Immune-based algorithms for dynamic optimization

The main problem with biologically inspired algorithms (like evolutionary algorithms or particle swarm optimization) when applied to dynamic optimization is to force their readiness for continuous search for new optima occurring in changing locations. Immune-based algorithm, being an instance of an algorithm that adapt by innovation seem to be a perfect candidate for continuous exploration of a search space. In this paper we describe various implementations of the immune principles and we compare these instantiations on complex environments.     

带极值抖动的变尺度粒子群优化算法

为克服粒子群优化算法容易陷入局部最优解的问题,提出一种带极值抖动的变尺度粒子群优化算法,该算法在粒子进化过程中动态调整学习因子,改善粒子的搜索性能,利用极值抖动方法帮助粒子逃离局部最优解,采用变尺度方法逐步缩小算法的优化范围,提高算法搜索密度。实验表明,该算法对9个具有代表性的基准测试函数,其优化效率及优化精度均优于以往提出的典型粒子群优化改进算法。     

Sequential approximation optimization assisted particle swarm optimization for expensive problems

In this paper, an efficient sequential approximation optimization assisted particle swarm optimization algorithm is proposed for optimization of expensive problems. This algorithm makes a good balance between the search ability of particle swarm optimization and sequential approximation optimization. Specifically, the proposed algorithm uses the optima obtained by sequential approximation optimization in local regions to replace the personal historical best particles and then runs the basic particle swarm optimization procedures. Compared with particle swarm optimization, the proposed algorithm is more efficient because the optima provided by sequential approximation optimization can direct swarm particles to search in a more accurate way. In addition, a space partition strategy is proposed to constraint sequential approximation optimization in local regions. This strategy can enhance the swarm diversity and prevent the preconvergence of the proposed algorithm. In order to validate the proposed algorithm, a lot of numerical benchmark problems are tested. An overall comparison between the proposed algorithm and several other optimization algorithms has been made. Finally, the proposed algorithm is applied to an optimal design of bearings in an all-direction propeller. The results show that the proposed algorithm is efficient and promising for optimization of the expensive problems.     

基于参数方程处理等式约束优化的粒子群算法

针对目前已有的粒子群优化算法求解有等式约束优化问题时对收敛速度和解的精度的影响,提出了一种新的基于参数方程的粒子群优化算法.它是粒子群在初始化和选代进化过程中使用求解参数方程的方法处理等式约束设计出的粒子群优化算法.数值实验结果表明,新算法是有效的.它不仅提高了收敛速度和解的精度,而且是一种通用的智能算法.     

基于混沌粒子群算法的物流配送路径优化

通过结合混沌的遍历性和粒子群的快速性的优点,提出了一种用于求解物流配送路径优化问题的混沌粒子群优化算法。该算法利用混沌变量产生初始粒子群,对子代部分粒子群进行微小扰动,随着搜索过程深入逐步调整扰动幅度,通过调整惯性权重因子克服标准PSO算法的早熟和易陷入局部最优值等缺陷。将混沌粒子群优化算法用于物流配送路径优化,建立了数学模型,在此基础上设计了相应的算法。将该算法和遗传算法、标准粒子群算法进行比较,证明了其收敛速度和寻优能力的优越性。     

面向列车运行调整问题的粒子群算法研究*

列车运行调整问题是一种特殊的NP完全问题,不仅具有众多约束,并且有着列车等级要求和延迟传播限制,使得该问题搜索空间庞大,可行解范围狭小,往往难以获得较优解。为求解列车运行调整问题,针对此特殊性,将捕食搜索策略思想引入到粒子群算法中,并在此基础上提出一种速度限制的调整方式,同时辅以自适应控制,使得算法在大范围搜索时更易跳出局部最小解,而在小范围搜索时粒子飞行速度更慢,搜索更精确。将该算法用于列车运行调整问题,所得调整方案比遗传算法和普通粒子群算法结果更逼近原开行方案。     

An effective cellular particle swarm optimization for parameters optimization of a multi-pass milling process

In the milling process, the selection of machining parameters is very important as these parameters determine the processing time, quality, cost and so on, especially in the high-accuracy machine tools. However, the parameters optimization of a multi-pass milling process is a nonlinear constrained optimization problem which is difficult to be solved by the traditional optimization techniques. Therefore, in order to solve this problem effectively, this paper proposes a novel parameters optimization method based on the cellular particle swarm optimization (CPSO). To address the constraints efficiently, the proposed method combines two constraints handling techniques, including the penalty function method and the constraints handling strategy of PSO. In the proposed CPSO, the smart cell constructs its neighborhood with self-adaptive function and constraints handling techniques, which guide the unfeasible particles to move to the feasible regions and search for better solutions. A case is adopted and solved to illustrate the effectiveness of the proposed CPSO algorithm. The results of the experiment study are analyzed and compared with those of the previous algorithms. The experimental results show that the proposed approach outperforms other algorithms and has achieved significant improvement.     

不确定可靠性优化问题的多目标粒子群优化算法

针对元件可靠性为区间值的系统可靠性优化问题, 提出一种区间多目标粒子群优化方法. 首先, 建立问题的区间多目标优化模型; 然后, 利用粒子群算法优化该模型, 定义一种不精确Pareto 支配关系, 并给出编码、约束处理、外部存储器更新、领导粒子选择等关键问题的解决方法; 最后, 将该方法应用于可靠性优化问题实例, 验证了方法的有效性.

     

基于粒子群优化算法的约束布局优化

布局优化是NP难问题，也是复杂的非线性约束优化问题．针对这个问题，将新的基于粒子群优化的方法应用于布局参数的优化，提出了适合粒子群优化的约束处理，并通过与直接搜索算法的混合，加强了算法在局部区域的搜索能力．通过实例将该算法与乘子法以及基于遗传算法的布局优化方法进行了比较．仿真结果表明，该算法可以提高布局优化问题解的质量，同时降低计算费用．     

并行定向扰动的混合粒子群优化算法

针对全向变异易使粒子失去已有的有利搜索信息的问题, 提出了一种并行定向变异的混合粒子群优化算法。该算法以当前群体最优位置为基准, 用变异信息矩阵和混沌位置变异矩阵对群体进行并行定向扰动, 有效利用了现有的有利搜索信息。该算法将并行定向变异与序列二次规划法融为一体, 实现了全局搜索和局部寻优的统一。仿真实验和比较分析结果表明并行定向变异混合粒子群优化算法具有良好的、稳定的优化效果。     

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.     

A hybrid algorithm based on particle swarm and chemical reaction optimization

In this paper, a hybrid method for optimization is proposed, which combines the two local search operators in chemical reaction optimization with global search ability of for global optimum. This hybrid technique incorporates concepts from chemical reaction optimization and particle swarm optimization, it creates new molecules (particles) either operations as found in chemical reaction optimization or mechanisms of particle swarm optimization. Moreover, some technical bound constraint handling has combined when the particle update in particle swarm optimization. The effects of model parameters like InterRate, γ, Inertia weight and others parameters on performance are investigated in this paper. The experimental results tested on a set of twenty-three benchmark functions show that a hybrid algorithm based on particle swarm and chemical reaction optimization can outperform chemical reaction optimization algorithm in most of the experiments. Experimental results also indicate average improvement and deviate over chemical reaction optimization in the most of experiments.     

A new hybrid approach for dynamic continuous optimization problems

A new hybrid approach for dynamic optimization problems with continuous search spaces is presented. The proposed approach hybridizes efficient features of the particle swarm optimization in tracking dynamic changes with a new evolutionary procedure. In the proposed dynamic hybrid PSO (DHPSO) algorithm, the swarm size is varied in a self-regulatory manner. Inspired from the microbial life, the particles can reproduce infants and the old ones die. The infants are especially reproduced by high potential particles and located near the local optimum points, using the quadratic interpolation method. The algorithm is adapted to perform in continuous search spaces, utilizing continuous movement of the particles and using Euclidian norm to define the neighborhood in the reproduction procedure. The performance of the new proposed approach is tested against various benchmark problems and compared with those of some other heuristic optimization algorithms. In this regard, different types of dynamic environments including periodic, linear and random changes are taken with different performance metrics such as real-time error, offline performance and offline error. The results indicate a desirable better efficiency of the new algorithm over the existing ones.     

基于改进粒子群算法的PID控制器参数自整定

针对标准粒子群算法寻优精度不高、易出现早熟收敛等缺陷,提出一种自适应混沌移民变异粒子群算法IPSO。该算法通过引入基因距离来反映粒子间合作与竞争的隐性知识,使粒子种群的多样性得到量化,采取自适应混沌移民变异策略对陷入聚集区域的粒子进行处理,使之获得继续搜索的能力,从而防止算法过早陷入局部最优。仿真结果表明,IPSO算法在PID控制器参数寻优问题上具有遗传算法和标准粒子群算法无法比拟的优势。     

一种混合粒子群算法的贝叶斯网络优化模型

传统的粒子群优化算法通过群体中粒子间的合作和竞争进行群体智能指导优化搜索,算法收敛速度快,但较易陷入局部较优值,进入早熟状态。为了解决这个问题,提出了一种混合粒子群算法的贝叶斯网络优化模型,它可以通过当前所选择的较优解群构造一个贝叶斯网络和联合概率分布模型,利用这个模型进行采样得到更优解,用其可随机替换掉PSO中的一些粒子或个体最优解;同时利用粒子群算法对当前选择出的较优解群进行深度搜索,并将得到的最优解融入到较优解群中。分析可知,该方法可以提高算法有效性和可靠性。     

一种催化粒子群算法及其性能分析

针对粒子群算法（PSO）在解决高维、多模复杂问题时容易陷入局部最优的问题,提出了一种新颖的混合算法—催化粒子群算法（CPSO）。在CPSO优化过程中,种群中的粒子始终保持其个体历史最优值pbests。CPSO种群更新由改造PSO、横向交叉以及垂直交叉三个搜索算子交替进行,其中,每个算子产生的中庸解均通过贪婪思想产生占优解pbests,并作为下一个算子的父代种群。在CPSO中,纵横交叉算法（CSO）作为PSO的加速催化剂,一方面通过横向交叉改善PSO的全局收敛性能,另一方面通过纵向交叉维持种群的多样性。对6个典型benchmark函数的仿真结果表明,相比其它主流PSO变体,CPSO在全局收敛能力和收敛速率方面具有明显优势。     

迭代粒子群算法及其在间歇过程鲁棒优化中的应用

针对无状态独立约束和终端约束的间歇过程鲁棒优化问题，将迭代方法与粒子群优化算法相结合，提出了迭代粒子群算法．对于该算法，首先将控制变量离散化，用标准粒子群优化算法搜索离散控制变量的最优解．然后在随后的迭代过程中将基准移到刚解得的最优值处，同时收缩控制变量的搜索域，使优化性能指标和控制轨线在迭代过程中不断趋于最优解．算法简洁、可行、高效，避免了求解大规模微分方程组的问题．对一个间歇过程的仿真结果证明了迭代粒子群算法可以有效地解决无状态独立约束和终端约束的间歇过程鲁棒优化问题．     

嵌入局部一维搜索技术的混合粒子群优化算法*

通过将粒子群优化算法(PSO)与经典局部一维搜索技术相结合,提出一种嵌入局部一维搜索技术的混合粒子群优化算法(LLS-PSO)。该算法在基本粒子群优化算法中引入一维搜索技术,选取最优粒子进行局部一维搜索,增强了在最优点附近的局部搜索能力,以加快算法的收敛速度。对三个经典复杂优化问题进行数值实验,并与基本PSO算法进行比较。实验分析和结果表明,LLS-PSO具有更好的优化性能。     

混沌粒子群算法及其在发电资源调度中的应用

为了有效地解决水火电力系统资源短期优化调度问题,提出了一种基于混沌粒子群算法的调度方案。设计了水火电力系统资源调度问题的数学模型,给出了混沌粒子群调度算法的框架,通过引入最优粒子的混沌搜索机制、优势粒子和劣势粒子的权重自适应调节机制,从而使算法具有动态自适应性,能够较容易地跳出局部最优。实验结果表明,本算法方案能有效解决水火发电资源调度问题,具有较好的应用价值。     

带审敛因子的变邻域粒子群算法

针对基本粒子群算法在求解高维空间中的复杂多峰函数时容易发生早熟收敛而陷入局部最优的问题,汲取变邻域搜索算法全局搜索的优势,提出了带审敛因子的变邻域粒子群算法.首先由基本粒子群的快速搜索能力得到较优的群体;然后通过审敛因子判断发生早熟收敛的粒子,并利用变邻域搜索算法的全局搜索能力对陷入早熟收敛的粒子进行优化,从而得到全局最优.相关实验表明,带审敛因子的粒子群算法的性能较常规粒子群算法更加优越.