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

Previous studies in Particle Swarm Optimization (PSO) have emphasized the role of population topologies in particle swarms. These studies have shown that a relationship between the way individuals in a population are organized and their aptitude to find global optima exists. A study of what graph statistics are relevant is of paramount importance. This work presents such a study, which will provide guidelines that can be used by researchers in the field of Particle Swarm Optimization (PSO) in particular and in the Evolutionary Computation arena in general.

What Makes a Successful Society

In this work, a Simulated Annealing (SA) algorithm is proposed for a Metabolic Engineering task: the optimization of the set of gene deletions to apply to a microbial strain to achieve a desired production goal. Each mutant strain is evaluated by simulating its phenotype using the Flux-Balance Analysis approach, under the premise that microorganisms have maximized their growth along natural evolution. A set based representation is used in the SA to encode variable sized solutions, enabling the automatic discovery of the ideal number of gene deletions. The approach was compared to the use of Evolutionary Algorithms (EAs) to solve the same task. Two case studies are presented considering the production of succinic and lactic acid as the target, with the bacterium E. coli. The variable sized SA seems to be the best alternative, outperforming the EAs, showing a fast convergence and low variability among the several runs and also enabing the automatic discovery of the ideal number of knockouts.

https://repositorium.sdum.uminho.pt/bitstream/1822/7473/1/Rocha_LNAI2007.pdf

Evaluating simulated annealing algorithms in the optimization of bacterial strains

Mobile marketing is an expanding industry due to the growth of mobile devices (e.g., tablets, smartphones). In this paper, we explore a categorical approach to cluster publishers of a mobile performance market, in which payouts are only issued when there is a conversion (e.g., a sale). As a case study, we analyze recent and real-world data from a global mobile marketing company. Several experiments were held, considering a first internal evaluation stage, using training data, clustering quality metrics and computational effort. In the second stage, the best method, COBWEB algorithm, was analyzed using an external evaluation based on business metrics, computed over test data, and that allowed an identification of interesting clusters.

/pdf/a-categorical-clustering-of-publishers-for-mobile-4ezzyoaale.pdf

A Categorical Clustering of Publishers for Mobile Performance Marketing

Several studies show that biological knowledge is growing at a continuous rate and distributed among different databases, making the process of data integration a hard task to perform, because they have different structures, different ways of storing data and also different approaches to export information, and are usually developed to provide information for a specific organism. Due to the large amount of biological data, the process of data integration has been one of the major challenges in the field of bioinformatics as well as discovering information about Transcriptional Regulatory Networks (TRN). When using a single source, this task is not easy to perform since the source often lacks enough information for the successful completion of the task. Therefore it is necessary to find information in several databases in order to create a useful body of knowledge. This work presents a new approach of integrating data related with TRNs for the Escherichia coli by creating a new integrated data repository gathering information from KEGG, EcoCyc, Regulon and NCBI databases.

/pdf/integrating-biological-databases-in-the-context-of-42nikia22s.pdf

Integrating biological databases in the context of transcriptional regulatory networks

Over the last years, comparative studies of biclustering algorithms have been described in the literature, showing that a variety of programming languages were used in their development. Because of this fact, many researchers have difficulty using some of these methods, since it is necessary to setup an environment for running a given algorithm or to have some programming skills in order to compile it. We present a new Java API for biclustering analysis in the context of gene expression data, allowing the use of 21 biclustering algorithms, in a single application. It is freely available at https://jbiclustge.github.io as an open source framework.

Rui Mendes

Papers

What Makes a Successful Society

Evaluating simulated annealing algorithms in the optimization of bacterial strains

A Categorical Clustering of Publishers for Mobile Performance Marketing

Integrating biological databases in the context of transcriptional regulatory networks

JBiclustGE: Java API with Unified Biclustering Algorithms for Gene Expression Data Analysis