Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike 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 for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

/pdf/differential-evolution-a-survey-of-the-state-of-the-art-3lz840pz2t.pdf

Differential Evolution: A Survey of the State-of-the-Art

This paper presents a variant of particle swarm optimizers (PSOs) that we call the comprehensive learning particle swarm optimizer (CLPSO), which uses a novel learning strategy whereby all other particles' historical best information is used to update a particle's velocity. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. Experiments were conducted (using codes available from http://www.ntu.edu.sg/home/epnsugan) on multimodal test functions such as Rosenbrock, Griewank, Rastrigin, Ackley, and Schwefel and composition functions both with and without coordinate rotation. The results demonstrate good performance of the CLPSO in solving multimodal problems when compared with eight other recent variants of the PSO.

Comprehensive learning particle swarm optimizer for global optimization of multimodal functions

A new differential evolution (DE) algorithm, JADE, is proposed to improve optimization performance by implementing a new mutation strategy ldquoDE/current-to-p bestrdquo with optional external archive and updating control parameters in an adaptive manner. The DE/current-to-pbest is a generalization of the classic ldquoDE/current-to-best,rdquo while the optional archive operation utilizes historical data to provide information of progress direction. Both operations diversify the population and improve the convergence performance. The parameter adaptation automatically updates the control parameters to appropriate values and avoids a user's prior knowledge of the relationship between the parameter settings and the characteristics of optimization problems. It is thus helpful to improve the robustness of the algorithm. Simulation results show that JADE is better than, or at least comparable to, other classic or adaptive DE algorithms, the canonical particle swarm optimization, and other evolutionary algorithms from the literature in terms of convergence performance for a set of 20 benchmark problems. JADE with an external archive shows promising results for relatively high dimensional problems. In addition, it clearly shows that there is no fixed control parameter setting suitable for various problems or even at different optimization stages of a single problem.

JADE: Adaptive Differential Evolution With Optional External Archive

Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.

/pdf/particle-swarm-optimization-basic-concepts-variants-and-4h7hr86hk4.pdf

Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

The particle swarm optimizer (PSO) is a stochastic, population-based optimization technique that can be applied to a wide range of problems, including neural network training. This paper presents a variation on the traditional PSO algorithm, called the cooperative particle swarm optimizer, or CPSO, employing cooperative behavior to significantly improve the performance of the original algorithm. This is achieved by using multiple swarms to optimize different components of the solution vector cooperatively. Application of the new PSO algorithm on several benchmark optimization problems shows a marked improvement in performance over the traditional PSO.

A Cooperative approach to particle swarm optimization

Recent research has explored methods for modifying the particle swarm algorithm so that it samples from a probability distribution rather than generating the particle's trajectory from averaged differences. The present paper explores a model where a Gaussian sample is taken, with the mean of the distribution varying randomly within some bounds. In particular, these experiments use either a stochastic barycenter or a beta distribution to define the mean of the Gaussian sample.

Stochastic barycenters and beta distribution for Gaussian particle swarms

This paper presents a flexible framework to the task of featureselection in classification of DNA microarray data. Theuser can select a number of filter methods in the preprocessingstage and choose from a wide set of classifiers (models and algorithms from WEKA [17] are available) and accuracy estimation methods. This approach implements wrapper methods, where Evolutionary Algorithms, with variable sized set based representations are used to reduce the number of attributes. Two case studies were used to validate the approach, with three distinct classifiers (1-nearest neighbour, decision trees, SVMs), a filter method based on discriminant fuzzy patterns and k-fold cross-validation to estimate the generalization error.

A platform for the selection of genes in DNA microarraydata using evolutionary algorithms

The optimization of input variables (typically feeding trajectories over time) in fed-batch fermentations has gained special attention, given the economic impact and the complexity of the problem. Evolutionary Computation (EC) has been a source of algorithms that have shown good performance in this task. In this chapter, Differential Evolution (DE) is proposed to tackle this problem and quite promising results are shown. DE is tested in several real world case studies and compared with other EC algorihtms, such as Evolutionary Algorithms and Particle Swarms. Furthermore, DE is also proposed as an alternative to perform online optimization, where the input variables are adjusted while the real fermentation process is ongoing. In this case, a changing landscape is optimized, therefore making the task of the algorithms more difficult. However, that fact does not impair the performance of the DE and confirms its good behaviour.

/pdf/differential-evolution-for-the-offline-and-online-2puijlulsl.pdf

Differential Evolution for the Offline and Online Optimization of Fed-Batch Fermentation Processes

In this paper, we perform an exploratory study of user Conversion Rate (CVR) prediction using recent big data from a global mobile marketing company. We design a stream processing engine to collect sampled mobile marketing data. Then, we execute a large set of CVR prediction tests, under a two-stage experimental procedure that considers a rolling window evaluation. First, several preprocessing and machine learning combinations are analyzed using preliminary data. Next, the selected combinations are tested on a larger set of unseen datasets. Interesting classification performances were achieved, with some learning models (e.g., XGboost, Logistic Regression) requiring a reduced computational effort, thus showing a potential value for user CVR prediction in this domain.

/pdf/a-comparison-of-data-driven-approaches-for-mobile-marketing-4ed57rjmjk.pdf

A Comparison of Data-Driven Approaches for Mobile Marketing User Conversion Prediction

Categorical Attribute traNsformation Environment (CANE) is a simpler but powerful data categorical preprocessing Python package. The package is valuable since there is currently a large range of Machine Learning (ML) algorithms that can only be trained using numerical data (e.g., Deep Learning, Support Vector Machines) and several real-world ML applications are associated with categorical data attributes. Currently, CANE offers three categorical to numeric transformation methods, namely: Percentage Categorical Pruned (PCP), Inverse Document Frequency (IDF) and a simpler One-Hot-Encoding method. Additionally, the CANE module is well documented with several code examples that can help in its adoption by non expert users. 

http://www.softwareimpacts.com/article/S2665963822000720/pdf

Rui Mendes

Papers

Stochastic barycenters and beta distribution for Gaussian particle swarms

A platform for the selection of genes in DNA microarraydata using evolutionary algorithms

Differential Evolution for the Offline and Online Optimization of Fed-Batch Fermentation Processes

A Comparison of Data-Driven Approaches for Mobile Marketing User Conversion Prediction

Categorical Attribute traNsformation Environment (CANE): A python module for categorical to numeric data preprocessing