Parametric Optimization Design of Aircraft Based on Hybrid Parallel Multi-objective Tabu Search Algorithm

For dealing with the multi-objective optimization problems of parametric design for aircraft, a novel hybrid parallel multi-objective tabu search （HPMOTS） algorithm is used. First, a new multi-objective tabu search （MOTS） algorithm is proposed. Comparing with the traditional MOTS algorithm, this proposed algorithm adds some new methods such as the combination of MOTS algorithm and ＂Pareto solution＂, the strategy of ＂searching from many directions＂ and the reservation of good solutions. Second, this article also proposes the improved parallel multi-objective tabu search （PMOTS） algorithm. Finally, a new hybrid algorithm--HPMOTS algorithm which combines the PMOTS algorithm with the non-dominated sorting-based multi-objective genetic algorithm （NSGA） is presented. The computing results of these algorithms are compared with each other and it is shown that the optimal result can be obtained by the HPMOTS algorithm and the computing result of the PMOTS algorithm is better than that of MOTS algorithm.

Even Search in a Promising Region for Constrained Multi-Objective Optimization

In recent years, a large number of approaches to constrained multi-objective optimization problems(CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However, an overly finetuned strategy or technique might overfit some problem types,resulting in a lack of versatility. In this article, we propose a generic search strategy that performs an even search in a promising region. The promising region, determined by obtained feasible non-dominated solutions, possesses two general properties.First, the constrained Pareto front(CPF) is included in the promising region. Second, as the number of feasible solutions increases or the convergence performance(i.e., approximation to the CPF) of these solutions improves, the promising region shrinks. Then we develop a new strategy named even search,which utilizes the non-dominated solutions to accelerate convergence and escape from local optima, and the feasible solutions under a constraint relaxation condition to exploit and detect feasible regions. Finally, a diversity measure is adopted to make sure that the individuals in the population evenly cover the valuable areas in the promising region. Experimental results on 45 instances from four benchmark test suites and 14 real-world CMOPs have demonstrated that searching evenly in the promising region can achieve competitive performance and excellent versatility compared to 11 most state-of-the-art methods tailored for CMOPs.

Quantum walk search algorithm for multi-objective searching with iteration auto-controlling on hypercube

Shenvi et al.have proposed a quantum algorithm based on quantum walking called Shenvi-Kempe-Whaley(SKW)algorithm,but this search algorithm can only search one target state and use a specific search target state vector.Therefore,when there are more than two target nodes in the search space,the algorithm has certain limitations.Even though a multiobjective SKW search algorithm was proposed later,when the number of target nodes is more than two,the SKW search algorithm cannot be mapped to the same quotient graph.In addition,the calculation of the optimal target state depends on the number of target states m.In previous studies,quantum computing and testing algorithms were used to solve this problem.But these solutions require more Oracle calls and cannot get a high accuracy rate.Therefore,to solve the above problems,we improve the multi-target quantum walk search algorithm,and construct a controllable quantum walk search algorithm under the condition of unknown number of target states.By dividing the Hilbert space into multiple subspaces,the accuracy of the search algorithm is improved from p_(c)=(1/2)-O(1/n)to p_(c)=1-O(1/n).And by adding detection gate phase,the algorithm can stop when the amplitude of the target state becomes the maximum for the first time,and the algorithm can always maintain the optimal number of iterations,so as to reduce the number of unnecessary iterations in the algorithm process and make the number of iterations reach t_(f)=(π/2)(?).

Do Search and Selection Operators Play Important Roles in Multi-Objective Evolutionary Algorithms:A Case Study

Multi-objective Evolutionary Algorithm (MOEA) is becoming a hot research area and quite a few aspects of MOEAs have been studied and discussed. However there are still few literatures discussing the roles of search and selection operators in MOEAs. This paper studied their roles by solving a case of discrete Multi-objective Optimization Problem (MOP): Multi-objective TSP with a new MOEA. In the new MOEA, We adopt an efficient search operator, which has the properties of both crossover and mutation, to generate the new individuals and chose two selection operators: Family Competition and Population Competition with probabilities to realize selection. The simulation experiments showed that this new MOEA could get good uniform solutions representing the Pareto Front and outperformed SPEA in almost every simulation run on this problem. Furthermore, we analyzed its convergence property using finite Markov chain and proved that it could converge to Pareto Front with probability 1. We also find that the convergence property of MOEAs has much relationship with search and selection operators.

A Hybrid Multi-Objective Evolutionary Algorithm for Optimal Groundwater Management under Variable Density Conditions

In this paper, a new hybrid multi-objective evolutionary algorithm （MOEA）, the niched Pareto tabu search combined with a genetic algorithm （NPTSGA）, is proposed for the management of groundwater resources under variable density conditions. Relatively few MOEAs can possess global search ability contenting with intensified search in a local area. Moreover, the overall searching ability of tabu search （TS） based MOEAs is very sensitive to the neighborhood step size. The NPTSGA is developed on the thought of integrating the genetic algorithm （GA） with a TS based MOEA, the niched Pareto tabu search （NPTS）, which helps to alleviate both of the above difficulties. Here, the global search ability of the NPTS is improved by the diversification of candidate solutions arising from the evolving genetic algorithm population. Furthermore, the proposed methodology coupled with a density-dependent groundwater flow and solute transport simulator, SEAWAT, is developed and its performance is evaluated through a synthetic seawater intrusion management problem. Optimization results indicate that the NPTSGA offers a tradeoff between the two conflicting objectives. A key conclusion of this study is that the NPTSGA keeps the balance between the intensification of nondomination and the diversification of near Pareto-optimal solutions along the tradeoff curves and is a stable and robust method for implementing the multi-objective design of variable-density groundwater resources.

Solving material distribution routing problem in mixed manufacturing systems with a hybrid multi-objective evolutionary algorithm

The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency. A multi-objective model was presented for the material distribution routing problem in mixed manufacturing systems, and it was solved by a hybrid multi-objective evolutionary algorithm (HMOEA). The characteristics of the HMOEA are as follows: 1) A route pool is employed to preserve the best routes for the population initiation; 2) A specialized best?worst route crossover (BWRC) mode is designed to perform the crossover operators for selecting the best route from Chromosomes 1 to exchange with the worst one in Chromosomes 2, so that the better genes are inherited to the offspring; 3) A route swap mode is used to perform the mutation for improving the convergence speed and preserving the better gene; 4) Local heuristics search methods are applied in this algorithm. Computational study of a practical case shows that the proposed algorithm can decrease the total travel distance by 51.66%, enhance the average vehicle load rate by 37.85%, cut down 15 routes and reduce a deliver vehicle. The convergence speed of HMOEA is faster than that of famous NSGA-II.

Optimal Polygonal Approximation of Digital Planar Curves Using Genetic Algorithm and Tabu Search

Three heuristic algorithms for optimal polygonal approximation of digital planar curves is presented. With Genetic Algorithm (GA), improved Genetic Algorithm (IGA) based on Pareto optimal solution and Tabu Search (TS), a near optimal polygonal approximation was obtained. Compared to the famous Teh chin algorithm, our algorithms have obtained the approximated polygons with less number of vertices and less approximation error. Compared to the dynamic programming algorithm, the processing time of our algorithms are much less expensive.

Localization of Voltage Regulators in Distribution Systems by a Mixed Genetic–Tabu Search Algorithm

The optimal allocation of regulators banks in distribution systems is a merely combinatorial problem in which the best points of installation correspond to the best benefit, considering the admitted objective function, without violating and operating limits. The objective function must be chosen so that its value represents the operation state of the system. As the problem possesses combinatorial nature, its complexity will increase exponentially with the number of possibilities. Systems with large numbers of nodes and / or with the possibility of installing more than one bank require a large number of calculations to find the solution. An additional issue is the fact that the problem does not have a continuous nature, presenting discontinuity points in the objective function, limiting the application of optimization methods based on gradients. Based on the nature of the problem two optimization methods were used to solve the problem: Genetic Algorithm (GA) and modified Tabu Search (TS). The GA function will scour the search space and find regions with local minima that are candidates to be the solution. On the other hand the TS provides local search in the regions defined by GA so that the overall optimum is achieved.

A Parallel Search System for Dynamic Multi-Objective Traveling Salesman Problem

This paper introduces a parallel search system for dynamic multi-objective traveling salesman problem. We design a multi-objective TSP in a stochastic dynamic environment. This dynamic setting of the problem is very useful for routing in ad-hoc networks. The proposed search system first uses parallel processors to identify the extreme solutions of the search space for each ofk objectives individually at the same time. These solutions are merged into the so-called hit-frequency matrix E. The solutions in E are then searched by parallel processors and evaluated for dominance relationship. The search system is implemented in two different ways master-worker architecture and pipeline architecture.

一种改进的Tabu Search算法及其在区域电网无功优化中的应用

提出将改进的Tabu(禁忌)搜索算法用于区域电网无功电压优化控制问题的求解.首先根据已知的实际电网的历史数据获得可行的初始解,然后对区域电网采用改进的禁忌搜索方法进行无功优化.在求解的过程中,由于对Tabu表中所记录的"移动"采取"有条件地释放Tabu表中的记录"这一策略,可以使搜索有效地跳出局部极小值点,更好地找到最优解.通过IEEE-14节点算例验证了该算法的有效性.

Tabu Search算法在优化配送路线问题中的应用

将ＴＳ算法应用到物流系统的配送路线优化问题中。在给出了此类问题的描述后，着重阐述了ＴＳ启发式算法的设计，编程实现此算法的要点。最后，用模拟算例对设计的算法进行了验证，计算结果是比较理想的。

Search for circular and noncircular critical slip surfaces in slope stability analysis by hybrid genetic algorithm

A local improvement procedure based on tabu search(TS) was incorporated into a basic genetic algorithm(GA) and a global optimal algorithm,i.e.,hybrid genetic algorithm(HGA) approach was used to search the circular and noncircular slip surfaces associated with their minimum safety factors.The slope safety factors of circular and noncircular critical slip surfaces were calculated by the simplified Bishop method and an improved Morgenstern-Price method which can be conveniently programmed,respectively.Comparisons with other methods were made which indicate the high efficiency and accuracy of the HGA approach.The HGA approach was used to calculate one case example and the results demonstrated its applicability to practical engineering.

Optimization Design of Halbach Permanent Magnet Motor Based on Multi-objective Sensitivity

The halbach permanent magnet synchronous motor(HPMSM)combines the advantages of permanent magnet motors and halbach arrays,which make it very suitable to act as a robot joint motor,and it can also be used in other fields,such as electric vehicles,wind power generation,etc.At first,the sizing equation is derived and the initial design dimensions are calculated for the HPMSM with the rated power of 275W,based on which the finite element parametric model of the motor is built up and the key structural parameters that affect the total harmonic distortion of air-gap flux density and output torque are determined by analyzing multi-objective sensitivity.Then the structure parameters are optimized by using the cuckoo search algorithm.Last,in view of the problem of local overheating of the motor,an improved stator slot structure is proposed and researched.Under the condition of the same outer dimensions,the electromagnetic performance of the HPMSM before and after the improvement are analyzed and compared by the finite element method.It is found that the improved HPMSM can obtain better performances.

基于改进Tabu Search算法的配电线路无功运行优化系统控制策略研究

在配电网无功补偿优化过程中,Tabu Search算法能够高效地将最优解搜索出来,因此,将Tabu Search算法应用于配电网无功补偿优化具有现实可操作性。本文对Tabu Search方法的原理进行了详细介绍,因为农村配电网线路具备很多特点,综合考量其并联电容器投切等问题及特点,本文在Tabu Search的基础上,进行了一系列改进,用来解决配电网的投切优化问题。提出了基于电容器投切分组的二进制编码优化,基于无功补偿的损耗降低特点,根据功率传输方向,对权重根据由低到高的顺序排列,然后进行二进制编码,从二进制编码串的末端进行移动;Tabu Search非常依赖于初始解,提出了当前时段处于运行退出状态时,根据无功缺额电容器的配置方式进行。

Test Cost Optimization Using Tabu Search

In order to deliver a complete reliable software product, testing is performed. As testing phase carries on, cost of testing process increases and it directly affects the overall project cost. Many a times it happens that the actual cost becomes more than the estimated cost. Cost is considered as the most important parameter with respect to software testing, in software industry. In recent year’s researchers have done a variety of work in the area of Cost optimization by using various concepts like Genetic Algorithm, simulated annealing and Automation in generation of test data etc. This paper proposes an efficient cost effective approach for optimizing the cost of testing using Tabu Search (TS), which will provide maximum code coverage along with the concepts of Dijkstra’s Algorithm which will be implemented in Aspiration criteria of Tabu Search in order to optimize the cost and generate a minimum cost path with maximum coverage.

3D Path Planning of the Solar Powered UAV in the Urban-Mountainous Environment with Multi-Objective and Multi-Constraint Based on the Enhanced Sparrow Search Algorithm Incorporating the Levy Flight Strategy

In response to practical application challenges in utilizing solar-powered unmanned aerial vehicle(UAV)for remote sensing,this study presents a three-dimensional path planning method tailored for urban-mountainous environment.Taking into account constraints related to the solar-powered UAV,terrain,and mission objectives,a multi-objective trajectory optimization model is transferred into a single-objective optimization problem with weight factors and multiconstraint and is developed with a focus on three key indicators:minimizing trajectory length,maximizing energy flow efficiency,and minimizing regional risk levels.Additionally,an enhanced sparrow search algorithm incorporating the Levy flight strategy(SSA-Levy)is introduced to address trajectory planning challenges in such complex environments.Through simulation,the proposed algorithm is compared with particle swarm optimization(PSO)and the regular sparrow search algorithm(SSA)across 17 standard test functions and a simplified simulation of urban-mountainous environments.The results of the simulation demonstrate the superior effectiveness of the designed improved SSA based on the Levy flight strategy for solving the established single-objective trajectory optimization model.

基于改进Tabu搜索算法的电力系统无功优化

本文将一种改进的 Tabu搜索算法 (MTSA)用于电力系统无功优化 ,建立了相应的数学模型 ,考虑了有功损耗费用和补偿费用 ,使得总费用最小。在一般 Tabu搜索算法的基础上 ,对搜索步长、禁忌表、不同循环起始点的选择以及算法终止判据等问题做了分析、讨论 ,并做了一些改进 ,使得更容易挑出局部最优解 ,保证可以搜索整个可行域 ,从而得到全局最优解的可能性更大。应用 MTSA对 IEEE6节点系统行了无功优化计算 ,与线性规划算法、Box算法进行了比较 ,结果表明 MTSA与 Box算法一类的随机搜索算法的优化结果相近 。

基于Tabu搜索方法的电力系统无功优化

将 Tabu搜索方法用于电力系统无功优化 ,采用二进制和十进制编码 2种方案。对IEEE30节点系统和 1 2 5节点山东省某地区电网进行了优化计算 ,并与简单遗传算法、结合模拟退火的遗传算法进行了比较 ,结果表明 Tabu搜索方法具有更强的全局寻优能力 ,可用于运行方式安排 。

电机电磁场逆问题数值计算的改进 TABU 算法

在分析现有ＴＡＢＵ算法基础上，本文提出了一种通用的连续变量全局优化ＴＡＢＵ算法；典型数学函数验证和应用实例表明：本文算法仅用模拟退火（ＳＡ）算法２０％左右的迭代次数便可得到略好于ＳＡ算法的（全局）最优解。

基于遗传算法与Tabu搜索的拆卸序列优化算法

为研究废弃装配体的拆卸序列优化,首先提出了建立装配体的干涉—自由矩阵,作为描述其结构的数学模型.其次,运用遗传算法原理,提出面向装配体拆卸序列自动生成及优化的计算模型.依据初始输入的若干拆卸序列和其它控制参数,由程序搜寻几何上可行的最佳拆卸序列.这里是以装配体在拆卸过程中具有最少的换向次数为优化目标.最后,鉴于遗传计算的未熟早敛问题,提出建立Tabu搜索与遗传算法的组合优化算法.通过把Tabu搜索的集中与分散策略引入遗传算法,可望获得更加健壮的搜索行为.大量的实例验证表明,用这种方法解决装配体拆卸序列的优化问题,所生成的可行拆卸序列在适应度函数值、数量、分布范围等方面均优于单纯的由遗传算法生成的结果.