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.

GNN Representation Learning and Multi-Objective Variable Neighborhood Search Algorithm for Wind Farm Layout Optimization

With the increasing demand for electrical services,wind farm layout optimization has been one of the biggest challenges that we have to deal with.Despite the promising performance of the heuristic algorithm on the route network design problem,the expressive capability and search performance of the algorithm on multi-objective problems remain unexplored.In this paper,the wind farm layout optimization problem is defined.Then,a multi-objective algorithm based on Graph Neural Network(GNN)and Variable Neighborhood Search(VNS)algorithm is proposed.GNN provides the basis representations for the following search algorithm so that the expressiveness and search accuracy of the algorithm can be improved.The multi-objective VNS algorithm is put forward by combining it with the multi-objective optimization algorithm to solve the problem with multiple objectives.The proposed algorithm is applied to the 18-node simulation example to evaluate the feasibility and practicality of the developed optimization strategy.The experiment on the simulation example shows that the proposed algorithm yields a reduction of 6.1% in Point of Common Coupling(PCC)over the current state-of-the-art algorithm,which means that the proposed algorithm designs a layout that improves the quality of the power supply by 6.1%at the same cost.The ablation experiments show that the proposed algorithm improves the power quality by more than 8.6% and 7.8% compared to both the original VNS algorithm and the multi-objective VNS algorithm.

A Study on Optimizing the Double-Spine Type Flow Path Design for the Overhead Transportation System Using Tabu Search Algorithm

Optimizing Flow Path Design(FPD)is a popular research area in transportation system design,but its application to Overhead Transportation Systems(OTSs)has been limited.This study focuses on optimizing a double-spine flow path design for OTSs with 10 stations by minimizing the total travel distance for both loaded and empty flows.We employ transportation methods,specifically the North-West Corner and Stepping-Stone methods,to determine empty vehicle travel flows.Additionally,the Tabu Search(TS)algorithm is applied to branch the 10 stations into two main layout branches.The results obtained from our proposed method demonstrate a reduction in the objective function value compared to the initial feasible solution.Furthermore,we explore howchanges in the parameters of the TS algorithm affect the optimal result.We validate the feasibility of our approach by comparing it with relevant literature and conducting additional tests on layouts with 20 and 30 stations.

Deep Neural Network Architecture Search via Decomposition-Based Multi-Objective Stochastic Fractal Search

Deep neural networks often outperform classical machine learning algorithms in solving real-world problems.However,designing better networks usually requires domain expertise and consumes significant time and com-puting resources.Moreover,when the task changes,the original network architecture becomes outdated and requires redesigning.Thus,Neural Architecture Search(NAS)has gained attention as an effective approach to automatically generate optimal network architectures.Most NAS methods mainly focus on achieving high performance while ignoring architectural complexity.A myriad of research has revealed that network performance and structural complexity are often positively correlated.Nevertheless,complex network structures will bring enormous computing resources.To cope with this,we formulate the neural architecture search task as a multi-objective optimization problem,where an optimal architecture is learned by minimizing the classification error rate and the number of network parameters simultaneously.And then a decomposition-based multi-objective stochastic fractal search method is proposed to solve it.In view of the discrete property of the NAS problem,we discretize the stochastic fractal search step size so that the network architecture can be optimized more effectively.Additionally,two distinct update methods are employed in step size update stage to enhance the global and local search abilities adaptively.Furthermore,an information exchange mechanism between architectures is raised to accelerate the convergence process and improve the efficiency of the algorithm.Experimental studies show that the proposed algorithm has competitive performance comparable to many existing manual and automatic deep neural network generation approaches,which achieved a parameter-less and high-precision architecture with low-cost on each of the six benchmark datasets.

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.

Tabu Search集中性和多样性自动平衡下的增强搜索策略

在禁忌搜索算法中,集中性搜索与多样性搜索是缺一不可但又相互矛盾的两个方面。本文提出了一种在禁忌搜索集中性和多样性自动平衡下的增强搜索策略算法,这种算法在集中性搜索与多样性搜索之间保持合理平衡的同时,又进一步对结果加强集中性搜索或者多样性搜索,以获全局最优解。以组合优化中的典型难题 TSP为例,通过自动更换邻域、候选集,较好地解决了集中性搜索与多样性搜索的冲突。仿真实验表明,解的质量提高了,验证该算法有效。

一种带时间窗和容量约束的车辆路线问题及其TabuSearch算法

本文提出一种带时间窗和容量约束的车辆路线问题 (CVRPTW ) ,并利用TabuSearch快速启式算法 ,针对Solomon提出的几个标准问题 ,快捷地得到了优良的数值结果。

Tabu Search中集中性和多样性的自适应搜索策略

近年来的研究表明 ,集中性与多样性策略在禁忌搜索中是非常重要的 但集中性与多样性常常又是矛盾的 ,如何解决集中性与多样性之间的矛盾就成为一个值得关注的话题 以组合优化中的著名难题TSP(travelingsalesmanprob lem)为例 ,提出了一种新颖的自适应搜索策略 ,通过邻域和候选集的相互配合 ,动态地调整候选集中分别用于集中性搜索与多样性搜索的元素个数 ,较好地解决了集中性与多样性的冲突问题 仿真实验表明 。

VRPTW的扰动恢复及其TABUSEARCH算法

本文对带时间窗的车辆路线安排扰动恢复问题进行了讨论,分析了各种可能的扰动:增加减少客户,时间窗、客户需求及路线可行性的扰动,构造了扰动模型.利用禁忌搜索算法对问题进行求解,同时通过对模型参数重新设置,得到了多个满足要求的不同的解,这样使解更具有实际可行性和有效性.

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.

交通信号实时配时模型及Tabu Search算法

针对单点信号交叉口 ,提出了一种新的信号实时配时模型 .该模型能更好地反映各种交通状况的实际需要 ,其加权系数 ,能随交通需求的变化而实时变化 .并用禁忌搜索算法 ,求出其近似解 .

用Tabu Search解决基于结群的电路划分问题

电路划分是VLSI物理设计中最重要的步骤之一。本文提出了一种自底向上的结群策略,首先将具有高互连关系的电路模块进行结群,然后再将结群后的宏模块进行划分,用Tabu Search启发式算法进行求解,测试电路选择标准MCNC benchmarks,实验结果表明在解的质量相当情况下,运算时间较少。

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。

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.

Enhancing the synchronizability of networks by rewiring based on tabu search and a local greedy algorithm

By considering the eigenratio of the Laplacian matrix as the synchronizability measure, this paper presents an efficient method to enhance the synchronizability of undirected and unweighted networks via rewiring. The rewiring method combines the use of tabu search and a local greedy algorithm so that an effective search of solutions can be achieved. As demonstrated in the simulation results, the performance of the proposed approach outperforms the existing methods for a large variety of initial networks, both in terms of speed and quality of solutions.

基于Tabu搜索算法真实世界中喜炎平注射液治疗儿童肺炎的联合用药复杂网络研究

目的挖掘喜炎平注射液治疗儿童肺炎核心联用药物方案的临床应用规律,为探索临床不同诊疗思路、用药经验和提高中医药临床证据的循证等级提供参考。方法本研究基于全国29家医院信息管理系统(Hospital Information System,HIS)儿童肺炎的用药数据,运用Tabu禁忌搜索算法,对真实世界喜炎平注射液治疗儿童肺炎人群的联合用药情况进行回顾性数据挖掘分析。结果在核心联用西药方面,抗感染治疗可以联用青霉素/美洛西林/阿莫西林、头孢呋辛/头孢曲松/头孢替安、阿奇霉素等;对症治疗可以联用对乙酰氨基酚/布洛芬、氨溴索+布地奈德+沙丁胺醇等;并发症治疗可以联用水合氯醛+苯巴比妥、磷酸肌酸+抗坏血酸等。在核心联用中药方面,可以联用小柴胡颗粒/小儿柴桂退热颗粒+鼻渊通窍颗粒、热毒宁注射液/蓝芩口服液/连花清瘟胶囊+开喉剑喷雾剂/口腔炎喷雾剂/双料喉风散、小儿肺咳颗粒+醒脾养儿颗粒/四磨汤口服液等。结论本研究的喜炎平注射液核心联用中西药方案,基本符合相关指南及诊疗规范,为优化临床联合用药、合理用药提供了一定的指导和参考。建议临床实际应用过程中,根据患儿的疾病进展情况,合理评估临床联合用药方案的疗效及安全性,注意用药配伍禁忌。

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.

Tabu Search Implementation on Traveling Salesman Problem and Its Variations: A Literature Survey

The Traveling Salesman Problem (TSP) and its allied problems like Vehicle Routing Problem (VRP) are one of the most widely studied problems in combinatorial optimization. It has long been known to be NP-hard and hence research on developing algorithms for the TSP has focused on approximate methods in addition to exact methods. Tabu search is one of the most widely applied metaheuristic for solving the TSP. In this paper, we review the tabu search literature on the TSP and its variations, point out trends in it, and bring out some interesting research gaps in this literature.

Dynamic Tabu Search Algorithm for Solving Departure Scheduling Problem

The aircraft departure scheduling problem is described comprehensively. A mathematical model is built for solving this problem. Then, a local search algorithm is proposed; based on it, the dynamic tabu search technique is applied, and the related implement techniques are presented. A simulation including condition and results is performed to solve a representative problem. It is concluded that （ 1 ） departure aircrafts of each queue keep the same order comparatively all the lime, and the distribution of the departure time is well-proportioned, which accords with the ＂first-come first-serve＂ principle; （2） the total time costs are minimized, which would economize money and reduce danger; （ 3 ） the optimization result is not exclusive, which means that several approaches can be chosen at will; （4） the solution obtained is the global optimal one, which guarantees the validity of the proposed method.