An efficient dynamical evolutionary algorithm for global optimization

In this paper, we introduce a new dynamical evolutionary algorithm (DEA) that aims to find the global optimum and give the theoretical explanation from statistical mechanics. The algorithm has been evaluated numerically using a wide set of test functions which are nonlinear, multimodal and multidimensional. The numerical results show that it is possible to obtain global optimum or more accurate solutions than other methods for the investigated hard problems.     

A new evolutionary search strategy for global optimization of high-dimensional problems

Global optimization of high-dimensional problems in practical applications remains a major challenge to the research community of evolutionary computation. The weakness of randomization-based evolutionary algorithms in searching high-dimensional spaces is demonstrated in this paper. A new strategy, SP-UCI is developed to treat complexity caused by high dimensionalities. This strategy features a slope-based searching kernel and a scheme of maintaining the particle population’s capability of searching over the full search space. Examinations of this strategy on a suite of sophisticated composition benchmark functions demonstrate that SP-UCI surpasses two popular algorithms, particle swarm optimizer (PSO) and differential evolution (DE), on high-dimensional problems. Experimental results also corroborate the argument that, in high-dimensional optimization, only problems with well-formative fitness landscapes are solvable, and slope-based schemes are preferable to randomization-based ones.     

An evolutionary strategy for decremental multiobjective optimization problems

In this article, an evolutionary algorithm for multiobjective optimization problems in a dynamic environment is studied. In particular, we focus on decremental multiobjective optimization problems, where some objectives may be deleted during evolution—for such a process we call it objective decrement. It is shown that the Pareto‐optimal set after objective decrement is actually a subset of the Pareto‐optimal set before objective decrement. Based on this observation, the inheritance strategy is suggested. When objective decrement takes place, this strategy selects good chromosomes according to the decremented objective set from the solutions found before objective decrement, and then continues to optimize them via evolution for the decremented objective set. The experimental results showed that this strategy can help MOGAs achieve better performance than MOGAs without using the strategy, where the evolution is restarted when objective decrement occurs. More solutions with better quality are found during the same time span. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 847–866, 2007.     

An efficient hybrid evolutionary algorithm for scheduling with setup times and weighted tardiness minimization

We confront the job shop scheduling problem with sequence-dependent setup times and weighted tardiness minimization. To solve this problem, we propose a hybrid metaheuristic that combines the intensification capability of tabu search with the diversification capability of a genetic algorithm which plays the role of long term memory for tabu search in the combined approach. We define and analyze a new neighborhood structure for this problem which is embedded in the tabu search algorithm. The efficiency of the proposed algorithm relies on some elements such as neighbors filtering and a proper balance between intensification and diversification of the search. We report results from an experimental study across conventional benchmarks, where we analyze our approach and demonstrate that it compares favorably to the state-of-the-art methods.     

一种交互演化改进鲸鱼算法及其收敛性分析

针对鲸鱼算法求解稳定性不强、收敛速度有时较慢、易陷入局部极值等缺点,提出一种具有轮盘赌选择和二次插值择优机制的双种群交互演化鲸鱼算法.首先,在搜索觅食阶段引入轮盘赌选择机制,可有效避免劣质解被多次选取的问题,保证算法的收敛性能;在算法进化结构和求解过程中用两种不同演化机制的种群以及它们之间不断的信息交互,可有效地平衡和调节算法的全局搜索与局部搜索能力;在双种群个体演化更新后、信息交互前,利用二次插值策略更新鲸鱼个体的位置,增加种群的多样性,而之后的择优选取新位置可提高算法的收敛速度.然后,给出算法流程并用概率测度法对算法的收敛性进行证明.最后,通过6种代表性算法对CEC2017测试函数集套件中不同特征函数在多个维度上进行仿真实验,结果表明改进算法的收敛速度、寻优精度和求解稳定性均有明显提高,具有很好的收敛性能.     

An on-line batch span minimization and quality control strategy for batch and semi-batch processes

A novel strategy is proposed to minimize the operation time of batch and semi-batch processes. The proposed on-line strategy is based on linear regression models and employs a cascade control structure in which the primary controller calculates an optimal operation profile for the secondary controller to follow. A special feature of the proposed on-line strategy is that it conducts run-wise information feedback and achieves the attainable minimum operation time as the batch run is repeated despite model uncertainty. The performance of the proposed strategy is illustrated through simulation studies involving an exothermic batch reactor and a semi-batch reactor producing 2-acetoacetyl pyrrole.     

An analysis strategy for swept solids

Numerous engineering components such as gears, turbine-blades, splined-shafts, and MEMS devices have a common underlying geometric structure in that they may be represented as a sweep of a 2D cross-section along a 3D trajectory. We consider here boundary value problems over such solids and propose an efficient strategy for solving such problems. The proposed strategy avoids the construction of a 3D finite element mesh, and therefore does not require a 3D mesh generator/solver. Instead a 2D mesh of the cross-section is coupled with an implicit 1D ‘hierarchical mesh’ over the trajectory. The latter is symbolically eliminated prior to computational analysis. This, we show, leads to the reduction of 3D scalar boundary value problems to simpler 2D vector problems over the cross-section. The proposed method is particularly well suited for the design exploration of swept solids, where numerous combinations of trajectories and/or cross-sections must be examined. Its computational superiority over extant 3D strategies is established through theory and numerical experiments.

A general optimization strategy for sound power minimization

A general approach for minimizing radiated acoustic power of a baffled plate excited by broad band harmonic excitation is given. The steps involve a finite element discretization for expressing acoustic power and vibration analysis, analytical design sensitivity analysis, and the use of gradient-based optimization algorithms. Acoustic power expressions are derived from the Rayleigh integral for plates. A general methodology is developed for computing design sensitivities using analytical expressions. Results show that analytical sensitivity analysis is important from both computational time and accuracy considerations. Applications of the optimization strategy to rectangular plates and an engine cover plate are presented. Thicknesses are chosen as design variables.     

A technique for the global minimization of a polynomial

In this paper a finite search method is proposed to identify the global minimum of a polynomial. Lavoro eseguito nell'ambito del gruppo G.N.I.M. del C.N.R.     

一种有效的解无约束全局优化的进化算法

为了解决进化算法在求解全局优化时易陷入局部最优和收敛速度慢的问题,设计了一个杂交算子,利用种群中最好点与其他点间的关系确定搜索方向,从而快速地找到实值函数的下降方向,一旦算法找到优于种群中最好点的点,利用所构造的两条直线交点的投影对其进行进一步优化,使函数值更迅速地下降.提出了适合杂交算子的初始种群生成方法.设计了一个既能提高收敛速度又能摆脱局部最优的变异算子以增强算法的效果.在此基础上,提出了一个求解全局优化问题的高效进化算法,并从理论上证明了全局收敛性,从数值上验证了有效性.     

应用于高维优化问题的免疫进化算法

针对免疫算法在伞局优化过程中多样性不足的问题,提出一种新型的免疫进化算法.随机克隆扩张和多受体随机编辑算了足该算法的主要特色,同时引入改进的超变异算了加强个体的学习能力;提出一种新的算法件能评价准则,以比较不同算法在全局优化中的表现.实验环节中,首先确定了克降扩张比:然后将免疫进化算法与快速克降算法和Opt-IMMAL...     

非凸二次规划全局极小问题的新型分枝定界算法

针对求解多面集上二次函数的全局近似最优解问题,利用逐步缩小对偶间隙的处理办法,提出了一个新型分枝定界算法。新算法的主要改进之处是利用了Lagrange 对偶性获取下界。最后,用构造和随机产生的问题实例,对提出的新算法和传统的分枝定界算法做了初步的数值比较实验。计算实验表明算法对求解中大规模非凸二次规划问题的有效性。     

An evolutionary factor analysis computation for mining website structures

This paper explores website link structure considering websites as interconnected graphs and analyzing their features as a social network. Two networks have been extracted for representing websites: a domain network containing subdomains or external domains linked through the website and a page network containing webpages browsed from the root domain. Factor analysis provides the statistical methodology to adequately extract the main website profiles in terms of their internal structure. However, due to the large number of indicators, the task of selecting a representative subset of indicators becomes unaffordable. A genetic search of an optimum subset of indicators is proposed in this paper, selecting a multi-objective fitness function based on factor analysis results. The optimum solution provides a coherent and relevant categorization of website profiles, and highlights the possibilities of genetic algorithms as a tool for discovering new knowledge in the field of web mining.     

An advanced evolutionary strategy with an adaptive penalty function for mixed-discrete structural optimisation

Modular three-dimensional steel frames are prefabricated structures with slender elements and dominating compressive forces. Their analysis includes different non-linearities and the optimisation variables are often mixed-discrete or topological. The optimisation of these systems requires the most advanced solution techniques but also a systematic preprocessing, a sophisticated non-linear structural analysis and an automated handling of stress- and displacement-constraints as well as further side-constraints resulting from the underlying codes of practice. In this contribution, a (μ+1)-evolutionary strategy combined with an adaptive penalty function and a special selection scheme is presented to solve this optimisation problem.     

A fast evaluation strategy for evolutionary algorithms

Evolutionary algorithms (EAs) are a popular and robust strategy for optimization problems. However, these algorithms may require huge computation power for solving real problems. This paper introduces a “fast evolutionary algorithm” (FEA) that does not evaluate all new individuals, thus operating faster. A fitness and associated reliability value are assigned to each new individual that is only evaluated using the true fitness function if the reliability value is below a threshold. Moreover, applying random evaluation and error compensation strategies to the FEA further enhances the performance of the algorithm. Simulation results show that for six optimization functions an average reduction of 40% in the number of evaluations was observed while obtaining similar solutions to those found using a traditional evolutionary algorithm. For these same functions, by completion, the algorithm also finds a 4% better fitness value on average for the same number of evaluations. For an image compression system, the algorithm found on average 3% (12%) better fitness values or compression ratios using only 58% (65%) number of evaluations needed by an EA in lossless (lossy) compression mode.     

An adaptive neural network strategy for improving the computational performance of evolutionary structural optimization

This work is focused on improving the computational efficiency of evolutionary algorithms implemented in large-scale structural optimization problems. Locating optimal structural designs using evolutionary algorithms is a task associated with high computational cost, since a complete finite element (FE) analysis needs to be carried out for each parent and offspring design vector of the populations considered. Each of these FE solutions facilitates decision making regarding the feasibility or infeasibility of the corresponding structural design by evaluating the displacement and stress constraints specified for the structural problem at hand. This paper presents a neural network (NN) strategy to reliably predict, in the framework of an evolution strategies (ES) procedure for structural optimization, the feasibility or infeasibility of structural designs avoiding computationally expensive FE analyses. The proposed NN implementation is adaptive in the sense that the utilized NN configuration is appropriately updated as the ES process evolves by performing NN retrainings using information gradually accumulated during the ES execution. The prediction capabilities and the computational advantages offered by this adaptive NN scheme coupled with domain decomposition solution techniques are investigated in the context of design optimization of skeletal structures on both sequential and parallel computing environments.     

An evolutionary clustering algorithm for gene expression microarray data analysis

Clustering is concerned with the discovery of interesting groupings of records in a database. Many algorithms have been developed to tackle clustering problems in a variety of application domains. In particular, some of them have been used in bioinformatics research to uncover inherent clusters in gene expression microarray data. In this paper, we show how some popular clustering algorithms have been used for this purpose. Based on experiments using simulated and real data, we also show that the performance of these algorithms can be further improved. For more effective clustering of gene expression microarray data, which is typically characterized by a lot of noise, we propose a novel evolutionary algorithm called evolutionary clustering (EvoCluster). EvoCluster encodes an entire cluster grouping in a chromosome so that each gene in the chromosome encodes one cluster. Based on such encoding scheme, it makes use of a set of reproduction operators to facilitate the exchange of grouping information between chromosomes. The fitness function that the EvoCluster adopts is able to differentiate between how relevant a feature value is in determining a particular cluster grouping. As such, instead of just local pairwise distances, it also takes into consideration how clusters are arranged globally. Unlike many popular clustering algorithms, EvoCluster does not require the number of clusters to be decided in advance. Also, patterns hidden in each cluster can be explicitly revealed and presented for easy interpretation even by casual users. For performance evaluation, we have tested EvoCluster using both simulated and real data. Experimental results show that it can be very effective and robust even in the presence of noise and missing values. Also, when correlating the gene expression microarray data with DNA sequences, we were able to uncover significant biological binding sites (both previously known and unknown) in each cluster discovered by EvoCluster.     

Robust control via concave minimization local and global algorithms

This paper is concerned with the robust control problem of linear fractional representation (LFT) uncertain systems depending on a time-varying parameter uncertainty. Our main result exploits a linear matrix inequality (LMI) characterization involving scalings and Lyapunov variables subject to an additional essentially nonconvex algebraic constraint. The nonconvexity enters the problem in the form of a rank deficiency condition or matrix inverse relation on the scalings only. It is shown that such problems, but also more generally rank inequalities and bilinear constraints, can be formulated as the minimization of a concave functional subject to LMI constraints. First of all, a local Frank and Wolfe (1956) feasible direction algorithm is introduced in this context to tackle this hard optimization problem. Exploiting the attractive concavity structure of the problem, several efficient global concave programming methods are then introduced and combined with the local feasible direction method to secure and certify global optimality of the solutions. Computational experiments indicate the viability of our algorithms, and in the worst case, they require the solution of a few LMI programs     

提高多目标进化算法分布性的动态调整机制

为提高多目标进化算法的分布性,提出一种基于极坐标的动态调整机制。在极坐标下,根据解集的拥挤程度,计算个体解的缩放系数。在进化过程中利用该缩放系数动态调整解集支配关系,适当提高分布性好的解在支配关系中的地位以改善解的分布。对测试函数的仿真试验结果表明,将该机制应用于经典算法能显著提高算法的分布性,同时保持良好的收敛性。     

基于吸引域的总体极小化问题的神经网络求解

神经网络求解优化问题具有非常强大的实时计算功用,因此近年来受到了密切的关注.这里考察了求解无约束总体极小化问题的神经网络方法,提出了一种新的网络求解模型.从基于吸引域分析方法为出发点证明了所给网络平稳点集合的全局吸引性.分析了网络的电路实现,并估计了各个平稳点的吸引域.这些理论分析与估计是构造所提神经网络模型的依据,同时也是网络可靠运行的基础.此外,数值模拟试验也充分揭示了这个网络模型在实际运行中都能够很好地求解总体极小化问题,是一个十分有效的神经网络系统.这里的结果表明:这里提出的网络模型无论从理论上还是实际运行中都能够可靠且稳定地求解总体极值问题,基于吸引域构造神经网络的方法是一种很有潜力的神经网络求解优化问题的研究方向.