A hybrid metaheuristic based on differential evolution and local search with quadratic interpolation

The use of Local Search technique in combination with other methods is often an effective way for increasing the e ciency of a global optimization algorithm. In this paper we present an hybrid version that integrates Di erential Evolution with Local Search, applying the Quadratic Interpolation formula for determining the neighborhood in which to explore towards better solutions. We present DE+LS(1) in which the closer neighborhood to the best population individual is explored, and DE+LS(2) in which the neigh- borhood of the two best population individuals is examined. The results showed that with DE+LS(2) improvements are not signi cant, but using DE+LS(1) an improvement is achieved, especially for large dimensions, in terms of solutions quality and speed of convergence.Eje: Workshop Agentes y sistemas inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Parameters calibration for parallel differential evolution based on islands

We are studying di erent alternatives to obtain a version of the Di erential Evolution (DE) algorithm that improves the solutions quality properties. One of the parallel alternatives, named Island Model, follows a Master/Worker scheme. With this model, multiple instances of DE are executed in parallel on various computing nodes or islands, each of them considering a di erent population of individuals. Each worker makes the search process, and communicates with each other to exchange information with certain frequency. This model signi cantly promote the exploration of a larger search space, which leads to good solutions quality. The aim of this paper is to analyse the behaviour of this model, when setting each island with di erent input parameters. We apply some input con guration tests for the islands, in order to analyse the impact in the solutions quality and the execution time, taking into account the crossover probability and mutation factor, and the crossing type. These parameters are crucial to guide the search towards certain areas of the search space.WPDP- XIII Workshop procesamiento distribuido y paraleloRed de Universidades con Carreras en Informática (RedUNCI

Minimum Population Search, an Application to Molecular Docking

Computer modeling of protein-ligand interactions is one of the most important phases in a drug design process. Part of the process involves the optimization of highly multi-modal objective (scoring) functions. This research presents the Minimum Population Search heuristic as an alternative for solving these global unconstrained optimization problems. To determine the effectiveness of Minimum Population Search, a comparison with seven state-of-the-art search heuristics is performed. Being specifically designed for the optimization of large scale multi-modal problems, Minimum Population Search achieves excellent results on all of the tested complexes, especially when the amount of available function evaluations is strongly reduced. A first step is also made toward the design of hybrid algorithms based on the exploratory power of Minimum Population Search. Computational results show that hybridization leads to a further improvement in performance

A Survey on Adaptation Strategies for Mutation and Crossover Rates of Differential Evolution Algorithm

Differential Evolution (DE), the well-known optimization algorithm, is a tool under the roof of Evolutionary Algorithms (EAs) for solving non-linear and non-differential optimization problems. DE has many qualities in its hand, which are attributing to its popularity. DE also is known for its simplicity in solving the given problem with few control parameters: the population size (NP), the mutation rate (F) and the crossover rate (Cr). To avoid the difficulty involved in setting of suitable values for NP, F and Cr many parameter adaptation strategies are proposed in the literature. This paper is to present the working principle of the parameter adaptation strategies of F and Cr. The adaptation strategies are categorized based on the logic used by the authors, and clear insights about all the categories are presented

Optimization of High-Dimensional Functions through Hypercube Evaluation

A novel learning algorithm for solving global numerical optimization problems is proposed. The proposed learning algorithm is intense stochastic search method which is based on evaluation and optimization of a hypercube and is called the hypercube optimization (HO) algorithm. The HO algorithm comprises the initialization and evaluation process, displacement-shrink process, and searching space process. The initialization and evaluation process initializes initial solution and evaluates the solutions in given hypercube. The displacement-shrink process determines displacement and evaluates objective functions using new points, and the search area process determines next hypercube using certain rules and evaluates the new solutions. The algorithms for these processes have been designed and presented in the paper. The designed HO algorithm is tested on specific benchmark functions. The simulations of HO algorithm have been performed for optimization of functions of 1000-, 5000-, or even 10000 dimensions. The comparative simulation results with other approaches demonstrate that the proposed algorithm is a potential candidate for optimization of both low and high dimensional functions

Monitoring Reservoir Storage over South Asian International River Basins using Remote Sensing

Satellite remote sensing has offered a unique promise for monitoring reservoir storage variations at near real-time. Such information is essential for flood mitigation—especially for regions dominated by international river basins like South Asia. In this dissertation, by using multi-satellite remote sensing observations, a series of algorithms was developed to improve the capability of monitoring reservoir storage variations at high temporal resolution and improved spatial coverage. These algorithms are presented in three studies. The goal of the first study is to generate a first of its kind remotely sensed reservoir storage dataset for South Asia. Reservoir storage variations were inferred by combining water surface area (obtained by classifying optical satellite images) and elevation measurements (obtained from satellite laser altimeter measurements). This resulted in a 13-year dataset containing estimations for a total of 21 South Asian reservoirs, which represents 28% of the integrated reservoir capacity in the region. The storage estimates were highly correlated with observations—the coefficients of determination (R2) were larger than 0.81, with a normalized root mean square error (NRMSE) ranging from 9.51% to 25.20%. The second study explores the solution towards monitoring reservoirs at a high temporal resolution under all-weather conditions. Because optical satellite images suffer from cloud contamination during the rainy season, the developed remote sensing reservoir dataset can be restrained from providing the critical information necessary for flood mitigation. A novel algorithm was developed by fusing passive microwave observations with optical satellite observations. This new algorithm has the advantage of working under all-weather conditions, and it reduces the reservoir monitoring time intervals from 10+ days to 4 days. The third study further extends the spatial representation of the remotely sensed reservoirs from the first algorithm. Although the laser altimeter measurements are able to capture many reservoirs undetectable by traditional radar altimeters, they are still insufficient to form a dense observation network at a basin scale. In order to extend the spatial coverage, a new algorithm was developed to estimate reservoir storage by using water surface area values from MODIS imageries and surface elevation values from Digital Elevation Model (DEM) data (collected by the Shuttle Radar Topography Mission; SRTM). Using the SRTM based method, the spatial coverage of the South Asian reservoir dataset is extended from 28% to 45% of the overall storage capacity in South Asia