Ensemble of niching algorithms

Although niching algorithms have been investigated for almost four decades as effective procedures to obtain several good and diverse solutions of an optimization problem, no effort has been reported on combining different niching algorithms to form an effective ensemble of niching algorithms. In this paper, we propose an ensemble of niching algorithms (ENA) and illustrate the concept by an instantiation which is realized using four different parallel populations. The offspring of each population is considered by all parallel populations. The instantiation is tested on a set of 16 real and binary problems and compared against the single niching methods with respect to searching ability and computation time. Results confirm that ENA method is as good as or better than the best single method in it on every test problem. Moreover, comparison with other state-of-the-art niching algorithms demonstrates the competitiveness of our proposed ENA.     

A niching genetic k-means algorithm and its applications to gene expression data

Partitional clustering is a common approach to cluster analysis. Although many algorithms have been proposed, partitional clustering remains a challenging problem with respect to the reliability and efficiency of recovering high quality solutions in terms of its criterion functions. In this paper, we propose a niching genetic k-means algorithm (NGKA) for partitional clustering, which aims at reliably and efficiently identifying high quality solutions in terms of the sum of squared errors criterion. Within the NGKA, we design a niching method, which encourages mating among similar clustering solutions while allowing for some competitions among dissimilar solutions, and integrate it into a genetic algorithm to prevent premature convergence during the evolutionary clustering search. Further, we incorporate one step of k-means operation into the regeneration steps of the resulted niching genetic algorithm to improve its computational efficiency. The proposed algorithm was applied to cluster both simulated data and gene expression data and compared with previous work. Experimental results clear show that the NGKA is an effective clustering algorithm and outperforms two other genetic algorithm based clustering methods implemented for comparison.     

An investigation on niching multiple species based on population replacement strategies for multimodal functions optimization

This paper studies the niching mechanism based on population replacement in the process of evolution to solve the multimodal functions optimization (MMFO) problems. In order to niche multiple species for the MMFO tasks, the overlapping population replacement is surely needed because the offspring population most probably does not inherit all of the genetic information contained in its parental population, and the basic procedure for niching genetic algorithms with overlapping population replacement is presented. Then four niching schemes, the nearest neighbors replacement crowding (NNRC), the species conservation technique (SCT), the HFC-I (implicit hierarchical fair competition), and the CPE (clearing procedure with elitist) are investigated. These niching schemes are characterized with regard to different niching strategies and parameterizations, and the corresponding niching procedures are outlined. Finally, experiments are carried out on a suite of test functions to compare different niching strategies regarding niching efficiency and scalability. Experimental results illustrate the intrinsic difference of the four niching schemes. The NNRC and HFC-I have a mechanism of multiple species coevolution via adapting multiple species to different niches, while the SCT and CPE tend to make use of a mandatory mechanism to conserve species just like the grid searching over the solution space based on species distance or clearing radius. All niching methods are able to deal with complex MMFO problems, while the NNRC and HFC-I show a better performance in terms of niching efficiency and scalability, and are more robust regarding the algorithm parameterization.

Niching Without Niching Parameters: Particle Swarm Optimization Using a Ring Topology

Niching is an important technique for multimodal optimization. Most existing niching methods require specification of certain niching parameters in order to perform well. These niching parameters, often used to inform a niching algorithm how far apart between two closest optima or the number of optima in the search space, are typically difficult to set as they are problem dependent. This paper describes a simple yet effective niching algorithm, a particle swarm optimization (PSO) algorithm using a ring neighborhood topology, which does not require any niching parameters. A PSO algorithm using the ring topology can operate as a niching algorithm by using individual particles' local memories to form a stable network retaining the best positions found so far, while these particles explore the search space more broadly. Given a reasonably large population uniformly distributed in the search space, PSO algorithms using the ring topology are able to form stable niches across different local neighborhoods, eventually locating multiple global/local optima. The complexity of these niching algorithms is only $ {cal O}(N)$, where $N$ is the population size. Experimental results suggest that PSO algorithms using the ring topology are able to provide superior and more consistent performance over some existing PSO niching algorithms that require niching parameters.      

The crowding approach to niching in genetic algorithms

A wide range of niching techniques have been investigated in evolutionary and genetic algorithms. In this article, we focus on niching using crowding techniques in the context of what we call local tournament algorithms. In addition to deterministic and probabilistic crowding, the family of local tournament algorithms includes the Metropolis algorithm, simulated annealing, restricted tournament selection, and parallel recombinative simulated annealing. We describe an algorithmic and analytical framework which is applicable to a wide range of crowding algorithms. As an example of utilizing this framework, we present and analyze the probabilistic crowding niching algorithm. Like the closely related deterministic crowding approach, probabilistic crowding is fast, simple, and requires no parameters beyond those of classical genetic algorithms. In probabilistic crowding, subpopulations are maintained reliably, and we show that it is possible to analyze and predict how this maintenance takes place. We also provide novel results for deterministic crowding, show how different crowding replacement rules can be combined in portfolios, and discuss population sizing. Our analysis is backed up by experiments that further increase the understanding of probabilistic crowding.     

A Review of Niching Genetic Algorithms for Multimodal Function Optimization

In this paper, a comprehensive review of approaches to solve multimodal function optimization problems via genetic niching algorithms is provided. These algorithms are presented according to their space–time classification. Methods based on fitness sharing and crowding methods are described in detail as they are the most frequently used.     

PID control of MIMO process based on rank niching genetic algorithm

Non-linear multiple-input multiple-output (MIMO) processes which are common in industrial plants are characterized by significant interactions and non- linearities among their variables. Thus, tuning several controllers in complex industrial plants is a challenge for process engineers and operators. An approach for adjusting the parameters of n proportional–integral–derivative (PID) controllers based on multiobjective optimization and genetic algorithms (GA) is presented in this paper. A modified genetic algorithm with elitist model and niching method is developed to guarantee a set of solutions (set of PID parameters) with different tradeoffs regarding the multiple requirements of the control performance. Experiments considering a fluid catalytic cracking (FCC) unit, under PI and dynamic matrix control (DMC) are carried out in order to evaluate the proposed method. The results show that the proposed approach is an alternative to classical techniques as Ziegler–Nichols rules and others.     

Analysis of new niching genetic algorithms for finding multiple solutions in the job shop scheduling

In this paper the performance of the most recent multi-modal genetic algorithms (MMGAs) on the Job Shop Scheduling Problem (JSSP) is compared in term of efficacy, multi-solution based efficacy (the algorithm’s capability to find multiple optima), and diversity in the final set of solutions. The capability of Genetic Algorithms (GAs) to work on a set of solutions allows us to reach different optima in only one run. Nevertheless, simple GAs are not able to maintain different solutions in the last iteration, therefore reaching only one local or global optimum. Research based on the preservation of the diversity through MMGAs has provided very promising results. These techniques, known as niching methods or MMGAs, allow not only to obtain different multiple global optima, but also to preserve useful diversity against convergence to only one solution (the usual behaviour of classical GAs). In previous works, a significant difference in the performance among methods was found, as well as the importance of a suitable parametrization. In this work classic methods are compared to the most recent MMGAs, grouped in three classes (sharing, clearing and species competition), for JSSP. Our experimental study found that those new MMGAs which have a certain type of replacement process perform much better (in terms of highest efficacy and multi-solution based efficacy) than classical MMGAs which do not have this type of process.     

自适应小生境遗传算法在系统级综合中的应用

多目标遗传算法是解决SoC系统级综合问题的有效途径之一,但现有的遗传算法只能求得非劣解集前沿的一部分,局部搜索能力差,收敛速度较慢。该文通过结合小生境技术,根据种群往代的多样性信息,自适应地确定子种群的规模和交叉、变异的概率,提出一种自适应小生境遗传算法,有效提高解集的覆盖率,加快收敛速度。以视频编解码的系统级综合为例,证明该算法可以较快地产生较多非 劣解。     

Multimodal optimization using a bi-objective evolutionary algorithm

In a multimodal optimization task, the main purpose is to find multiple optimal solutions (global and local), so that the user can have better knowledge about different optimal solutions in the search space and as and when needed, the current solution may be switched to another suitable optimum solution. To this end, evolutionary optimization algorithms (EA) stand as viable methodologies mainly due to their ability to find and capture multiple solutions within a population in a single simulation run. With the preselection method suggested in 1970, there has been a steady suggestion of new algorithms. Most of these methodologies employed a niching scheme in an existing single-objective evolutionary algorithm framework so that similar solutions in a population are deemphasized in order to focus and maintain multiple distant yet near-optimal solutions. In this paper, we use a completely different strategy in which the single-objective multimodal optimization problem is converted into a suitable bi-objective optimization problem so that all optimal solutions become members of the resulting weak Pareto-optimal set. With the modified definitions of domination and different formulations of an artificially created additional objective function, we present successful results on problems with as large as 500 optima. Most past multimodal EA studies considered problems having only a few variables. In this paper, we have solved up to 16-variable test problems having as many as 48 optimal solutions and for the first time suggested multimodal constrained test problems which are scalable in terms of number of optima, constraints, and variables. The concept of using bi-objective optimization for solving single-objective multimodal optimization problems seems novel and interesting, and more importantly opens up further avenues for research and application.     

排挤小生态遗传算法的改进方法

提出了基于搜索空间聚类分析的聚类排挤小生态遗传算法.通过分析适应值曲面的拓扑结构和扩大相似个体的搜索范围,聚类排挤可确定搜索空间的局部性,减少排挤的替换错误并抑制种群的遗传漂移;通过结合确定性替换和概率替换策略,聚类排挤提高了并行局部爬山能力和并行子种群维持能力.对不同多峰问题的仿真优化结果表明,聚类排挤小生态遗传算法的有效峰数量、平均峰值比和全局最优解比等综合性能一致地优于适应值共享、简单确定性排挤和概率排挤等小生态遗传算法.     

非线性方程组求解的一种新方法

针对现有的非线性方程组求解方法不能同时收敛到所有解的问题,提出了一种混合小生境遗传算法的求解新方法.采用确定性拥挤小生境创造出种群的小生境进化环境,克服遗传算法的遗传漂移现象,维持种群的多样性,使算法能同时收敛到多个解;以拟牛顿算法作为遗传算法的局部搜索算子进行精确搜索,进一步提高算法收敛速度和精度.选择了几组典型的多解非线性方程组进行了求解验证,结果表明所设计的混合小生境遗传算法能在解的定义域内同时收敛到所有解,收敛速度快、精度高,是求解非线性方程组全局解的一种有效方法.     

一种求解旅行商问题的进化多目标优化方法

为了克服传统小生境(Niching)策略中的参数设置难题,提出一种求解旅行商问题的进化多目标优化方法:建立以路径长度和平均离群距离为目标的双目标优化模型,利用改进非支配排序遗传算法(NSGAII)进行求解.为了在全局探索能力与局部开发能力之间保持平衡,算法中采用一种使路径长度相同的可行解互不占优的评价策略,并通过一种新的离散差分进化算子和简化的2-Opt策略生成候选解.与已有算法的数值试验结果比较表明,求解旅行商问题(TSP)的改进非支配排序遗传算法(NSGAII-TSP)能够更好地保持种群多样性,从而克服局部最优解的吸引并具有更鲁棒的全局探索能力.通过借助特殊的个体评价策略,所提出的算法可以更好地进行全局优化,甚至同时得到多个全局最优解.     

A review: accuracy optimization in clustering ensembles using genetic algorithms

The clustering ensemble has emerged as a prominent method for improving robustness, stability, and accuracy of unsupervised classification solutions. It combines multiple partitions generated by different clustering algorithms into a single clustering solution. Genetic algorithms are known as methods with high ability to solve optimization problems including clustering. To date, significant progress has been contributed to find consensus clustering that will yield better results than existing clustering. This paper presents a survey of genetic algorithms designed for clustering ensembles. It begins with the introduction of clustering ensembles and clustering ensemble algorithms. Subsequently, this paper describes a number of suggested genetic-guided clustering ensemble algorithms, in particular the genotypes, fitness functions, and genetic operations. Next, clustering accuracies among the genetic-guided clustering ensemble algorithms is compared. This paper concludes that using genetic algorithms in clustering ensemble improves the clustering accuracy and addresses open questions subject to future research.     

A niche-elimination operation based NSGA-III algorithm for many-objective optimization

Decomposition-based multi-objective evolutionary algorithms have been found to be very promising for many-objective optimization. The recently presented non-dominated sorting genetic algorithm III (NSGA-III) employs the decomposition idea to efficiently promote the population diversity. However, due to the low selection pressure of the Pareto-dominance relation the convergence of NSGA-III could still be improved. For this purpose, an improved NSGA-III algorithm based on niche-elimination operation (we call it NSGA-III-NE) is proposed. In the proposed algorithm, an adaptive penalty distance (APD) function is presented to consider the importance of convergence and diversity in the different stages of the evolutionary process. Moreover, the niche-elimination operation is designed by exploiting the niching technique and the worse-elimination strategy. The niching technique identifies the most crowded subregion, and the worse-elimination strategy finds and further eliminates the worst individual. The proposed NSGA-III-NE is tested on a number of well-known benchmark problems with up to fifteen objectives and shows the competitive performance compared with five state-of-the-art decomposition-based algorithms. Additionally, a vector angle based selection strategy is also proposed for handling irregular Pareto fronts.     

Tackling the premature convergence problem in Monte-Carlo localization

Monte-Carlo localization uses particle filtering to estimate the position of the robot. The method is known to suffer from the loss of potential positions when there is ambiguity present in the environment. Since many indoor environments are highly symmetric, this problem of premature convergence is problematic for indoor robot navigation. It is, however, rarely studied in particle filters. We introduce a number of so-called niching methods used in genetic algorithms, and implement them on a particle filter for Monte-Carlo localization. The experiments show a significant improvement in the diversity maintaining performance of the particle filter.     

A class of multiple time scale algorithms for simulating power system frequency dynamics

In this paper the wide spectrum of the response times involved in the average system frequency dynamic behavior of power systems is exploited in the computer simulation of the power system. The various subsystem models of the power plants are decomposed into different time scales. A class of multiple time scale algorithm is proposed that used different time steps for the integration of the subsystems belonging to different time scale groupings. A special feature of the algorithms is the coordination of the interfaces between the different time scale solutions. Numerical study of a 5 bus power system is included to demonstrate the improvement in the solution time, stability, and accuracy of the new approach over previous methods.     

Allocating data to multicomputer nodes by physical optimization algorithms for loosely synchronous computations

Three optimization methods derived from natural sciences are considered for allocating data to multicomputer nodes. These are simulated annealing, genetic algorithms and neural networks. A number of design choices and the addition of preprocessing and postprocessing steps lead to versions of the algorithms which differ in solution qualities and execution times. In this paper the performances of these versions are critically evaluated and compared for test cases with different features. The performance criteria are solution quality, execution time, robustness, bias and parallelizability. Experimental results show that the physical algorithms produce better solutions than those of recursive bisection methods and that they have diverse properties. Hence, different algorithms would be suitable for different applications. For example, the annealing and genetic algorithms produce better solutions and do not show a bias towards particular problem structures, but they are slower than the neural network algorithms. Preprocessing graph contraction is one of the additional steps suggested for the physical methods. It produces a significant reduction in execution time, which is necessary for their applicability to large problems.     

基于改进遗传算法的稀疏重构算法

稀疏重构算法中凸松弛法在恢复效率方面、贪婪追踪法在恢复精度方面存在不足,基于遗传算法迭代优化的思想,结合模拟退火以及多种群算法的优势,提出了基于模拟退火遗传算法和基于多种群遗传算法的启发式稀疏重构算法。所提算法均从传统遗传算法易陷入局部最优解的缺陷出发,分别通过保持个体间的差异性和提高种群多样性来搜索待求稀疏信号的全局最优解,并通过理论分析证明了所提算法参数选取及搜索策略的有效性。此外,以阵列信号处理中空间信源的波达方向（DOA）估计问题为例,验证所提算法的有效性。仿真结果表明,相较于正交匹配追踪OMP算法和基于l1范数奇异值分解的l1-SVD算法,所提算法提高了DOA估计的精度,且降低了运算复杂度,使其快速收敛至全局最优解。     

A Weighted Sum Validity Function for clustering with a Hybrid Niching Genetic Algorithm.

Clustering is inherently a difficult problem, both with respect to the construction of adequate objective functions as well as to the optimization of the objective functions. In this paper, we suggest an objective function called the Weighted Sum Validity Function (WSVF), which is a weighted sum of the several normalized cluster validity functions. Further, we propose a Hybrid Niching Genetic Algorithm (HNGA), which can be used for the optimization of the WSVF to automatically evolve the proper number of clusters as well as appropriate partitioning of the data set. Within the HNGA, a niching method is developed to preserve both the diversity of the population with respect to the number of clusters encoded in the individuals and the diversity of the subpopulation with the same number of clusters during the search. In addition, we hybridize the niching method with the k-means algorithm. In the experiments, we show the effectiveness of both the HNGA and the WSVF. In comparison with other related genetic clustering algorithms, the HNGA can consistently and efficiently converge to the best known optimum corresponding to the given data in concurrence with the convergence result. The WSVF is found generally able to improve the confidence of clustering solutions and achieve more accurate and robust results.