatic design and adaptive administration of database systems are widely demanded, and the automatic selection of compression schema to compromise the trade-off is very important. In this paper, we present a model with novel techniques to integrate a rapidly convergent agent-based evolution framework, i.e. the SWAF (SWarm Algorithm Framework), into adaptive attribute compression for relational database. The model evolutionally consults statistics of CPU load and IO bandwidth to select compression schemas considering both aspects of the trade-off. We have im- plemented a prototype model on Oscar RDBMS with experiments highlighting the correctness and efficiency of our techniques.

Optimal Static State Estimation Using hybrid Particle Swarm-Differential Evolution Based Optimization

In this paper, swarm optimization hybridized with differential evolution (PSO-DE) technique is proposed to solve static state estimation (SE) problem as a minimization problem. The proposed hybrid method is tested on IEEE 5-bus, 14-bus, 30-bus, 57-bus and 118-bus standard test systems along with 11-bus and 13-bus ill-conditioned test systems under different simulated conditions and the results are compared with the same, obtained using standard weighted least square state estimation (WLS-SE) technique and general particle swarm optimization (GPSO) based technique. The performance of the proposed optimization technique for SE, in terms of minimum value of the objective function and standard deviations of minimum values obtained in 100 runs, is found better as compared to the GPSO based technique. The statistical error analysis also shows the superiority of the proposed PSO-DE based technique over the other two techniques.

A Measurement Study on Resource Popularity and Swarm Evolution of BitTorrent System

Analyzing and modeling of the BitTorrent (BT) resource popularity and swarm evolution is important for better understanding current BT system and designing accurate BT simulators. Although lots of measurement studies on BT almost cover each important aspect, little work reflects the recent development of BT system. In this paper, we develop a hybrid measurement system incorporating both active and passive approaches. By exploiting DHT (Distribute Hash Table) and PEX (Peer Exchange) protocols, we collect more extensive information compared to prior measurement systems. Based on the measurement results, we study the resource popularity and swarm evolution with different population in minute/ hour/day scales, and discover that: 1) the resources in BT system appear obvious unbalanced distribution and hotspot phenomenon, in that 74.6% torrents have no more than 1000 peers;2) The lifetime of torrents can be divided into a fast growing stage, a dramatically shrinking stage, a sustaining stage and a slowly fading out stage in terms of swarm population;3) Users’ interest and diurnal periodicity are the main factors that influence the swarm evolution. The former dominates the first two stages, while the latter is decisive in the third stage. We raise an improved peer arrival rate model to describe the variation of the swarm population. Comparison results show that our model outperforms the state-of-the-art approach according to root mean square error and correlation coefficient.

Mafic Dyke Swarms: Their Temporality and Bearing on the Secular Evolution of the Earth

Pioneering U-Pb isotopic studies by a small group of workers in the mid-late 1980s demonstrated the feasibility of using rare accessory mineral chronometers in mafic(gabbroic)intrusive rocks.These examples showed that mafic layered intrusions and diabase/dolerite dyke swarms alike crystallized high-temperature

A Hybrid Differential Evolution Algorithm Integrated with Particle Swarm Optimization

To implement self-adaptive control parameters, a hybrid differential evolution algorithm integrated with particle swarm optimization （PSODE） is proposed. In the PSODE, control parameters are encoded to be a symbiotic individual of original individual, and each original individual has its own symbiotic individual. Differential evolution （ DE） operators are used to evolve the original population. And, particle swarm optimization （PSO） is applied to co-evolving the symbiotic population. Thus, with the evolution of the original population in PSODE, the symbiotic population is dynamically and self-adaptively adjusted and the realtime optimum control parameters are obtained. The proposed algorithm is compared with some DE variants on nine functious. The results show that the average performance of PSODE is the best.

PSO Clustering Algorithm Based on Cooperative Evolution

Among the bio-inspired techniques,PSO-based clustering algorithms have received special attention. An improved method named Particle Swarm Optimization (PSO) clustering algorithm based on cooperative evolution with multi-populations was presented. It adopts cooperative evolutionary strategy with multi-populations to change the mode of traditional searching optimum solutions. It searches the local optimum and updates the whole best position (gBest) and local best position (pBest) ceaselessly. The gBest will be passed in all sub-populations. When the gBest meets the precision,the evolution will terminate. The whole clustering process is divided into two stages. The first stage uses the cooperative evolutionary PSO algorithm to search the initial clustering centers. The second stage uses the K-means algorithm. The experiment results demonstrate that this method can extract the correct number of clusters with good clustering quality compared with the results obtained from other clustering algorithms.

Simulation of Old Urban Residential Area Evolution Based on Complex Adaptive System

On the basis of complex adaptive system theory, this paper proposed an agent-based model of old urban residential area, in which, residents and providers are the two adaptive agents. The behaviors of residents and providers in this model are trained with back propagation and simulated with Swarm software based on environment-rules-agents interaction. This model simulates the evolution of old urban residential area and analyzes the relations between the evolution and urban management with the background of Chaozhou city. As a result, the following are obtained ： （ 1 ） Simulation without government intervention indicates the trend of housing ageing, environmental deterioration, economic depression, and social filtering-down in old urban residential area. If the development of old urban residential area is under control of developers in market, whose desire is profit maximization, and factors such as social justice, historic and culture value will be ignored. （2） If the government carries out some policies and measures which will perfectly serve their original aims, simulation reveals that old urban residential area could be adapted to environment and keep sustainable development. This conclusion emphasizes that government must act as initiator and program maker for guiding residents and other providers directly in the development of old urban residential area.

A hybrid differential evolution algorithm for a stochastic location-inventory-delivery problem with joint replenishment

A practical stochastic location-inventory-delivery problem with multi-item joint replenishment is studied.Unlike the conventional location-inventory model with a continuous-review(r,Q)inventory policy,the periodic-review inventory policy is adopted with multi-item joint replenishment under stochastic demand,and the coordinated delivery cost is considered.The proposed model considers the integrated optimization of strategic,tactical,and operational decisions by simultaneously determining(a)the number and location of distribution centers(DCs)to be opened,(b)the assignment of retailers to DCs,(c)the frequency and cycle interval of replenishment and delivery,and(d)the safety stock level for each item.An intelligent algorithm based on particle swarm optimization(PSO)and adaptive differential evolution(ADE)is proposed to address this complex problem.Numerical experiments verified the effectiveness of the proposed two-stage PSO-ADE algorithm.A sensitivity analysis is presented to reveal interesting insights that can guide managers in making reasonable decisions.

Hybrid Support Vector Regression with Parallel Co-Evolution Algorithm Based on GA and PSO for Forecasting Monthly Rainfall

Accurate and timely monthly rainfall forecasting is a major challenge for the scientific community in hydrological research such as river management project and design of flood warning systems. Support Vector Regression (SVR) is a very useful precipitation prediction model. In this paper, a novel parallel co-evolution algorithm is presented to determine the appropriate parameters of the SVR in rainfall prediction based on parallel co-evolution by hybrid Genetic Algorithm and Particle Swarm Optimization algorithm, namely SVRGAPSO, for monthly rainfall prediction. The framework of the parallel co-evolutionary algorithm is to iterate two GA and PSO populations simultaneously, which is a mechanism for information exchange between GA and PSO populations to overcome premature local optimum. Our methodology adopts a hybrid PSO and GA for the optimal parameters of SVR by parallel co-evolving. The proposed technique is applied over rainfall forecasting to test its generalization capability as well as to make comparative evaluations with the several competing techniques, such as the other alternative methods, namely SVRPSO (SVR with PSO), SVRGA (SVR with GA), and SVR model. The empirical results indicate that the SVRGAPSO results have a superior generalization capability with the lowest prediction error values in rainfall forecasting. The SVRGAPSO can significantly improve the rainfall forecasting accuracy. Therefore, the SVRGAPSO model is a promising alternative for rainfall forecasting.

Modified particle swarm optimization-based antenna tilt angle adjusting scheme for LTE coverage optimization

In order to solve the challenging coverage problem that the long term evolution（ LTE） networks are facing, a coverage optimization scheme by adjusting the antenna tilt angle（ ATA） of evolved Node B（ e NB） is proposed based on the modified particle swarm optimization（ MPSO） algorithm.The number of mobile stations（ MSs） served by e NBs, which is obtained based on the reference signal received power（RSRP） measured from the MS, is used as the metric for coverage optimization, and the coverage problem is optimized by maximizing the number of served MSs. In the MPSO algorithm, a swarm of particles known as the set of ATAs is available; the fitness function is defined as the total number of the served MSs; and the evolution velocity corresponds to the ATAs adjustment scale for each iteration cycle. Simulation results showthat compared with the fixed ATA, the number of served MSs by e NBs is significantly increased by 7. 2%, the quality of the received signal is considerably improved by 20 d Bm, and, particularly, the system throughput is also effectively increased by 55 Mbit / s.

Bio-inspired environmental adaptability of swarm active matter

How biologically active matters survive adaptively in complex and changeable environments is a common concern of scientists.Genetics,evolution and natural selection are vital factors in the process of biological evolution and are also the key to survival in harsh environments.However,it is challenging to intuitively and accurately reproduce such longterm adaptive survival processes in the laboratory.Although simulation experiments are intuitive and efficient,they lack fidelity.Therefore,we propose to use swarm robots to study the adaptive process of active matter swarms in complex and changeable environments.Based on a self-built virtual environmental platform and a robot swarm that can interact with the environment,we introduce the concept of genes into the robot system,giving each robot unique digital genes,and design robot breeding methods and rules for gene mutations.Our previous work[Proc.Natl.Acad.Sci.USA 119 e2120019119(2022)]has demonstrated the effectiveness of this system.In this work,by analyzing the relationship between the genetic traits of the population and the characteristics of environmental resources,and comparing different experimental conditions,we verified in both robot experiments and corresponding simulation experiments that agents with genetic inheritance can survive for a long time under the action of natural selection in periodically changing environments.We also confirmed that in the robot system,both breeding and mutation are essential factors.These findings can help answer the practical scientific question of how individuals and swarms can successfully adapt to complex,dynamic,and unpredictable actual environments.

Recent Advances in Particle Swarm Optimization for Large Scale Problems

Accompanied by the advent of current big data ages,the scales of real world optimization problems with many decisive design variables are becoming much larger.Up to date,how to develop new optimization algorithms for these large scale problems and how to expand the scalability of existing optimization algorithms have posed further challenges in the domain of bio-inspired computation.So addressing these complex large scale problems to produce truly useful results is one of the presently hottest topics.As a branch of the swarm intelligence based algorithms,particle swarm optimization (PSO) for coping with large scale problems and its expansively diverse applications have been in rapid development over the last decade years.This reviewpaper mainly presents its recent achievements and trends,and also highlights the existing unsolved challenging problems and key issues with a huge impact in order to encourage further more research in both large scale PSO theories and their applications in the forthcoming years.

Seismic swarm intelligence inversion with sparse probability distribution of reflectivity

Seismic inversion,such as velocity and impedance,is an ill-posed problem.To solve this problem,swarm intelligence(SI)algorithms have been increasingly applied as the global optimization approach,such as differential evolution(DE)and particle swarm optimization(PSO).Based on the well logs,the sparse probability distribution(PD)of the reflectivity distribution is spatial stationarity.Therefore,we proposed a general SI scheme with constrained by a priori sparse distribution of the reflectivity,which helps to provide more accurate potential solutions for the seismic inversion.In the proposed scheme,as two key operations,the creating of probability density function library and probability transformation are inserted into standard SI algorithms.In particular,two targeted DE-PD and PSO-PD algorithms are implemented.Numerical example of Marmousi2 model and field example of gas hydrates show that the DE-PD and PSO-PD estimate better inversion solutions than the results of the original DE and PSO.In particular,the DE-PD is the best performer both in terms of mean error and fitness value of velocity and impendence inversion.Overall,the proposed SI with sparse distribution scheme is feasible and effective for seismic inversion.

电推进GEO卫星的改进粒子群轨道保持优化设计

针对地球同步轨道(GEO)卫星轨道保持问题,提出了一种基于改进粒子群算法(PSO)的序列电推力轨道保持方法。首先,建立了GEO卫星高精度非线性轨道动力学模型和序列电推力模型。然后,设计了GEO卫星相对轨道保持策略,建立了以燃料消耗为性能指标的序列电推力轨道保持问题优化模型并进行了离散化。接着,通过引入差分进化算法和维度学习策略对粒子群优化算法进行了适应性改进,同时对推力大小和作用时间进行寻优计算。最后,通过数值仿真对所提出的改进粒子群优化算法进行了对比校验。结果表明,该方法在完成GEO卫星轨道保持任务的同时具备燃料消耗低和收敛速度快等优点。

生存进化阶段性搜索微粒群算法及其可靠性冗余分配优化应用

为高效解决含有异质冗余的多态系统(MSS)可靠性优化问题,并弥补微粒群优化(PSO)算法易早熟收敛的不足,从作用力方式和种群拓扑结构两方面对算法进行改进。改进PSO算法中单一的作用力方式,设置前后两个搜索阶段,对应两个搜索阶段分别构造平衡引斥力方式和双层引力(个体和全局最优解引力、中间适应度微粒引力)方式,提出阶段性搜索微粒群(SPSO)算法;利用生物个体“择友而交”和优胜劣汰的生存体系构建生存进化(SE)拓扑结构,以结构演化和算法进化并行方式将该拓扑结构融入SPSO算法,提出生存进化阶段性搜索微粒群(SPSO-SE)算法,进一步提升算法的优化性能;利用Benchmark函数对所提算法与PSO的改进算法进行测试对比,结果表明,所提SPSO-SE算法具有更好的寻优能力。采用SPSO-SE算法对串-并联和桥式结构的多态系统的可靠性冗余分配问题进行优化,得到的系统结构费用更低、可靠度更高。

改进粒子群算法的紫外光协作多无人机任务分配方法

为了解决无人机协同作战问题,需要将多任务分配给多个无人机。利用无线紫外光实现强电磁干扰环境下无人机机间隐秘信息传输,提出一种改进粒子群算法的多无人机任务分配方法,综合考虑无人机执行任务所付出的威胁代价、航程代价以及完成任务的时间差,结合压缩因子和差分进化思想解决粒子群优化算法容易陷入局部最优的问题。仿真结果表明,改进粒子群算法相较于传统粒子群算法在不同无人机和任务数量比下的任务分配平均成功率提高了约16%,算法在收敛时的迭代次数平均减少了约4.5倍,最优适应度值平均减小了近一倍,在多无人机任务分配的实际应用中有一定的意义。