基于差分扰动的混合蛙跳算法

针对基本混合蛙跳算法在处理复杂函数优化问题时容易陷入局部最优、求解精度低的缺点,借鉴差分进化中的变异思想,提出了一种改进的混合蛙跳算法,利用子群中其他个体的有利信息,对其更新策略进行局部扰动。实验结果表明,改进的混合蛙跳算法对复杂函数优化问题具有较强的求解能力。算法寻优效率高、全局性能好、优化结果稳定,性能明显优于所比较的算法。     

Novel computing paradigms for parameter estimation in Hammerstein controlled auto regressive auto regressive moving average systems

In the present study, strength of meta-heuristic computing techniques is exploited for estimation problem of Hammerstein controlled auto regressive auto regressive moving average (HCARARMA) system using differential evolution (DE), genetic algorithms (GAs), pattern search (PS) and simulated annealing (SA) algorithms. The approximation theory in mean squared error sense is utilized for construction of cost function for HCARARMA model and highly uncorrelated adjustable parameter of the system is optimized with global search exploration of DE, GAs, PS and SA algorithms. Comparative study is carried out from desired known parameters of the HCARARMA model for different degree of freedom and noise variation scenarios. Performance analysis of the DE, GAs, PS and SA algorithms is conducted through results of statistics based on sufficient large independent executions in terms of measure of central tendency and variation for both precision and complexity indices. The exhaustive simulations established that the population-based heuristics are more accurate than single solution-based methodologies for HCARARMA identification.     

Colonial competitive differential evolution: An experimental study for optimal economic load dispatch

Differential evolution (DE) algorithm is a population-based algorithm designed for global optimization of the optimization problems. This paper proposes a different DE algorithm based on mathematical modeling of socio-political evolution which is called Colonial Competitive Differential Evolution (CCDE). The two typical CCDE algorithms are benchmarked on three well-known test functions, and the results are verified by a comparative study with two original DE algorithms which include DE/best/1 and DE/rand/2. Also, the effectiveness of CCDE algorithms is tested on Economic Load Dispatch (ELD) problem including 10, 15, 40, and 140-unit test systems. In this study, the constraints and operational limitations, such as valve-point loading, transmission losses, ramp rate limits, and prohibited operating zones are considered. The comparative results show that the CCDE algorithms have good performance and are reliable tools in solving ELD problem.     

Design of microwave broadband absorbers using a self‐adaptive differential evolution algorithm

Microwave broadband absorber design for a desired frequency and angle range is presented. The design technique is based on a self‐adaptive Differential Evolution (DE) algorithm. Numerical examples are compared with the existing in the literature and with those found by the other evolutionary algorithms. The results show that the new DE algorithm version outerperforms other global optimizers like Particle swarm optimization (PSO) variants and the classical DE algorithm. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.     

Linking physical remote sensing models with crop growth simulation models,applied for sugar beet

Abstract

In recent years, remote sensing and crop growth simulation models have become increasingly recognized as potential tools for growth monitoring and yield estimation of agricultural crops. In this paper, a methodology is developed to link remote sensing data with a crop growth model for monitoring crop growth and development. The Cloud equations for radar backscattering and the optical canopy radiation model EXTRAD were linked to the crop growth simulation model SUCROS: SUCROS-Cloud-EXTRAD. This combined model was initialized and re-parameterized to fit simulated X-band radar backscattering and/or optical reflectance values, to measured values. The developed methodology was applied for sugar beet. The simulated canopy biomass after initialization and re-parameterization was compared with simulated canopy biomass with SUCROS using standard input, and with measured biomass in the field, for 11 fields in different years and different locations. The seasonal-average error in simulated canopy biomass was smaller with the initialized and re-parameterized model (225-475 kg ha^?1), than with SUCROS using standard input (390-700 kg ha^?1), with ‘end-of-season’ canopy biomass values between 5500 and 7000kgha^?1. X-band radar backscattering and optical reflectance data were very effective in the initialization of SUCROS. The radar backscattering data further adjusted SUCROS only during early crop growth (exponential growth), whereas optical data still adjusted SUCROS until late in the growing season (at high levels of leaf area index (LAI), 3-5).     

A hybrid differential evolution algorithm based on particle swarm optimization for nonconvex economic dispatch problems

This paper presents the design and application of an efficient hybrid heuristic search method to solve the practical economic dispatch problem considering many nonlinear characteristics of power generators, and their operational constraints, such as transmission losses, valve-point effects, multi-fuel options, prohibited operating zones, ramp rate limits and spinning reserve. These practical operation constraints which can usually be found at the same time in realistic power system operations make the economic load dispatch problem a nonsmooth optimization problem having complex and nonconvex features with heavy equality and inequality constraints.The proposed approach combines in the most effective way the properties of two of the most popular evolutionary optimization techniques now in use for power system optimization, the Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. To improve the global optimization property of DE, the PSO procedure is integrated as additional mutation operator.The effectiveness of the proposed algorithm (termed DEPSO) is demonstrated by solving four kinds of ELD problems with nonsmooth and nonconvex solution spaces. The comparative results with some of the most recently published methods confirm the effectiveness of the proposed strategy to find accurate and feasible optimal solutions for practical ELD problems.     

Optimal foraging algorithm for global optimization

An optimization algorithm, inspired by the animal Behavioral Ecology Theory—Optimal Foraging Theory, named the Optimal Foraging Algorithm (OFA) has been developed. As a new stochastic search algorithm, OFA is used to solve the global optimization problems following the animal foraging behavior. During foraging, animals know how to find the best pitch with abundant prey; in establishing OFA, the basic operator of OFA was constructed following this foraging strategy. During foraging, an individual of the foraging swarms obtained more opportunities to capture prey through recruitment; in OFA the recruitment was adopted to ensure the algorithm has a higher chance to receive the optimal solution. Meanwhile, the precise model of prey choices proposed by Krebs et al. was modified and adopted to establish the optimal solution choosing strategy of OFA. The OFA was tested on the benchmark functions that present difficulties common to many global optimization problems. The performance comparisons among the OFA, real coded genetic algorithms (RCGAs), Differential Evolution (DE), Particle Swarm Optimization (PSO) algorithm, Bees Algorithm (BA), Bacteria Foraging Optimization Algorithm (BFOA) and Shuffled Frog-leaping Algorithm (SFLA) are carried out through experiments. The parameter of OFA and the dimensions of the multi-functions are researched. The results obtained by experiments and Kruskal-Wallis test indicate that the performance of OFA is better than the other six algorithms in terms of the ability to converge to the optimal or the near-optimal solutions, and the performance of OFA is the second-best one from the view of the statistical analysis.     

Particle Swarm Optimization and Differential Evolution for model-based object detection

Automatically detecting objects in images or video sequences is one of the most relevant and frequently tackled tasks in computer vision and pattern recognition.The starting point for this work is a very general model-based approach to object detection. The problem is turned into a global continuous optimization one: given a parametric model of the object to be detected within an image, a function is maximized, which represents the similarity between the model and a region of the image under investigation.In particular, in this work, the optimization problem is tackled using Particle Swarm Optimization (PSO) and Differential Evolution (DE). We compare the performances of these optimization techniques on two real-world paradigmatic problems, onto which many other real-world object detection problems can be mapped: hippocampus localization in histological images and human body pose estimation in video sequences. In the former, a 2D deformable model of a section of the hippocampus is fit to the corresponding region of a histological image, to accurately localize such a structure and analyze gene expression in specific sub-regions. In the latter, an articulated 3D model of a human body is matched against a set of images of a human performing some action, taken from different perspectives, to estimate the subject's posture in space.Given the significant computational burden imposed by this approach, we implemented PSO and DE as parallel algorithms within the nVIDIA? CUDA computing architecture.     

The spherical search algorithm for bound-constrained global optimization problems

In this paper, a new optimization algorithm called Spherical Search (SS) is proposed to solve the bound-constrained non-linear global optimization problems. The main operations of SS are the calculation of spherical boundary and generation of new trial solution on the surface of the spherical boundary. These operations are mathematically modeled with some more basic level operators: Initialization of solution, greedy selection and parameter adaptation, and are employed on the 30 black-box bound constrained global optimization problems. This study also analyzes the applicability of the proposed algorithm on a set of real-life optimization problems. Meanwhile, to show the robustness and proficiency of SS, the obtained results of the proposed algorithm are compared with the results of other well-known optimization algorithms and their advanced variants: Particle Swarm Optimization (PSO), Differential Evolution (DE), and Covariance Matrix Adapted Evolution Strategy (CMA-ES). The comparative analysis reveals that the performance of SS is quite competitive with respect to the other peer algorithms.     

Multilevel thresholding based on Chaotic Darwinian Particle Swarm Optimization for segmentation of satellite images

This paper proposes an improved variant of Darwinian Particle Swarm Optimization algorithm based on chaotic functions. Most of the evolutionary algorithms faces the problem of getting trapped in local optima in its search for global optimum solutions. This is highly influenced by the use of random sequences by different operators in these algorithms along their run. The proposed algorithm replaces random sequences by chaotic sequences mitigating the problem of premature convergence. Experiments were conducted to investigate the efficiency of 10 defined chaotic maps and the best one was chosen. Performance of the proposed Chaotic Darwinian Particle Swarm Optimization (CDPSO) algorithm is compared with chaotic variants of optimization algorithms like Cuckoo Search, Harmony Search, Differential Evolution and Particle Swarm Optimization exploiting the chosen optimal chaotic map. Various histogram thresholding measures like minimum cross entropy and Tsallis entropy were used as objective functions and implemented for satellite image segmentation scenario. The experimental results are validated qualitatively and quantitatively by evaluating the mean, standard deviation of the fitness values, PSNR, MSE, SSIM and the total time required for the execution of each optimization algorithm.     

利用改进遗传算法的参数估计

基于极大似然法的参数估计实质上是一个复杂的非线性优化问题,传统的优化方法计算效率较低且容易陷入局部极值。而遗传算法是一种有导向的随机搜索方法,能以较大的概率收敛到全局最优解。本文将单纯形法引入到并行遗传算法中,提出了一种改进的遗传算法,可以有效地提高算法的收敛速度、防止搜索过程中的早熟现象。应用于系统初始状态未知时的参数估计问题,获得了满意的结果。     

Differential evolution methods based on local searches

In this paper we analyze the behavior of a quite standard Differential Evolution (DE) algorithm applied to the objective function transformed by means of local searches. First some surprising results are presented which concern the application of this method to standard test functions.Later we introduce an application to disk- and to sphere-packing problems, two well known and particularly hard global optimization problems. For these problems some more refined variations of the basic method are necessary in order to take at least partially into considerations the many symmetries those problems possess. Coupling these techniques with DE and local optimization resulted in a new method which, when tested on moderately sized packing problems, was capable of confirming known putative optima for the problem of packing disks, and of discovering quite a significant number of new putative optima for the problem of packing spheres.     

Exploring dynamic self-adaptive populations in differential evolution

Although the Differential Evolution (DE) algorithm has been shown to be a simple yet powerful evolutionary algorithm for optimizing continuous functions, users are still faced with the problem of preliminary testing and hand-tuning of the evolutionary parameters prior to commencing the actual optimization process. As a solution, self-adaptation has been found to be highly beneficial in automatically and dynamically adjusting evolutionary parameters such as crossover rates and mutation rates. In this paper, we present a first attempt at self-adapting the population size parameter in addition to self-adapting crossover and mutation rates. Firstly, our main objective is to demonstrate the feasibility of self-adapting the population size parameter in DE. Using De Jong's F1–F5 benchmark test problems, we showed that DE with self-adaptive populations produced highly competitive results compared to a conventional DE algorithm with static populations. In addition to reducing the number of parameters used in DE, the proposed algorithm actually outperformed the conventional DE algorithm for one of the test problems. It was also found that that an absolute encoding methodology for self-adapting population size in DE produced results with greater optimization reliability compared to a relative encoding methodology.     

Improving the performance and scalability of Differential Evolution on problems exhibiting parameter interactions

Differential Evolution (DE) is a powerful optimization procedure that self-adapts to the search space, although DE lacks diversity and sufficient bias in the mutation step to make efficient progress on non-separable problems. We present an enhancement to DE that introduces greater diversity while also directing the search to more promising regions. The Combinatorial Sampling Differential Evolution (CSDE) is introduced which can sample vectors in two ways; highly correlated with the search space or around a ‘better’ individual. The CSDE approach can provide a similar number of samples as crossover, without being biased towards the principle coordinate axes of a decision space. This approach to sampling vectors is capable of optimizing problems with extensive parameter interactions. It also demonstrates fast convergence towards the global optimum and is highly scalable in the decision space on a variety of single and multi-objective problems due to the balance between sampling highly directed correlated vectors and non-correlated vectors which contribute to sampling diversity.     

An improved differential evolution algorithm with fitness-based adaptation of the control parameters

Differential Evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms of current interest. DE operates through the similar computational steps as employed by a standard Evolutionary Algorithm (EA). However, unlike the traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used, which makes the scheme self-organizing in this respect. Scale Factor (F) and Crossover Rate (Cr) are two very important control parameters of DE since the former regulates the step-size taken while mutating a population member in DE and the latter controls the number of search variables inherited by an offspring from its parent during recombination. This article describes a very simple yet very much effective adaptation technique for tuning both F and Cr, on the run, without any user intervention. The adaptation strategy is based on the objective function value of individuals in the DE population. Comparison with the best-known and expensive variants of DE over fourteen well-known numerical benchmarks and one real-life engineering problem reflects the superiority of proposed parameter tuning scheme in terms of accuracy, convergence speed, and robustness.     

基于分布估计的离散差分骨干粒子群优化

粒子群优化（PSO）和差分演化（DE）是两种新兴的优化技术,已经成功地应用于连续优化问题,但是它们至今尚不能像解决连续优化问题那样有效地处理组合优化问题。最近,有人提出差分骨干PSO（DBPSO）用于解决连续优化问题。首先提出离散DBPSO用于组合优化问题,然后在离散DBPSO中引入分布估计算法（EDA）来提高性能,把EDA抽样得到的全局统计信息和DBPSO获得的局部演化信息相结合来产生新解,形成基于EDA的离散DBPSO。实验结果表明EDA能大大提高离散DBPSO的性能。     

Multiobjective differential evolution using homeostasis based mutation for application in software cost estimation

The problem in software cost estimation revolves around accuracy. To improve the accuracy, heuristic/meta-heuristic algorithms have been known to yield better results when it is applied in the domain of software cost estimation. For the sake of accuracy in results, we are still modifying these algorithms. Here we have proposed a new meta-heuristic algorithm based on Differential Evolution (DE) by Homeostasis mutation operator. Software development requires high prediction and low Root Mean Squared Error (RMSE) and mean magnitude relative error(MMRE). The problem in software cost estimation relates to accurate prediction and minimization of RMSE and MMRE, which are used to solve multiobjective optimization. Many versions of DE were proposed, however multi-objective versions where the concept of Pareto optimality is used, are most popular. Pareto-Based Differential Evolution (PBDE) is one of them. Although the performance of this algorithm is very good, its convergence rate can be further improved by minimizing the time complexity of nondominated sorting, and by improving the diversity of solutions. This has been implemented by using efficient nondominated algorithm whose time complexity is better than the previous one and a new mutation scheme is implemented in DE which can provide more diversity among solutions. The proposed variant multiplies the Homeostasis value with one more vector, named the Homeostasis mutation vector, in the existing mutation vector to provide more bandwidth for selecting effective mutant solutions. The proposed approach provides more promising solutions to guide the evolution and helps DE escape the situation of stagnation. The performance of the proposed algorithm is evaluated on twelve benchmark test functions (bi-objective and tri-objective) on the Pareto-optimal front. The performance of the proposed algorithm is compared with other state-of-the-art algorithms on five multi-objective evolutionary algorithms (MOEAs). The result verifies that our proposed Homeostasis mutation strategy performs better than other state-of-the-art algorithms. Finally, application of MODE-HBM is applied to solve in terms of Pareto front, representing the trade-off between development RMSE, MMRE, and prediction for COCOMO model.     

Optimization of fed-batch fermentation processes with bio-inspired algorithms

The optimization of the feeding trajectories in fed-batch fermentation processes is a complex problem that has gained attention given its significant economical impact. A number of bio-inspired algorithms have approached this task with considerable success, but systematic and statistically significant comparisons of the different alternatives are still lacking. In this paper, the performance of different metaheuristics, such as Evolutionary Algorithms (EAs), Differential Evolution (DE) and Particle Swarm Optimization (PSO) is compared, resorting to several case studies taken from literature and conducting a thorough statistical validation of the results. DE obtains the best overall performance, showing a consistent ability to find good solutions and presenting a good convergence speed, with the DE/rand variants being the ones with the best performance. A freely available computational application, OptFerm, is described that provides an interface allowing users to apply the proposed methods to their own models and data.     

基于差分进化的临近空间探测资源调度

针对临近空间预警探测平台的协同资源调度问题,根据临近空间探测资源与探测任务的特点,考虑多种实际约束条件,构建临近空间探测资源的优化调度模型。根据该模型规模大、求解复杂的情况,提出一种基于种群灾变的自适应差分进化算法求解模型。仿真实验结果表明,该算法相比传统算法收敛速度更快、寻优能力更强。     

差分进化粒子群混合优化算法的研究与应用

对基本粒子群算法（PSO）和差分进化算法（DE）进行了分析,有机结合两种进化算法提出了一种新型差分进化粒子群混合优化算法,该算法将优化过程分成两阶段,两分群分别采用PSO算法和DE算法同时进行。迭代过程中引入进化速度因子并通过群体间的信息交流阻止算法陷入局部最优。对4个高维复杂函数寻优测试表明算法的鲁棒性、收敛速度和精度,全局搜索能力均优于常规PSO和DE。将提出的改进算法用于乙烯收率软测量建模,应用结果表明模型精度较高、泛化性能较好。