Swarm intelligence for mixed-variable design optimization

Many engineering optimization problems frequently encounter continuous variables and discrete variables which adds considerably to the solution complexity.Very few of the existing methods can yield a globally optimal solution when the objective functions are non-convex and non-differentiable.This paper presents a hybrid swarm intelligence approach(HSIA)for solving these nonlinear optimization problems which contain integer,discrete,zero-one and continuous variables.HSIA provides an improvement in global search reliability in a mixed-variable space and converges steadily to a good solution.An approach to handle various kinds of variables and constraints is discussed.Comparison testing of several examples of mixed-variable optimization problems in the literature showed that the proposed approach is superior to current methods for finding the best solution,in terms of both solution quality and algorithm robustness.     

An adaptive ant colony system algorithm for continuous-space optimization problems

ＩＮＴＲＯＤＵＣＴＩＯＮＡｎｔｃｏｌｏｎｙａｌｇｏｒｉｔｈｍｓ (Ｈｅｒｔｚ ,ｅｔａｌ.,2 0 0 0 ) ,ｉｎｖｅｓｔｉｇａｔｅｄｓｙｓｔｅｍａｔｉｃａｌｌｙａｔｆｉｒｓｔｉｎＤｏｒｉｇｏ’ｓＰｈ .Ｄ .ｄｉｓｓｅｒｔａｔｉｏｎ ( 1 992 )ａｓｔｈｅｉｍｉ ｔａｔｉｏｎｏｆｔｈｅｆｏｏｄ ｓｅｅｋｉｎｇｂｅｈａｖｉｏｒｉｎａｎｔｓｏｃｉｅｔ ｉｅｓ,ｈａｖｅａｔｔｒａｃｔｅｄｔｈｅｇｒｅａｔａｔｔｅｎｔｉｏｎｏｆｒｅ ｓｅａｒｃｈｅｒｓｉｎｃｏｍｐｒｅｈｅｎｓｉｖｅｆｉｅｌｄｓｏｆｓｙｓｔｅｍｏｐｔｉ ｍｉｚａｔ…     

离散变量结构优化设计算法研究综述

对离散变量结构优化设计的算法作了总结，这些算法被分成传统的方法和现代方法。对其中的一些算法作了描述，如圆整法、相对差商法、模拟退火算法、遗传算法、Tabu搜索算法。指出了拓扑优化存在奇异最优解问题，并介绍了奇异最优解的处理方法。     

Hybrid discrete particle swarm optimization algorithm for capacitated vehicle routing problem

INTRODUCTION The vehicle routing problem (VRP), which was first introduced by Dantzig and Ramser (1959), is a well-known combinatorial optimization problem in the field of service operations management and logis- tics. The capacitated vehicle routing problem (CVRP) is an NP-hard problem for simultaneously determining the routes for several vehicles from a central depot to a set of customers, and then return to the depot without exceeding the capacity constraints of each vehicle. In pr…     

Application of honey-bee mating optimization on state estimation of a power distribution system including distributed generators

We present a new approach based on honey-bee mating optimization to estimate the state variables in distribution networks including distributed generators. The proposed method considers practical models of electrical equipments such as static var compensators, voltage regulators, and under-load tap changer transformers, which have usually nonlinear and discrete characteristics. The feasibility of the proposed approach is demonstrated by comparison with the methods based on neural networks, ant colony optimization, and genetic algorithms for two test systems, a network with 34-bus radial test feeders and a realistic 80-bus 20 kV network.     

混合交通网络设计问题的迭代降维算法(英文)

提出了一种优化的迭代降维算法求解混合交通网络设计问题. 混合(连续/离散) 交通网络设计问题常表示为一个带均衡约束的数学规划问题,上层通过新建路段和改善已有路段来优化网络性能,下层是一个传统的 Wardrop 用户均衡模型. 迭代降维算法的基本思想是降维,先保持一组变量(离散/连续) 不变,交替地对另一组变量(连续/离散) 实现最优化. 以迭代的形式反复求解连续网络设计和离散网络设计问题,直至最后收敛到最优解. 通过一个数值算例对算法的效果进行了验证.     

模拟退火算法在优化中的研究进展

模拟退火算法在处理全局优化、离散变量优化等困难问题中,具有传统优化算法无可比拟的优势.在计算机计算速度不断提高的条件下,具有不可低估的发展潜力和重要的研究价值.本文研究分析了基本的模拟退火算法原理,主要介绍了模拟退火算法的发展历史和特点,综合了近期关于模拟退火算法研究进展.本文在比较了模拟退火算法与传统的优化方法的基础上,最后指出了它的发展趋势和研究热点,对模拟退火算法在优化中的推广应用具有重要意义.     

An adaptive ant colony system algorithm for continuous-space optimization problems

Ant colony algorithms comprise a novel category of evolutionary computation methods for optimization problems, especially for sequencing-type combinatorial optimization problems. An adaptive ant colony algorithm is proposed in this paper to tackle continuous-space optimization problems, using a new objective-function-based heuristic pheromone assignment approach for pheromone update to filtrate solution candidates. Global optimal solutions can be reached more rapidly by self-adjusting the path searching behaviors of the ants according to objective values. The performance of the proposed algorithm is compared with a basic ant colony algorithm and a Square Quadratic Programming approach in solving two benchmark problems with multiple extremes. The results indicated that the efficiency and reliability of the proposed algorithm were greatly improved. Project (No. 9845-005) supported by National High-Tech. Research & Development Plan, China     

基于离散粒子群优化算法的造林规划设计

指出造林规划设计问题实质是一个离散约束优化问题。应用离散粒子群优化算法求解目标函数,以保证解的合理性法和惩罚函数法相结合处理约束条件。分析实例表明,离散粒子群优化算法可用来优化造林规划设计方案,与模拟退火算法比较,效果更好。此研究结果可为科学造林和最优化经营管理提供新思路,丰富粒子群应用领域。     

一种求解有约束优化问题的遗传算法

遗传算法是模拟生物进化机制新发展起来的一种搜索和优化方法，它是基于自然进化机制并且在寻找目标函数或在目标函数附近解决优化问题。遗传算法已在有约束优化问题领域得到应用，并显示出良好的发展前景。本文介绍了一种有约束优化问题的混合遗传算法，并通过实例验证了此方法是可行的和有效的。     

基于粒子群算法求解整数规划的改进方法

标准粒子群算法主要用于优化连续性,而对粒子群算法求解非线性整数规划,算法的粒子位置必须解决取整问题。基此,文章提出一种粒子位置最终取整的方法,以改进粒子群算法解决整数规划的具体过程。基准函数的仿真结果表明,改进后的取整方法的搜索成功率优于直接取整和随机取整,综合搜索效率更佳。     

离散多因素投资组合最优化模型的分枝定界算法

In this paper, a new branch-and-bound algorithm based on the Lagrangian dual relaxation and continuous relaxation is proposed for discrete multi-factor portfolio selection model with roundlot restriction in financial optimization. This discrete portfolio model is of integer quadratic programming problems. The separable structure of the model is investigated by using Lagrangian relaxation and dual search. Computational results show that the algorithm is capable of solving real-world portfolio problems with data from US stock market and randomly generated test problems with up to 120 securities.     

基于改进PSO算法的电力系统无功优化

本文采用的无功优化数学模型虑了系统的实际约束条件和无功调节手段,能够准确直观地反映电力系统无功优化的实际问题。在此基础上对基于粒子群算法的无功优化作了深入的研究,尝试利用粒子群优化算法对无功优化问题进行求解。对常规粒子群算法的搜索策略进行了改进,对约束条件、离散变量都采取了一定的改进处理措施。以IEEE-6节点系统为例进行了仿真计算,验证了本文模型和算法的正确性、适用性和经济性。     

最优问题的梯度神经网络的稳定性

研究了一类最优问题的求解。利用其梯度神经网络，得到了求解最优问题解的简单易行的方法，同时给出了判定最优问题解存在的一些充分条件。     

Scheduling of household power consumption for Step and Time-of-Use tariff system

This paper proposes a hybrid optimization to solve the scheduling of household power consumption for Step and Time-of-Use (TOU) tariff system. The target function is the cost of electricity, and the optimization object is total instantaneous power within a billing period. The control variables are starting moments of each household appliance. The optimization procedure is divided into two stages. Firstly, the prerequisite for minimal cost is calculated through mathematical analysis and generalized function theory. Secondly, the solution is obtained by using a heuristic algorithm in which the result of the first stage is considered to reduce the searching space. And an evaluation methodology is deduced to evaluate the optimization. The computer simulation demonstrates that the proposed approach can reduce the cost of electricity evidently in the sense of probability. The approach shows great value for embedded applications.     

改进的模拟退火算法求全局优化问题的最优解

围绕全局优化问题,对模拟退火算法进行了分析,针对过程变量优化问题中普遍存在的多峰现象,探讨了应用模拟退火算法求全局优化问题的最优解的方法．通过对线性问题和非线性问题的计算,表明该法能够有效地解决全局优化问题．     

基于蚁群优化和遗传操作的混合方法

尽管蚁群优化算法（ACO）在优化计算中已得到了很多应用,但在进行大规模优化时,其收敛时间过长仍是应用该算法的一个瓶颈．为了确保资源利用完成时间最小化和完成用户指定的最终期限延迟最小化,找到一个优化的调度方法,在计算网格中针对资源分配和调度提出了基于蚁群优化和遗传操作的混合方法．     

基于Pareto遗传算法的多目标优化

在工程实际当中存在着大量的多目标优化问题,传统的多目标优化方法存在着明显的缺陷.本文介绍一种基于Pareto最优概念的遗传算法来求解多目标优化问题.这种方法能够给出多目标优化问题的Pareto解集,而不是单纯的一个解,从而可以帮助决策者在Pareto解集中挑选适合设计要求的解作为最终解.     

改进遗传算法在电力系统无功优化中的应用

将遗传算法用于电力系统无功优化,并对传统遗传算法的编码方式、遗传操作算子、交叉和变异概率以及迭代终止判据等方面进行了改进,同时使用本文的算法对IEEE6及IEEE14节点系统进行了无功优化计算,结果表明改进后的遗传算法合理可行。     

求解约束全局优化问题的一类新的填充函数

求解全局优化问题的填充函数法的关键之一在于构造一个称为填充函数的辅助函数,文章提出了一类新的求解不等式约束的连续全局优化问题的填充函数,讨论了其填充性质. 为进一步设计求解算法提供理论基础.