解决作业车间调度的微粒群退火算法*

针对微粒群优化算法在求解作业车间调度问题时存在的易早熟、搜索准确度差等缺点,在微粒群优化算法的基础上引入了模拟退火算法,从而使得算法同时具有全局搜索和跳出局部最优的能力,并且增加了对不可行解的优化,从而提高了算法的搜索效率;同时,在模拟退火算法中引入自适应温度衰变系数,使得SA算法能根据当前环境自动调整搜索条件,从而避免了微粒群优化算法易早熟的缺点。对经典JSP问题的仿真实验表明,与其他算法相比,该算法是一种切实可行、有效的方法。     

An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis

Clustering is a popular data analysis and data mining technique. A popular technique for clustering is based on k-means such that the data is partitioned into K clusters. However, the k-means algorithm highly depends on the initial state and converges to local optimum solution. This paper presents a new hybrid evolutionary algorithm to solve nonlinear partitional clustering problem. The proposed hybrid evolutionary algorithm is the combination of FAPSO (fuzzy adaptive particle swarm optimization), ACO (ant colony optimization) and k-means algorithms, called FAPSO-ACO–K, which can find better cluster partition. The performance of the proposed algorithm is evaluated through several benchmark data sets. The simulation results show that the performance of the proposed algorithm is better than other algorithms such as PSO, ACO, simulated annealing (SA), combination of PSO and SA (PSO–SA), combination of ACO and SA (ACO–SA), combination of PSO and ACO (PSO–ACO), genetic algorithm (GA), Tabu search (TS), honey bee mating optimization (HBMO) and k-means for partitional clustering problem.     

机载雷达组网多站测角被动跟踪轨迹优化研究

研究多观测器轨迹优化控制问题,由于多站测角被动跟踪系统运行存在误差,用机载雷达组网的可移动传感器采集信息,可对雷达载体轨迹优化进行研究,利用控制雷达载体的飞行轨迹可有效解决跟踪目标的弱观测性及估计器的稳定性。为了改善传统轨迹优化算法容易陷入早熟收敛和局部最小的问题,提出一种模拟退火(Simulated Annealing,SA)和粒子群优化(Particle Swarm Optimization,PSO)算法的混合优化方法(SA-PSO)。在给出了角度信息的适应度函数表达式基础上,结合模拟退火算法的局部搜索能力和粒子群优化算法的全局搜索能力,提高优化算法的收敛速度、精度以及全局搜索能力。实验证明,改进的混合算法对雷达载体轨迹优化有效,并减小对机动目标的被动跟踪误差。     

A hybrid biogeography-based optimization algorithm for job shop scheduling problem

In this paper, a hybrid biogeography-based optimization (HBBO) algorithm has been proposed for the job-shop scheduling problem (JSP). Biogeography-based optimization (BBO) is a new bio-inpired computation method that is based on the science of biogeography. The BBO algorithm searches for the global optimum mainly through two main steps: migration and mutation. As JSP is one of the most difficult combinational optimization problems, the original BBO algorithm cannot handle it very well, especially for instances with larger size. The proposed HBBO algorithm combines the chaos theory and “searching around the optimum” strategy with the basic BBO, which makes it converge to global optimum solution faster and more stably. Series of comparative experiments with particle swarm optimization (PSO), basic BBO, the CPLEX and 14 other competitive algorithms are conducted, and the results show that our proposed HBBO algorithm outperforms the other state-of-the-art algorithms, such as genetic algorithm (GA), simulated annealing (SA), the PSO and the basic BBO.     

A hybrid particle swarm optimization and its application in neural networks

In this paper, a novel particle swarm optimization model for radial basis function neural networks (RBFNN) using hybrid algorithms to solve classification problems is proposed. In the model, linearly decreased inertia weight of each particle (ALPSO) can be automatically calculated according to fitness value. The proposed ALPSO algorithm was compared with various well-known PSO algorithms on benchmark test functions with and without rotation. Besides, a modified fisher ratio class separability measure (MFRCSM) was used to select the initial hidden centers of radial basis function neural networks, and then orthogonal least square algorithm (OLSA) combined with the proposed ALPSO was employed to further optimize the structure of the RBFNN including the weights and controlling parameters. The proposed optimization model integrating MFRCSM, OLSA and ALPSO (MOA-RBFNN) is validated by testing various benchmark classification problems. The experimental results show that the proposed optimization method outperforms the conventional methods and approaches proposed in recent literature.     

求解工程约束优化问题的PSO-ABC混合算法*

针对包含约束条件的工程优化问题,提出了基于人工蜂群的粒子群优化PSO-ABC算法。将PSO中较优的粒子作为ABC算法的蜜源,并使用禁忌表存储其局部极值,克服粒子群优化算法易陷入局部最优的缺陷。采用可行性规则进行约束处理,将粒子种群分为可行子群和不可行子群,并在ABC算法产生蜜源的过程中保留部分较优的可行解和不可行解的信息,弥补了可行性规则处理最优点位于约束边界附近的问题时存在的不足。四个典型工程优化设计的实验结果表明,该算法能够寻得更优的约束最优化解,且稳健性更强。     

一种用于PID控制参数优化的混合果蝇算法

针对果蝇优化算法（ FOA）收敛速度快但寻优精度低的缺点,为了改善果蝇算法的优化性能,提出一种混合果蝇优化算法（ HFOA）。HFOA采用分段优化的思想,在优化过程后期采用收敛稳定性较好的粒子群优化（ PSO）算法优化果蝇算法中果蝇个体飞行距离和味道浓度的判定值,采用误差性能指标积分准则ITAE作为适应度函数,并将优化方案应用于一类不稳定系统的PID控制。Matlab仿真验证表明：HFOA计算高效,具有良好的稳定性,收敛精度高,进而验证了HFOA应用于PID控制参数优化是可行而有效的。     

A comparative study of the improvement of performance using a PSO modified by ACO applied to TSP

Swarm-inspired optimization has become very popular in recent years. Particle swarm optimization (PSO) and Ant colony optimization (ACO) algorithms have attracted the interest of researchers due to their simplicity, effectiveness and efficiency in solving complex optimization problems. Both ACO and PSO were successfully applied for solving the traveling salesman problem (TSP). Performance of the conventional PSO algorithm for small problems with moderate dimensions and search space is very satisfactory. As the search, space gets more complex, conventional approaches tend to offer poor solutions. This paper presents a novel approach by introducing a PSO, which is modified by the ACO algorithm to improve the performance. The new hybrid method (PSO–ACO) is validated using the TSP benchmarks and the empirical results considering the completion time and the best length, illustrate that the proposed method is efficient.     

基于微粒群算法与模拟退火算法的协同进化方法

提出了一种基于模拟退火与微粒群算法的协同进化方法,利用了微粒群算法的易实现性、局部快速收敛性以及模拟退火算法的全局收敛性.通过两种算法的协同搜索,可以有效克服微粒群算法的早熟收敛.仿真结果表明,本文的协同进化方法不仅具有较好的全局收敛性能,而且具有较快的收敛速度.文章从理论上证明了该方法以概率1收敛于全局最优解.     

二进制粒子群优化算法在化工优化问题中的应用

优化问题是化工过程的一个主要问题,而由化工问题建模所得到的优化问题大多较为复杂,此时要求的优化算法具有良好的优化性能。粒子群优化算法是新近发展起来的一种优化算法,但其对多极值函数的优化时,易陷局部极值。本文在分析粒子群优化算法的机理、考虑二进制比十进制更易于学习等的基础上,提出采用二进制表示粒子群优化算法,使每个粒子更易于从个体极值与全局极值中学习,从而使算法具有更强的搜索能力与更快的收敛速度,性能测试说明了所提出的算法是有效的．最后将算法用于求解换热网络的优化问题,取得良好效果。     

一种模拟退火和粒子群混合优化算法

针对粒子群优化算法(PSO)容易陷入局部极值点、进化后期收敛慢和优化精度较差等缺点.把模拟退火技术(SA)引入到PSO箅法中,提出了一种混合优化算法.混合优化算法在各温度下依次进行PSO和SA搜索,是一种两层的串行结构.由于PSO提供了并行搜索结构,所以,混合优化算法使SA转化成并行SA算法.SA的概率突跳性保证了种群的多样性,从而防止PSO算法陷入局部极小.混合优化算法保持了PSO算法简单容易实现的特点,改善了算法的全局优化能力,提高了算法的收敛速度和计算精度.仿真结果表明,混合优化算法的优化性能优于基本PSO算法.     

Hybrid BFOA–PSO algorithm for automatic generation control of linear and nonlinear interconnected power systems

In the bacteria foraging optimization algorithm (BFAO), the chemotactic process is randomly set, imposing that the bacteria swarm together and keep a safe distance from each other. In hybrid bacteria foraging optimization algorithm and particle swarm optimization (hBFOA–PSO) algorithm the principle of swarming is introduced in the framework of BFAO. The hBFOA–PSO algorithm is based on the adjustment of each bacterium position according to the neighborhood environment. In this paper, the effectiveness of the hBFOA–PSO algorithm has been tested for automatic generation control (AGC) of an interconnected power system. A widely used linear model of two area non-reheat thermal system equipped with proportional-integral (PI) controller is considered initially for the design and analysis purpose. At first, a conventional integral time multiply absolute error (ITAE) based objective function is considered and the performance of hBFOA–PSO algorithm is compared with PSO, BFOA and GA. Further a modified objective function using ITAE, damping ratio of dominant eigenvalues and settling time with appropriate weight coefficients is proposed to increase the performance of the controller. Further, robustness analysis is carried out by varying the operating load condition and time constants of speed governor, turbine, tie-line power in the range of +50% to ?50% as well as size and position of step load perturbation to demonstrate the robustness of the proposed hBFOA–PSO optimized PI controller. The proposed approach is also extended to a non-linear power system model by considering the effect of governor dead band non-linearity and the superiority of the proposed approach is shown by comparing the results of craziness based particle swarm optimization (CRAZYPSO) approach for the identical interconnected power system. Finally, the study is extended to a three area system considering both thermal and hydro units with different PI coefficients and comparison between ANFIS and proposed approach has been provided.     

基于粒子群优化算法的并行模拟退火算法

针对模拟退火（simulated annealing,SA）算法收敛速度慢,随机采样策略缺乏记忆能力,算法内在的串行性使其具有并行化问题依赖等缺点,提出了基于粒子群优化（particle swarm optimization,PSO）算法的并行模拟退火算法。该算法利用粒子群优化算法中个体的记忆功能引导算法在解空间中开展精细搜索,在反向学习算法基础上设计新的反向转动操作机制增加了算法的多样性,借助PSO的天然并行性克服了SA的并行问题依赖性,并在集群上实现了多Agent协同进化的改进算法。对Toy模型的蛋白质结构预测问题进行了仿真实验,结果表明该算法能有效提高求解问题的质量和效率。     

基于混沌和差分进化的混合粒子群优化算法

研究粒子群算法优化问题,由于标准粒子群优化算法(PSO)在高维复杂函数优化中易早收敛,影响全系统优化。为改进的混合粒子群优化算法,提出了一种基于混沌和差分进化的混合粒子群优化算法(CDEHPSO)。把基于Logistic映射的混沌序列引入到种群初始化操作中。在算法进化过程中,通过一种粒子早熟判断机制,在基本粒子群优化算法中引入了差分变异、交叉和选择操作,对早熟粒子个体进行差分进化操作,从而维持了种群的多样性并有效避免了算法陷入局部最优。仿真结果表明,相比于粒子群优化算法和差分进化算法(DE),CDEHPSO算法具有收敛速度快、搜索能力强的优点。     

A novel hybrid gravitational search and pattern search algorithm for load frequency control of nonlinear power system

In this paper, a hybrid gravitational search algorithm (GSA) and pattern search (PS) technique is proposed for load frequency control (LFC) of multi-area power system. Initially, various conventional error criterions are considered, the PI controller parameters for a two-area power system are optimized employing GSA and the effect of objective function on system performance is analyzed. Then GSA control parameters are tuned by carrying out multiple runs of algorithm for each control parameter variation. After that PS is employed to fine tune the best solution provided by GSA. Further, modifications in the objective function and controller structure are introduced and the controller parameters are optimized employing the proposed hybrid GSA and PS (hGSA-PS) approach. The superiority of the proposed approach is demonstrated by comparing the results with some recently published modern heuristic optimization techniques such as firefly algorithm (FA), differential evolution (DE), bacteria foraging optimization algorithm (BFOA), particle swarm optimization (PSO), hybrid BFOA-PSO, NSGA-II and genetic algorithm (GA) for the same interconnected power system. Additionally, sensitivity analysis is performed by varying the system parameters and operating load conditions from their nominal values. Also, the proposed approach is extended to two-area reheat thermal power system by considering the physical constraints such as reheat turbine, generation rate constraint (GRC) and governor dead band (GDB) nonlinearity. Finally, to demonstrate the ability of the proposed algorithm to cope with nonlinear and unequal interconnected areas with different controller coefficients, the study is extended to a nonlinear three unequal area power system and the controller parameters of each area are optimized using proposed hGSA-PS technique.     

A genetic algorithm and a particle swarm optimizer hybridized with Nelder–Mead simplex search

This paper integrates Nelder–Mead simplex search method (NM) with genetic algorithm (GA) and particle swarm optimization (PSO), respectively, in an attempt to locate the global optimal solutions for the nonlinear continuous variable functions mainly focusing on response surface methodology (RSM). Both the hybrid NM–GA and NM–PSO algorithms incorporate concepts from the NM, GA or PSO, which are readily to implement in practice and the computation of functional derivatives is not necessary. The hybrid methods were first illustrated through four test functions from the RSM literature and were compared with original NM, GA and PSO algorithms. In each test scheme, the effectiveness, efficiency and robustness of these methods were evaluated via associated performance statistics, and the proposed hybrid approaches prove to be very suitable for solving the optimization problems of RSM-type. The hybrid methods were then tested by ten difficult nonlinear continuous functions and were compared with the best known heuristics in the literature. The results show that both hybrid algorithms were able to reach the global optimum in all runs within a comparably computational expense.     

基于粒子群优化算法的PID控制器参数整定

PID控制器的性能完全依赖于其参数的整定和优化，但参数的整定及在线自适应调整对常规的PID控制器是难以解决的问题。根据粒子群算法具有对整个参数空间进行高效并行搜索的特点，提出了一种基于粒子群优化算法整定PID控制器参数的设计方法，并定义了一种新的性能指标函数来评价PID控制器的性能。现以二阶的船舶控制装置为研究对象，运用粒子群优化方法对PID控制器参数进行了寻优研究。仿真结果表明，该方法比一般PID参数整定方法具有更好的控制性能指标，有着一定的工程应用价值。     

一种自适应混合粒子群优化算法及其应用*

为提高粒子群算法的寻优精度,提出一种将单纯形法(SM)和粒子群(PSO)算法相结合的自适应混合粒子群优化(AHPSO)算法,该算法根据进化需要动态调整粒子的惯性权重,并在进化停滞时使用SM优化。通过仿真实验证明了AHPSO的寻优性能优于SPSO和SMPSO。将AHPSO用于某航空发动机的PID参数优化,其整定性能优于现有的工业方法和其他PSO算法。     

基于模拟退火的花朵授粉优化算法

针对花朵授粉算法寻优精度低、收敛速度慢、易陷入局部极小的不足,提出一种把模拟退火(SA)融入到花朵授粉算法中的混合算法。该算法通过SA的概率突跳策略使其避免陷入局部最优,并利用SA的全域搜索的性能增强算法的全局寻优能力。通过6个标准测试函数进行测试,仿真结果表明,改进算法在4个测试函数中能够找到理论最优值,其收敛精度、收敛速度、鲁棒性均比基本的花朵授粉算法(FPA)、蝙蝠算法(BA)、粒子群优化(PSO)算法及改进的粒子群算法有较大的提高;同时,对非线性方程组问题进行求解的算例应用也验证了改进算法的有效性。     

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。