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.

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.

Hybrid Global Optimization Algorithm for Feature Selection

This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm(PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing into the combined power of TVAC(Time-Variant Acceleration Coefficients)and IW(Inertial Weight).Proposed algorithm has been tested against linear,non-linear,traditional,andmultiswarmbased optimization algorithms.An experimental study is performed in two stages to assess the proposed PLTVACIW-PSO.Phase I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIWPSO vs.IW based Particle Swarm Optimization(PSO)algorithms,TVAC based PSO algorithms,traditional PSO,Genetic algorithms(GA),Differential evolution(DE),and,finally,Flower Pollination(FP)algorithms.In phase II,the proposed PLTVACIW-PSO uses the same 12 known Benchmark functions to test its performance against the BAT(BA)and Multi-Swarm BAT algorithms.In phase III,the proposed PLTVACIW-PSO is employed to augment the feature selection problem formedical datasets.This experimental study shows that the planned PLTVACIW-PSO outpaces the performances of other comparable algorithms.Outcomes from the experiments shows that the PLTVACIW-PSO is capable of outlining a feature subset that is capable of enhancing the classification efficiency and gives the minimal subset of the core features.

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.

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.

Performance Evaluation and Comparison of Multi - Objective Optimization Algorithms for the Analytical Design of Switched Reluctance Machines

This paper systematically evaluates and compares three well-engineered and popular multi-objective optimization algorithms for the design of switched reluctance machines.The multi-physics and multi-objective nature of electric machine design problems are discussed,followed by benchmark studies comparing generic algorithms(GA),differential evolution(DE)algorithms and particle swarm optimizations(PSO)on a 6/4 switched reluctance machine design with seven independent variables and a strong nonlinear multi-objective Pareto front.To better quantify the quality of the Pareto fronts,five primary quality indicators are employed to serve as the algorithm testing metrics.The results show that the three algorithms have similar performances when the optimization employs only a small number of candidate designs or ultimately,a significant amount of candidate designs.However,DE tends to perform better in terms of convergence speed and the quality of Pareto front when a relatively modest amount of candidates are considered.

Hybrid Particle Swarm Optimization with Differential Evolution for Numerical and Engineering Optimization

In this paper, a hybrid particle swarm optimization （PSO） algorithm with differential evolution （DE） is proposed for numerical benchmark problems and optimization of active disturbance rejection controller （ADRC） parameters. A chaotic map with greater Lyapunov exponent is introduced into PSO for balancing the exploration and exploitation abilities of the proposed algorithm. A DE operator is used to help PSO jump out of stagnation. Twelve benchmark function tests from CEC2005 and eight real world opti- mization problems from CEC2011 are used to evaluate the performance of the proposed algorithm. The results show that statistically, the proposed hybrid algorithm has performed consistently well compared to other hybrid variants. Moreover, the simulation results on ADRC parameter optimization show that the optimized ADRC has better robustness and adaptability for nonlinear discrete-time systems with time delays.

基于PSO-DE-CA的FIR滤波器设计

为优化有限脉冲响应(FIR)数字滤波器的设计,提出一种基于双种群的文化算法。种群空间分别按照粒子群优化和差分进化算法独立进化。信仰空间作为知识库,用于保存求解问题的群体经验。仿真实验结果表明,在设计FIR数字滤波器时,该算法具有较高的鲁棒性和较快的收敛速度,优化结果好于同类算法。

基于PSO-DE混合算法的结构可靠性优化设计

为提高结构可靠性优化设计的效率,利用粒子群优化(PSO)和差分进化(DE)算法的搜索特性,构造一种PSO-DE混合算法,以克服基本PSO算法的早熟问题.将PSO-DE混合算法与结构可靠性优化理论相结合,建立了结构系统失效概率约束下以结构质量最小化为目标的优化模型.算例结果表明:与基本PSO算法相比,文中提出的PSO-DE混合算法提高了收敛速度和计算精度;该算法易于实现,鲁棒性好.

一种新的双种群PSO-DE混合算法

给出一种新的粒子群算法和差分进化算法相结合的混合算法.该算法基于一种双种群进化策略,其中一个种群由粒子群算法进化,另一种群由差分进化算法进化.此外,采用一种信息分享机制,在算法的进化过程中2个种群中的个体可以实现协同进化.为了进一步提高混合算法的性能,在差分进化算法中融入一种线性递减加权策略的变异操作和指数递增交叉概率算子.通过4个标准测试函数的测试结果表明文中提出的混合算法是一种收敛速度快、求解精度高、鲁棒性较强的全局优化算法.

一种基于SAPSO-DE混合算法的结构非概率可靠性优化设计

针对不确定性结构的非概率可靠性优化问题,提出一种基于模拟退火粒子群算法和差分进化算法(SAPSODE混合算法)的结构非概率可靠性优化设计方法。考虑结构非概率可靠性指标约束,建立最小化结构体积为目标的优化模型。为了提高结构非概率可靠性优化问题的计算精度和效率,采用基于认知经验进化的SAPSO-DE混合算法进行非概率可靠性优化设计。研究结果表明:基于SAPSO-DE混合算法的结构非概率可靠性优化设计方法克服了PSO算法的早熟现象,并提高了收敛速度和精度;该方法的全局搜索能力强,且具有较强的稳定性。

Supply Chain Production-distribution Cost Optimization under Grey Fuzzy Uncertainty

Most supply chain programming problems are restricted to the deterministic situations or stochastic environmcnts. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertain programming model to optimize the supply chain production-distribution cost. The programming parameters of the material suppliers, manufacturer, distribution centers, and the customers are integrated into the presented model. On the basis of the chance measure and the credibility of grey fuzzy variable, the grey fuzzy simulation methodology was proposed to generate input-output data for the uncertain functions. The designed neural network can expedite the simulation process after trained from the generated input-output data. The improved Particle Swarm Optimization （PSO） algorithm based on the Differential Evolution （DE） algorithm can optimize the uncertain programming problems. A numerical example was presented to highlight the significance of the uncertain model and the feasibility of the solution strategy.

A Perspective of Conventional and Bio-inspired Optimization Techniques in Maximum Likelihood Parameter Estimation

Maximum likelihood estimation is a method of estimating the parameters of a statistical model in statistics. It has been widely used in a good many multi-disciplines such as econometrics, data modelling in nuclear and particle physics, and geographical satellite image classification, and so forth. Over the past decade, although many conventional numerical approximation approaches have been most successfully developed to solve the problems of maximum likelihood parameter estimation, bio-inspired optimization techniques have shown promising performance and gained an incredible recognition as an attractive solution to such problems. This review paper attempts to offer a comprehensive perspective of conventional and bio-inspired optimization techniques in maximum likelihood parameter estimation so as to highlight the challenges and key issues and encourage the researches for further progress.

Metaheuristic algorithms for groundwater model parameter inversion:Advances and prospects

Groundwater inverse modeling is a vital technique for estimating unmeasurable model parameters and enhancing numerical simulation accuracy.This paper comprehensively reviews the current advances and future prospects of metaheuristic algorithm-based groundwater model parameter inversion.Initially,the simulation-optimization parameter estimation framework is introduced,which involves the integration of simulation models with metaheuristic algorithms.The subsequent sections explore the fundamental principles of four widely employed metaheuristic algorithms-genetic algorithm(GA),particle swarm optimization(PSO),simulated annealing(SA),and differential evolution(DE)-highlighting their recent applications in water resources research and related areas.Then,a solute transport model is designed to illustrate how to apply and evaluate these four optimization algorithms in addressing challenges related to model parameter inversion.Finally,three noteworthy directions are presented to address the common challenges among current studies,including balancing the diverse exploration and centralized exploitation within metaheuristic algorithms,local approxi-mate error of the surrogate model,and the curse of dimensionality in spatial variational heterogeneous pa-rameters.In summary,this review paper provides theoretical insights and practical guidance for further advancements in groundwater inverse modeling studies.

Evolutionary Computation for Expensive Optimization:A Survey

Expensive optimization problem(EOP) widely exists in various significant real-world applications. However, EOP requires expensive or even unaffordable costs for evaluating candidate solutions, which is expensive for the algorithm to find a satisfactory solution. Moreover, due to the fast-growing application demands in the economy and society, such as the emergence of the smart cities, the internet of things, and the big data era, solving EOP more efficiently has become increasingly essential in various fields, which poses great challenges on the problem-solving ability of optimization approach for EOP. Among various optimization approaches, evolutionary computation(EC) is a promising global optimization tool widely used for solving EOP efficiently in the past decades. Given the fruitful advancements of EC for EOP, it is essential to review these advancements in order to synthesize and give previous research experiences and references to aid the development of relevant research fields and real-world applications. Motivated by this, this paper aims to provide a comprehensive survey to show why and how EC can solve EOP efficiently. For this aim, this paper firstly analyzes the total optimization cost of EC in solving EOP. Then, based on the analysis, three promising research directions are pointed out for solving EOP, which are problem approximation and substitution, algorithm design and enhancement, and parallel and distributed computation. Note that, to the best of our knowledge, this paper is the first that outlines the possible directions for efficiently solving EOP by analyzing the total expensive cost. Based on this, existing works are reviewed comprehensively via a taxonomy with four parts, including the above three research directions and the real-world application part. Moreover, some future research directions are also discussed in this paper. It is believed that such a survey can attract attention, encourage discussions, and stimulate new EC research ideas for solving EOP and related real-world applications more efficiently.

融合差分进化和Sine混沌的改进粒子群算法

将差分进化与Sine混沌相结合,提出一种改进的粒子群算法。利用Sine混沌映射对初始种群进行优化,提高了收敛速度;该算法通过引入非同步变化的学习因子的速度更新公式,引入随机惯性权重,使算法能够更好地兼顾全局搜索与局部优化;借鉴差分进化算法中的交叉操作,采用淘汰机制随机搜索策略,提高算法的全局搜索能力,提高算法收敛速度。为了验证融合差分进化和Sine混沌的改进粒子群算法(improved particle swarm optimization algorithm,IPSO)的性能,与基于压缩学习因子的粒子群算法(yield-based particle swarm optimization,YPSO)、自适应加权粒子群算法(self-adaptive particle swarm optimization,SPSO)等PSO相关算法以及蜘蛛蜂优化算法(spider wasp optimization,SWO)、能量谷算法(energy valley algorithm,EVA)等2023年最新算法相比较,验证融合差分进化和Sine混沌的改进粒子群算法(IPSO)的有效性。在不同维度下解决12个常用基准函数,对12个测试函数进行实验,并与其他的几种算法进行比较,实验结果表明,改进后的PSO算法收敛速度快,收敛精度高。

改进教与学算法的静压推力滑动轴承优化

为了使静压推力滑动轴承在运行过程中功率损失最小,提出了改进的教与学算法(DWTLBO),对静压推力滑动轴承进行优化设计。与其它经典的智能优化算法如粒子群算法(PSO)、差分进化算法(DE)和教与学算法(TLBO)相比,该算法在学习阶段引入差分进化算子增加了各组之间的交叉率,进一步提高算法的多样性和局部搜索能力,避免早熟收敛。通过建立推力轴承模型,设计了轴承阶梯半径,油槽凹口半径,润滑油粘度,润滑油流量四个设计变量,采用改进的教与学算法对模型的相关参数进行优化。优化结果表明,提出的改进算法与传统的教与学算法相比,获得模型的最优解更佳,有利于在以后的工程优化中提高模型的设计精度。

SCMOPSO算法的陆军合成旅协同多任务分配方法

针对陆军合成旅作战任务多样化、作战力量多类别带来的协同指挥难的问题,提出一种基于SCMOPSO算法的陆军合成旅协同多任务分配方法。根据陆军合成旅协同作战特性建立了协同多任务分配模型,提出一种SCMOPSO算法对该模型进行求解,设计了不同作战场景进行仿真验证。仿真结果表明,该方法显著降低了陆军合成旅协同多任务分配方案的任务执行时间与成本消耗,且所提SCMOPSO算法在各项评价指标中均优于传统的NSGA-II与MOPSO算法。

基于DG不确定仿射模型的综合能源系统低碳优化规划方法

针对风电、光伏等新能源分布式发电系统(distributedgenerator,DG)受环境因素变化导致出力不确定、现有碳交易模型交易价格固定导致减碳成本增多的问题,提出基于DG不确定仿射模型的综合能源系统低碳优化规划方法。首先,根据环境条件建立基于矩阵形式仿射算法的DG出力模型,降低DG出力不确定性对综合能源系统优化规划的影响。其次,将碳排放量作为惩罚措施引入综合能源系统优化规划中,改进传统碳交易模型,降低综合能源系统碳排放量。然后,基于差分进化粒子群优化算法,对建立的综合能源系统低碳规划模型求解,避免算法在寻优过程陷入局部最优。最后,在IEEE33节点系统上的仿真结果表明,所提规划方法比传统随机优化和区间优化规划方法分别降低了8.68%和2.93%的总投资成本,比传统固定碳交易价格模型降低了6.28%的碳排放量。