Hybrid Genetic Programming with Local Search Operators for Dynamic Force Identification

In this paper, based on the Darwinian and Lamarckian evolution theories, three hybrid genetic programming (GP) algorithms integrated with different local search operators (LSOs) are implemented to improve the search efficiency of the standard GP. These three LSOs are the genetic algorithm, the linear bisection search, and the Hooke and Jeeves method. A simple encoding method is presented to encode the GP individuals into the expressions that can be recognized by the different LSOs. The implemented hybrid GP algorithms are applied to identify the excitation force acting on the structures from the measured structural response, which is an important type of inverse problem in structural dynamics. Illustrative examples of a frame structure and a multistory building structure demonstrate that, compared with the standard GP, the hybrid GP algorithms have higher search efficiency which can be used as alternate global search and optimization tools for other engineering problem solving.     

Efficient Parallel Implementation of Hybrid Optimization Approaches for Solving Groundwater Inverse Problems

Inverse problems that are constrained by large-scale partial differential equation (PDE) systems demand very large computational resources. Solutions to these problems generally require the solution of a large number of complex PDE systems. Three-dimensional groundwater inverse problems fall under this category. In this paper, we describe the implementation of a parallel simulation-optimization framework for solving PDE-based inverse problems and demonstrate it for the solution of groundwater contaminant source release history reconstruction problems that are of practical importance. The optimization component employs several optimization algorithms, including genetic algorithms (GAs) and several local search (LS) approaches that can be used in a hybrid mode. This hybrid GA-LS optimizer is used to drive a parallel finite-element (FEM) groundwater forward transport simulator. Parallelism is exploited within the transport simulator (fine grained parallelism) as well as the optimizer (coarse grained parallelism) through the exclusive use of the Message Passing Interface (MPI) communication library. Algorithmic and parallel performance results are presented for an IBM SP3 supercomputer. Simulation and performance results presented in this paper illustrate that an effective combination of efficient optimization algorithms and parallel computing can enable solution to three-dimensional groundwater inverse problems of a size and complexity not attempted before.     

面向全局和工程优化问题的混合进化JAYA算法

为了更好求解复杂函数优化和工程约束优化问题,进一步增强JAYA算法的寻优能力,提出一种面向全局优化的混合进化JAYA算法.首先在计算当前最优和最差个体时引入反向学习机制,提高最优和最差个体跳离局部极值区域的可能性;然后在个体位置更新中引入并融合正弦余弦算子和差分扰动机制,不仅增加了种群的多样性,而且较好平衡与满足了算法在不同迭代时期对探索和挖掘能力的不同需求;最后在算法结构上采用奇偶不同的混合进化策略,有效利用不同演化机制的优势结果,进一步提升了算法的收敛性和精度.之后给出了算法流程伪代码,理论分析证明了改进算法的时间复杂度与基本JAYA相同,而通过6种代表性算法在包含和组合了30个基准函数的CEC2017测试套件上进行的多维度函数极值优化测试,以及对拉伸弹簧、波纹舱壁、管柱设计、钢筋混凝土梁、焊接梁和汽车侧面碰撞6个具有挑战性的工程设计问题的优化求解,都清楚地表明改进后算法的寻优精度、收敛性能和求解稳定性均有显著提升,在求解CEC复杂函数和工程约束优化问题上有着明显优势.     

Coevolutionary computation

This article proposes a general framework for the use of coevolution to boost the performance of genetic search. It combines coevolution with yet another biologically inspired technique, called lifetime fitness evaluation (LTFE). Two unrelated problems--neural net learning and constraint satisfaction--are used to illustrate the approach. Both problems use predator-prey interactions to boost the search. In contrast with traditional "single population" genetic algorithms (GAs), two populations constantly interact and co-evolve. However, the same algorithm can also be used with different types of co-evolutionary interactions. As an example, the symbiotic coevolution of solutions and genetic representations is shown to provide an elegant solution to the problem of finding a suitable genetic representation. The approach presented here greatly profits from the partial and continuous nature of LTFE. Noise tolerance is one advantage. Even more important, LTFE is ideally suited to deal with coupled fitness landscapes typical for coevolution.     

Efficient Hybrid Genetic Algorithm for Resource Leveling via Activity Splitting

Resource leveling problem is an attractive field of research in project management. Traditionally, a basic assumption of this problem is that network activities could not be split. However, in real-world projects, some activities can be interrupted and resumed in different time intervals but activity splitting involves some cost. The main contribution of this paper lies in developing a practical algorithm for resource leveling in large-scale projects. A novel hybrid genetic algorithm is proposed to tackle multiple resource-leveling problems allowing activity splitting. The proposed genetic algorithm is equipped with a novel local search heuristic and a repair mechanism. To evaluate the performance of the algorithm, we have generated and solved a new set of network instances containing up to 5,000 activities with multiple resources. For small instances, we have extended and solved an existing mixed integer programming model to provide a basis for comparison. Computational results demonstrate that, for large networks, the proposed algorithm improves the leveling criterion at least by 76% over the early schedule solutions. A case study on a tunnel construction project has also been examined.     

Augmented Lagrangian Genetic Algorithm for Structural Optimization

This paper presents a robust hybrid genetic algorithm for optimization of space structures using the augmented Lagrangian method. An attractive characteristic of genetic algorithm is that there is no line search and the problem of computation of derivatives of the objective function and constraints is avoided. This feature of genetic algorithms is maintained in the hybrid genetic algorithm presented in this paper. Compared with the penalty function‐based genetic algorithm, only a few additional simple function evaluations are needed in the new algorithm. Furthermore, the trial and error approach for the starting penalty function coefficient and the process of arbitrary adjustments are avoided. There is no need to perform extensive numerical experiments to find a suitable value for the penalty function coefficient for each type or class of optimization problem. The algorithm is general and can be applied to a broad class of optimization problems.     

作业车间调度问题解的不可行性检测算法和快速修复算法

为了判别作业车间调度问题的解的可行性,提出了一种基于图论的启发式判别算法,并通过实例验证了方法的正确性.提出了普适于作业车间调度问题的快速修补新算法,可以对于作业车间调度问题的不可行解进行修正使之变成可行解.判别算法和修补算法在最不利情形下的计算复杂性均为O(n),判别算法在最有利情形下的计算复杂性为O(2 |J|+|M|).所提出的算法具有很大的灵活性,对于局部蚂蚁算法、遗传算法以及一般的作业车间调度问题均适用.     

基于精英重组的混合多目标进化算法

针对多目标进化算法搜索效率低和收敛性差的问题,提出了基于精英重组的混合多目标进化算法,将多目标优化问题分解为多个单目标优化问题单独求解,并采用基于遗传算法的精英重组策略将多个相异解重组生成唯一的精英解.提出区域化的种群初始化方法,改进局部搜索及群体选择机制,采用以优化子群为核心的分组交叉策略及自适应多位变异算子,并引入基于混沌优化的重启机制,有效克服了精英保存的固有缺陷,以及现有多目标进化算法存在的目标空间解拥挤、收敛慢、易早熟等问题.多目标测试函数的数值仿真和关键步骤的性能分析证明了本文算法的有效性和优越性.     

Optimization of Pile Groups Using Hybrid Genetic Algorithms

This paper presents an automated optimal design method using a hybrid genetic algorithm for pile group foundation design. The design process is a sizing and topology optimization for pile foundations. The objective is to minimize the material volume of the foundation taking the configuration, number, and cross-sectional dimensions of the piles as well as the thickness of the pile cap as design variables. A local search operator by the fully stressed design (FSD) approach is incorporated into a genetic algorithm (GA) to tackle two major shortcomings of a GA, namely, large computation effort in searching the optimum design and poor local search capability. The effectiveness and capability of the proposed algorithm are first illustrated by a five by five pile group subjected to different loading conditions. The proposed optimization algorithm is then applied to a large-scale foundation project to demonstrate the practicality of the algorithm. The proposed hybrid genetic algorithm successfully minimizes the volume of material consumption and the result matches the engineering expectation. The FSD operator has great improvement on both design quality and convergence rate. Challenges encountered in the application of optimization techniques to design of pile groups consisting of hundreds of piles are discussed.     

免疫遗传算法在BP神经网络中的应用

提出了一种基于免疫遗传算法(IGA)的BP神经网络设计方法.该算法在遗传算法(GA)的基础上引入生物免疫系统中的多样性保持机制和抗体浓度调节机制,有效地克服了GA算法的搜索效率低、个体多样性差及早熟现象,提高了算法的收敛性能.为了解决BP神经网络权值随机初始化带来的问题,用多样性模拟退火算法(SAND)进行神经网络权值初始化,并给出了算法详细的设计步骤.仿真结果表明,同混合遗传算法相比,该算法设计的BP神经网络具有较快的收敛速度和较强的全局收敛性能.     

Permutation-Based Particle Swarm Optimization for Resource-Constrained Project Scheduling

In the light of particle swarm optimization (PSO) which utilizes both local and global experiences during search process, a permutation-based scheme for the resource-constrained project scheduling problem (RCPSP) is presented. In order to handle the permutation-feasibility and precedence-constraint problems when updating the particle-represented sequence or solution for the RCPSP, a hybrid particle-updating mechanism incorporated with a partially mapped crossover of a genetic algorithm and a definition of an activity-move-range is developed. The particle-represented sequence should be transformed to a schedule (including start times and resource assignments for all activities) through a serial method and accordingly evaluated against the objective of minimizing project duration. Experimental analyses are presented to investigate the performances of the permutation-based PSO. The study aims at providing an alternative for solving the RCPSP in the construction field by utilizing the advantages of PSO.     

Hybrid Time-Cost Optimization of Nonserial Repetitive Construction Projects

Time-cost trade-off analysis represents a challenging task because the activity duration and cost have uncertainty associated with them, which should be considered when performing schedule optimization. This study proposes a hybrid technique that combines genetic algorithms (GAs) with dynamic programming to solve construction projects time-cost trade-off problems under uncertainty. The technique is formulated to apply to project schedules with repetitive nonserial subprojects that are common in the construction industry such as multiunit housing projects and retail network development projects. A generalized mathematical model is derived to account for factors affecting cost and duration relationships at both the activity and project levels. First, a genetic algorithm is utilized to find optimum and near optimum solutions from the complicated hyperplane formed by the coding system. Then, a dynamic programming procedure is utilized to search the vicinity of each of the near optima found by the GA, and converges on the global optima. The entire optimization process is conducted using a custom developed computer code. The validation and implementation of the proposed techniques is done over three axes. Mathematical correctness is validated through function optimization of test functions with known optima. Applicability to scheduling problems is validated through optimization of a 14 activity miniproject found in the literature for results comparison. Finally implementation to a case study is done over a gas station development program to produce optimum schedules and corresponding trade-off curves. Results show that genetic algorithms can be integrated with dynamic programming techniques to provide an effective means of solving for optimal project schedules in an enhanced realistic approach.     

Genetic-Algorithm-Based Strategies for Dynamic Identification of Nonlinear Systems with Noise-Corrupted Response

The main objective of this paper is to investigate efficiency and correctness of different real-coded genetic algorithms and identification criteria in nonlinear system identification within the framework of non-classical identification techniques. Two conventional genetic algorithms have been used, standard genetic algorithm and microgenetic algorithm. Moreover, an advanced multispecies genetic algorithm has been proposed: it combines an adaptive rebirth operator, a migration strategy, and a search space reduction technique. Initially, a critical analysis has been conducted on these soft computing strategies to provide some guidelines for similar engineering and physical applications. Therefore, the hysteretic Bouc-Wen model has been numerically investigated to achieve three main results. First, the computational effectiveness and accuracy of the proposed strategy are checked to show that the proposed optimizer outperforms the aforementioned conventional genetic algorithms. Secondarily, a comparative study is performed to show that an improved performance can be obtained by using the Hilbert transform-based acceleration envelope as objective function in the optimization problem (instead of the pure acceleration response). Finally, system identification is conducted by making use of the proposed optimizer to verify its substantial noise-insensitive property also in the presence of high noise-to-signal ratio.     

智能优化算法在机械故障诊断领域的应用综述

群体智能优化算法是根据生物集群运动、交互、进化等行为机制而开发的自然启发算法,凭借其显著的灵活性、适应性、鲁棒性以及全局寻优能力,被广泛应用于现实世界中各类优化问题的求解. 受羊群间歇性集体运动现象启发,本文提出了一种新的仿生群体智能优化方法—羊群迁徙优化（Sheep flock migrate optimization, SFMO）算法,创新性地建立了3个核心运算模块,即放牧算子、集体运动算子和补偿策略. 与现有的群体智能优化算法相比,SFMO可以通过广泛随机搜索指导下的种群迁徙,降低算法陷于局部最优的概率,为群体智能优化领域提供了一种新的解决方案. 收敛性证明和复杂度分析进一步为SFMO提供了理论支撑. 以CEC-2017基准函数为基础的数值仿真验证表明：SFMO能够有效解决函数优化问题,并在多模态函数优化问题中具有显著优势.     

Integrated Genetic Algorithm for Optimization of Space Structures

Gradient‐based mathematical‐optimization algorithms usually seek a solution in the neighborhood of the starting point. If more than one local optimum exists, the solution will depend on the choice of the starting point, and the global optimum cannot be found. This paper presents the optimization of space structures by integrating a genetic algorithm with the penalty‐function method. Genetic algorithms are inspired by the basic mechanism of natural evolution, and are efficient for global‐searches. The technique employs the Darwinian survival‐of‐the‐fittest theory to yield the best or better characters among the old population, and performs a random information exchange to create superior offspring. Different types of crossover operations are used in this paper, and their relative merit is investigated. The integrated genetic algorithm has been implemented in C language and is applied to optimization of three space truss structures. In each case, an optimum solution was obtained after a limited number of iterations.     

基于混合编码策略的电弧炉冶炼钢种的碳含量预报模型

提出一种混合编码策略的遗传算法(GA)训练电炉钢碳含量神经网络预报模型。先采用二进制编码策略,再采用十进制编码策略继续优化预报模型的权阈值,这种混合编码策略综合了二进制编码搜索能力强和十进制编码变异量可任意小的优点。仿真结果表明,混合编码策略的遗传算法(GA)具有更快的收敛速度和更好的寻优性能。对100 t电弧炉冶炼0.85%～1.00%C的钢种,预报碳含量的精度为±0.04%时混合编码GA的命中率为96%,二进制编码GA的命中率为90%。     

Permutation-Based Elitist Genetic Algorithm for Optimization of Large-Sized Resource-Constrained Project Scheduling

The resource-constrained project scheduling problem (RCPSP) has received the attention of many researchers because its general model can be used in a wide variety of construction planning and scheduling applications. The exact procedures and priority-rule-based heuristics fail to search for the optimum solution to the RCPSP of large-sized project networks in a reasonable amount of time for successful application in practice. This paper presents a permutation-based elitist genetic algorithm for solving the problem in order to fulfill the lack of an efficient optimal solution algorithm for project networks with 60 activities or more as well as to overcome the drawback of the exact solution approaches for large-sized project networks. The proposed algorithm employs the elitist strategy to preserve the best individual solution for the next generation so the improved solution can be obtained. A random number generator that provides and examines precedence feasible individuals is developed. A serial schedule generation scheme for the permutation-based decoding is applied to generate a feasible solution to the problem. Computational experiments using a set of standard test problems are presented to demonstrate the performance and accuracy of the proposed algorithm.     

多感官群集智能算法及其在前向神经网络训练方面的应用

针对连续域函数优化问题,提出了一种新的全局极大值搜索方法——多感官群集智能算法(multi-sense swarmintelli-gence algorithm,MSA).受鱼群算法(artificial fish-swarmalgorithm,AFA)和FS算法(free search algorithm,FSA)的启发,MSA的搜索机制将大范围勘察和小范围精确搜索相结合,个体在使用视觉信息快速逼近局部较优解的同时,利用嗅觉信息避免群体过于集中并引导个体向全局较优解方向移动.仿真结果证明:MSA鲁棒性较强,全局收敛性好,收敛速度较快,收敛精度较高.最后,将该方法应用于前向神经网络训练,结果表明满足应用要求.     

两级选址-路径问题的大规模邻域搜索模拟退火算法

针对目前越来越普遍的多级配送模式,建立以总成本最小为目标函数的两级选址-路径问题模型,并提出了大规模邻域搜索模拟退火算法进行求解.在模拟退火算法框架中,嵌入大规模邻域搜索过程,包含破坏、重组和局部搜索方法,从而进一步提高算法在解空间中构建邻域的范围.采用两级选址-路径问题标准算例对算法求解效果进行验证,并与标准模拟退火算法和国际已知最优解进行对比.结果显示,所建模型和算法正确有效,并且在求解大规模问题时算法能够取得相对更好的优化结果.     

A Multi-Agent Immune Network Algorithm and Its Application to Murphree Efficiency Determination for the Distillation Column

Artificial Immune Network (aiNet) algorithms have become popular for global optimization in many modern industrial applications. However, high-dimensional systems using such models suffer from a potential premature convergence problem. In the existing aiNet algorithms, the premature convergence problem can be avoided by implementing various clonal selection methods, such as immune suppression and mutation approaches, both for single population and multi-population cases. This paper presents a new Multi-Agent Artificial Immune Network (Ma-aiNet) algorithm, which combines immune mechanics and multiagent technology, to overcome the premature convergence problem in high-dimensional systems and to efficiently use the agent ability of sensing and acting on the environment. Ma-aiNet integrates global and local search algorithms. The performance of the proposed method is evaluated using 10 benchmark problems, and the results are compared with other well-known intelligent algorithms. The study demonstrates that Ma-aiNet outperforms other algorithms tested. Ma-aiNet is also used to determine the Murphree efficiency of a distillation column with satisfactory results.