基于杂合机制的免疫遗传算法在动态问题中的应用

针对遗传算法在求解动态问题时存在多样性缺失,无法快速响应环境变化的问题,提出一种基于杂合子机制的免疫遗传算法．该算法借鉴免疫系统中多样性与记忆机理,从保持等位基因多样性出发,在免疫变异中引入杂合映射机制,使种群能够探索更大的解空间．同时,通过引入记忆策略,使算法迅速跟踪最优解变化轨迹．该方法在动态０-１优化问题的求解中取得了较好的效果．     

A genetic algorithm with memory for mixed discrete-continuous design optimization

This paper describes a new approach for reducing the number of the fitness function evaluations required by a genetic algorithm (GA) for optimization problems with mixed continuous and discrete design variables. The proposed additions to the GA make the search more effective and rapidly improve the fitness value from generation to generation. The additions involve memory as a function of both discrete and continuous design variables, multivariate approximation of the fitness function in terms of several continuous design variables, and localized search based on the multivariate approximation. The approximation is demonstrated for the minimum weight design of a composite cylindrical shell with grid stiffeners.     

A double-module immune algorithm for multi-objective optimization problems

Multi-objective optimization problems (MOPs) have become a research hotspot, as they are commonly encountered in scientific and engineering applications. When solving some complex MOPs, it is quite difficult to locate the entire Pareto-optimal front. To better settle this problem, a novel double-module immune algorithm named DMMO is presented, where two evolutionary modules are embedded to simultaneously improve the convergence speed and population diversity. The first module is designed to optimize each objective independently by using a sub-population composed with the competitive individuals in this objective. Differential evolution crossover is performed here to enhance the corresponding objective. The second one follows the traditional procedures of immune algorithm, where proportional cloning, recombination and hyper-mutation operators are operated to concurrently strengthen the multiple objectives. The performance of DMMO is validated by 16 benchmark problems, and further compared with several multi-objective algorithms, such as NSGA-II, SPEA2, SMSEMOA, MOEA/D, SMPSO, NNIA and MIMO. Experimental studies indicate that DMMO performs better than the compared targets on most of test problems and the advantages of double modules in DMMO are also analyzed.     

Hybrid genetic algorithm for optimization problems with permutation property

Permutation property has been recognized as a common but challenging feature in combinatorial problems. Because of their complexity, recent research has turned to genetic algorithms to address such problems. Although genetic algorithms have been proven to facilitate the entire space search, they lack in fine-tuning capability for obtaining the global optimum. Therefore, in this study a hybrid genetic algorithm was developed by integrating both the evolutional and the neighborhood search for permutation optimization.Experimental results of a production scheduling problem indicate that the hybrid genetic algorithm outperforms the other methods, in particular for larger problems. Numerical evidence also shows that different input data from the initial, transient and steady states influence computation efficiency in different ways. Therefore, their properties have been investigated to facilitate the measure of the performance and the estimation of the accuracy.     

求解串并联系统配置问题的免疫遗传算法

通过对串并联系统配置可靠性问题的分析,提出了基于免疫遗传算法（IGA）求解该问题的方法。在保留基本遗传算法随机全局搜索能力的基础上,借鉴生物免疫机制中抗体的多样性保持策略,大大提高了算法的群体多样性。实验结果表明,免疫遗传算法可有效改善基本遗传算法的未成熟收敛和局部搜索能力差的缺点,具有很好的全局收敛能力,其全局收敛性及收敛速度均得到了提高。     

Interweaving genetic programming and genetic algorithm for structural and parametric optimization in adaptive platform product customization

An adaptive product platform offers high customizability for generating feasible product variants for customer requirements. Customization takes place not only to product platform structure but also to its relevant parameters. Structural and parametric optimization processes are interwoven with each other to achieve the total optimality. This paper presents an evolutionary method dealing with interwoven structural and parametric optimization of adaptive platform product customization. The method combines genetic programming and genetic algorithm for handling structural and parametric optimization, respectively. Efficient genetic representation and operation schemes are carefully adapted. While designing these schemes, features specific to structural and parameter customization are considered for the simplification of platform product management. The experimental results show that the performance of the proposed algorithm outperforms that of the tandem evolutionary algorithm in which a genetic algorithm for parametric optimization is totally nested in a genetic programming for structural optimization.     

A permutation-based dual genetic algorithm for dynamic optimization problems

Adaptation to dynamic optimization problems is currently receiving growing interest as one of the most important applications of genetic algorithms. Inspired by dualism and dominance in nature, genetic algorithms with the dualism mechanism have been applied for several dynamic problems with binary encoding. This paper investigates the idea of dualism for combinatorial optimization problems in dynamic environments, which are also extensively implemented in the real-world. A new variation of the GA, called the permutation-based dual genetic algorithm (PBDGA), is presented. Within this GA, two schemes based on the characters of the permutation in group theory are introduced: a partial-dualism scheme motivated by a new multi-attribute dualism mechanism and a learning scheme. Based on the dynamic test environments constructed by stationary benchmark problems, experiments are carried out to validate the proposed PBDGA. The experimental results show the efficiency of PBDGA in dynamic environments.     

免疫遗传蚁群融合算法

提出了一种融合蚁群系统、免疫算法和遗传算法的混合算法。将免疫算法和遗传算法引入到每次蚁群迭代的过程中,利用免疫算法的局部优化能力和遗传算法的全局搜索能力,来提高蚁群系统的收敛速度。该算法通过遗传算法的选择、交叉、变异操作和免疫算法的自适应疫苗接种操作,有效地解决了蚁群系统的易陷入局部最优和易退化的缺点。通过对旅行商问题的仿真实验表明该算法具有非常好的收敛速度和全局最优解的搜索能力。     

基于改进遗传算法的电力系统无功优化的研究

电力系统无功优化问题是一个多变量、多约束的混合非线性规划问题,其操作变量既有连续变量又有离散变量,其优化过程比较复杂。遗传算法是模拟生物在自然环境中的遗传和进化过程而形成的一种自适应的全局优化搜索算法,可用于解决含有离散变量的复杂优化问题。本文选用遗传算法求解电力系统无功优化问题,并对基本遗传算法的编码、初始种群、适应度函数和交叉、变异策略等进行改进,使用本文提出的改进算法对IEEE1 4节点进行无功优化计算,结果证明本文模型和算法的实用性、可靠性和优越性。     

Neuro-fuzzy and genetic algorithm in multiple response optimization

Optimization of a multiple output system, whose function is only approximately known and is represented in tabular form, is modeled and optimized by the combined use of a neuro-fuzzy network and optimization techniques which do not require the explicit representation of the function. Neuro-fuzzy network is useful for learning the approximate original tabular system. However, the results obtained by the neuro-fuzzy network are represented implicitly in the network. The MANFIS neuro-fuzzy network, which is an extension of the ANFIS network, is used to model the multiple output system and a genetic algorithm is used to optimize the resulting multiple objective decision making problem. A chemical process whose function is represented approximately in tabular form is solved to illustrate the approach.     

Applying genetic algorithm for the development of the components-based embedded system

The embedded system is primarily designed for a particular piece of equipment and it varies on a case-by-case basis. The functionality is required to be specific to the equipment and consequently the application domain is limited. The software embedded in the system also faces problem due to the limitation of the hardware capacity. It is necessary for the designers to consider the hardware capacity and software specification simultaneously while an embedded system is developed. If hardware and software are taken into account concurrently, the design applicability and efficiency are decreased. The evolutionary computing (EC), which comprises techniques of evolutionary programming, evolution strategies, genetic algorithms, and genetic programming has been widely used to solve optimization problems for large scale and complex systems. It is capable to escape not only from local optima due to population based approach, but also from unbiased nature, which enables it to perform well in a situation with little domain knowledge. Therefore, this study proposes an evolutionary approach that applies the characteristics of software reuse, the metrics for the object-oriented concept, and the genetic algorithm to effectively manage and optimize the embedded system. This approach is implemented in the World Wide Web environment. Numerous results associated with performance enhancements of the algorithm are presented in this paper.     

A hybrid genetic algorithm for constrained multi-objective optimization under uncertainty and target matching problems

This work presents a new approach for interval-based uncertainty analysis. The proposed approach integrates a local search strategy as the worst-case-scenario technique of anti-optimization with a constrained multi-objective genetic algorithm. Anti-optimization is a term for an approach to safety factors in engineering structures which is described as pessimistic and searching for least favorable responses, in combination with optimization techniques but in contrast to probabilistic approaches. The algorithm is applied and evaluated to be efficient and effective in producing good results via target matching problems: a simulated topology and shape optimization problem where a ‘target’ geometry set is predefined as the Pareto optimal solution and a constrained multiobjective optimization problem formulated such that the design solutions will evolve and converge towards the target geometry set.     

Performance evaluation of a two stage adaptive genetic algorithm (TSAGA) in structural topology optimization

Genetic algorithm with island and adaptive features has been used for reaching the global optimal solution in the context of structural topology optimization. A two stage adaptive genetic algorithm (TSAGA) involving a self-adaptive island genetic algorithm (SAIGA) for the first stage and adaptive techniques in the second stage is proposed for the use in bit-array represented topology optimization. The first stage, consisting a number of island runs each starting with a different set of random population and searching for better designs in different peaks, helps the algorithm in performing an extensive global search. After the completion of island runs the initial population for the second stage is formed from the best members of each island that provides greater variety and potential for faster improvement and is run for a predefined number of generations. In this second stage the genetic parameters and operators are dynamically adapted with the progress of optimization process in such a way as to increase the convergence rate while maintaining the diversity in population. The results obtained on several single and multiple loading case problems have been compared with other GA and non-GA-based approaches, and the efficiency and effectiveness of the proposed methodology in reaching the global optimal solution is demonstrated.     

A study of a parallelized immune coevolutionary algorithm for division-of-labor problems

Recently, a number of parallelized optimization algorithms have been proposed. We have proposed a co-evolutionary immune algorithm (IA) to solve the division-of-labor problems, in particular the n-th agents travelling salesman problem (n-TSP). In this article, we extend the co-evolutionary IA for a large-scale n-TSP with (1) an improvement for the search speed through parallelized search on the PC-cluster, and (2) the introduction of a new division-processing pre-estimated division processing to improve the search ability. Some computational experiments show the proposed method can obtain better quality solutions for division-of-labor problems, and present an applicable parameter cofinguration.This work was presented, in part, at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28–30, 2004     

A genetic algorithm for optimization of laminated dies manufacturing

Laminated tooling is based on taking sheets of metal and stacking them to produce the final product, after cutting each layer profile using laser or other techniques. CNC machining removes the extra material and brings the final product to specific tolerances. To reduce the cost of laminated dies manufacturing, the amount of the extra material and the number of slices must likewise be reduced. This is considered an optimization problem, which can be solved by genetic algorithms (G.A.). However, in most instances, premature convergence prevents the system from searching for a more optimal solution, a common problem in many G.A. applications. To address this problem, a new niching method is presented in this paper. Using the proposed method, results show not only a significant improvement in the quality of the optimum solution but also a substantial reduction in the processing time.     

A three-fold approach for job shop problems: A divide-and-integrate strategy with immune algorithm

This paper presents a novel divide-and-integrate strategy based approach for solving large scale job-shop scheduling problems. The proposed approach works in three phases. First, in contrast to traditional job-shop scheduling approaches where optimization algorithms are used directly regardless of problem size, priority rules are deployed to decrease problem scale. These priority rules are developed with slack due dates and mean processing time of jobs. Thereafter, immune algorithm is applied to solve each small individual scheduling module. In last phase, integration scheme is employed to amalgamate the small modules to get gross schedule with minimum makespan. This integration is carried out in dynamic fashion by continuously checking the preceding module's machine ideal time and feasible slots (satisfying all the constraint). In this way, the proposed approach will increase the machine utilization and decrease the makespan of gross schedule. Efficacy of the proposed approach has been tested with extremely hard standard test instances of job-shop scheduling problems. Implementation results clearly show effectiveness of the proposed approach.     

The hybrid genetic algorithm with two local optimization strategies for traveling salesman problem

Traveling salesman problem (TSP) is proven to be NP-complete in most cases. The genetic algorithm (GA) is improved with two local optimization strategies for it. The first local optimization strategy is the four vertices and three lines inequality, which is applied to the local Hamiltonian paths to generate the shorter Hamiltonian circuits (HC). After the HCs are adjusted with the inequality, the second local optimization strategy is executed to reverse the local Hamiltonian paths with more than 2 vertices, which also generates the shorter HCs. It is necessary that the two optimization strategies coordinate with each other in the optimization process. The two optimization strategies are operated in two structural programs. The time complexity of the first and second local optimization strategies are O(n) and O(n³), respectively. The two optimization strategies are merged into the traditional GA. The computation results show that the hybrid genetic algorithm (HGA) can find the better approximate solutions than the GA does within an acceptable computation time.     

物流配送中烟花算法结合遗传算法的异质车队路径优化方法

针对物流配送中车辆路径的问题,提出一种烟花算法结合遗传算法的物流配送异质车队路径优化方法。根据优先聚类其次路径的两阶段构造理论将新型群体智能算法烟花算法与遗传算法进行有效结合,首先按运力空间划分聚类区域,并采用改进的遗传算法解决为客户分配车辆的问题,然后通过采用烟花算法对路径排序实现本地路径优化。将该方法的实验结果与经验结果进行了比较,结果表明,所提出的混合算法模型得到的实验结果优于经验结果。     

Interval optimization of rotor-bearing systems with dynamic behavior constraints using an interval genetic algorithm

A new interval optimization algorithm is presented in this paper. In engineering, most optimization algorithms focus on exact parameters and optimum objectives. However, exact parameters are not easy to be manufactured to because of manufacturing errors and expensive manufacturing cost. To account for these problems, it is necessary to estimate interval design parameters and allowable objective error. This is the first paper to propose a new interval optimization algorithm within the context of Genetic Algorithms. This new algorithm, the Interval Genetic Algorithm (IGA), can neglect interval analysis and determines the optimum interval parameters. Furthermore, it can also effectively maximize the design scope. The optimizing ability of the IGA is tested through the interval optimization of a two-dimensional function. Then the IGA is applied to rotor-bearing systems. The results show that the IGA is effective in deriving optimal interval design parameters within the allowable error when minimizing shaft weight and/or transmitted force of rotor-bearing systems.     

A genetic algorithm approach for multi-objective optimization of supply chain networks

Supply chain network (SCN) design is to provide an optimal platform for efficient and effective supply chain management. It is an important and strategic operations management problem in supply chain management, and usually involves multiple and conflicting objectives such as cost, service level, resource utilization, etc. This paper proposes a new solution procedure based on genetic algorithms to find the set of Pareto-optimal solutions for multi-objective SCN design problem. To deal with multi-objective and enable the decision maker for evaluating a greater number of alternative solutions, two different weight approaches are implemented in the proposed solution procedure. An experimental study using actual data from a company, which is a producer of plastic products in Turkey, is carried out into two stages. While the effects of weight approaches on the performance of proposed solution procedure are investigated in the first stage, the proposed solution procedure and simulated annealing are compared according to quality of Pareto-optimal solutions in the second stage.