A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.

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Metaheuristics: From Design to Implementation

An electron density (ED)-based methodology is developed for the automatic identification of intermolecular interactions using pro-molecular density. The expression of the ED gradient in terms of atomic components furnishes the basis for the Independent Gradient Model (IGM). This model leads to a density reference for non interacting atoms/fragments where the atomic densities are added whilst their interaction turns off. Founded on this ED reference function that features an exponential decay also in interference regions, IGM model provides a way to identify and quantify the net ED gradient attenuation due to interactions. Using an intra/inter uncoupling scheme, a descriptor (δginter) is then derived that uniquely defines intermolecular interaction regions. An attractive feature of the IGM methodology is to provide a workflow that automatically generates data composed solely of intermolecular interactions for drawing the corresponding 3D isosurface representations.

/pdf/accurately-extracting-the-signature-of-intermolecular-4xca35fg3p.pdf

Accurately extracting the signature of intermolecular interactions present in the NCI plot of the reduced density gradient versus electron density.

http://www.lcc.uma.es/~eat/pdf/itor2013.pdf

Parallel metaheuristics: recent advances and new trends

Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals. In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization. Ant colony optimization (ACO) takes inspiration from the foraging behavior of some ant species. These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony. Ant colony optimization exploits a similar mechanism for solving optimization problems. From the early nineties, when the first ant colony optimization algorithm was proposed, ACO attracted the attention of increasing numbers of researchers and many successful applications are now available. Moreover, a substantial corpus of theoretical results is becoming available that provides useful guidelines to researchers and practitioners in further applications of ACO. The goal of this article is to introduce ant colony optimization and to survey its most notable applications

Ant Colony Optimization.

Ant colony optimization (ACO) is a well-known swarm intelligence method, inspired in the social behavior of ant colonies for solving optimization problems. When facing large and complex problem instances, parallel computing techniques are usually applied to improve the efficiency, allowing ACO algorithms to achieve high quality results in reasonable execution times, even when tackling hard-to-solve optimization problems. This work introduces a new taxonomy for classifying software-based parallel ACO algorithms and also presents a systematic and comprehensive survey of the current state-of-the-art on parallel ACO implementations. Each parallel model reviewed is categorized in the new taxonomy proposed, and an insight on trends and perspectives in the field of parallel ACO implementations is provided.

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A survey on parallel ant colony optimization

The purpose of this paper is to propose effective parallelization strategies for the Ant Colony Optimization (ACO) metaheuristic on Graphics Processing Units (GPUs). The Max-Min Ant System (MMAS) algorithm augmented with 3-opt local search is used as a framework for the implementation of the parallel ants and multiple ant colonies general parallelization approaches. The four resulting GPU algorithms are extensively evaluated and compared on both speedup and solution quality on a state-of-the-art Fermi GPU architecture. A rigorous effort is made to keep parallel algorithms true to the original MMAS applied to the Traveling Salesman Problem. We report speedups of up to 23.60 with solution quality similar to the original sequential implementation. With the intent of providing a parallelization framework for ACO on GPUs, a comparative experimental study highlights the performance impact of ACO parameters, GPU technical configuration, memory structures and parallelization granularity.

Parallel Ant Colony Optimization on Graphics Processing Units.

https://isiarticles.com/bundles/Article/pre/pdf/7840.pdf

Parallel Ant Colony Optimization on Graphics Processing Units

This paper presents a parallel implementation of an ant colony optimization metaheuristic for the solution of an industrial scheduling problem in an aluminum casting center. The usefulness and efficiency of the algorithm, in its sequential form, to solve that particular optimization problem has already been shown in previous work. However, even if this method, as well as metaheuristics in general, offers good quality of solution, it still needs considerable computational time and resources. Moreover, the structure of the algorithm makes it well suited for parallelization, so a considerable improvement can be achieved that way. In this paper an efficient and straightforward OpenMP implementation on a shared memory architecture is presented and the main algorithmic and programming issues that had to be adressed are discussed. The code is written in C and the application has been executed on a Silicon Graphics Origin2000 parallel machine.

/pdf/parallel-implementation-of-an-ant-colony-optimization-x33au2755o.pdf

Parallel implementation of an ant colony optimization metaheuristic with openmp

This paper focus on multiple CNN-based (Convolutional Neural Network) models for COVID-19 forecast developed by our research team during the first French lockdown. In an effort to understand and predict both the epidemic evolution and the impacts of this disease, we conceived models for multiple indicators: daily or cumulative confirmed cases, hospitalizations, hospitalizations with artificial ventilation, recoveries, and deaths. In spite of the limited data available when the lockdown was declared, we achieved good short-term performances at the national level with a classical CNN for hospitalizations, leading to its integration into a hospitalizations surveillance tool after the lockdown ended. Also, A Temporal Convolutional Network with quantile regression successfully predicted multiple COVID-19 indicators at the national level by using data available at different scales (worldwide, national, regional). The accuracy of the regional predictions was improved by using a hierarchical pre-training scheme, and an efficient parallel implementation allows for quick training of multiple regional models. The resulting set of models represent a powerful tool for short-term COVID-19 forecasting at different geographical scales, complementing the toolboxes used by health organizations in France.

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Convolutional neural networks and temporal CNNs for COVID-19 forecasting in France

We present a shared memory approach to the parallelization of the Ant Colony Optimization (ACO) metaheuristic and a performance comparison with an existing message passing implementation. Our aim is to show that the shared memory approach is a competitive strategy for the parallelization of ACO algorithms. The sequential ACO algorithm on which are based both parallelization schemes is first described, followed by the parallelization strategies themselves. Through experiments, we compare speedup and efficiency measures on four TSP problems varying from 318 to 657 cities. We then discuss factors that explain the difference in performance of the two approaches. Further experiments are presented to show the performance of the shared memory implementation when varying numbers of ants are distributed among the available processors. In this last set of experiments, the solution quality obtained is taken into account when analyzing speedup and efficiency measures.

Michaël Krajecki

Papers

Parallel Ant Colony Optimization on Graphics Processing Units.

Parallel Ant Colony Optimization on Graphics Processing Units

Parallel implementation of an ant colony optimization metaheuristic with openmp

Convolutional neural networks and temporal CNNs for COVID-19 forecasting in France

Comparing parallelization of an ACO: message passing vs. shared memory