Characterization of the convergence of stationary Fokker-Planck learning

The convergence properties of the stationary Fokker-Planck algorithm for the estimation of the asymptotic density of stochastic search processes is studied. Theoretical and empirical arguments for the characterization of convergence of the estimation in the case of separable and nonseparable nonlinear optimization problems are given. Some implications of the convergence of stationary Fokker-Planck learning for the inference of parameters in artificial neural network models are outlined

A Personalized Rolling Optimal Charging Schedule for Plug-In Hybrid Electric Vehicle Based on Statistical Energy Demand Analysis and Heuristic Algorithm

To alleviate the emission of greenhouse gas and the dependence on fossil fuel, Plug-in Hybrid Electrical Vehicles (PHEVs) have gained an increasing popularity in current decades. Due to the fluctuating electricity prices in the power market, a charging schedule is very influential to driving cost. Although the next-day electricity prices can be obtained in a day-ahead power market, a driving plan is not easily made in advance. Although PHEV owners can input a next-day plan into a charging system, e.g., aggregators, day-ahead, it is a very trivial task to do everyday. Moreover, the driving plan may not be very accurate. To address this problem, in this paper, we analyze energy demands according to a PHEV owner’s historical driving records and build a personalized statistic driving model. Based on the model and the electricity spot prices, a rolling optimization strategy is proposed to help make a charging decision in the current time slot. On one hand, by employing a heuristic algorithm, the schedule is made according to the situations in the following time slots. On the other hand, however, after the current time slot, the schedule will be remade according to the next tens of time slots. Hence, the schedule is made by a dynamic rolling optimization, but it only decides the charging decision in the current time slot. In this way, the fluctuation of electricity prices and driving routine are both involved in the scheduling. Moreover, it is not necessary for PHEV owners to input a day-ahead driving plan. By the optimization simulation, the results demonstrate that the proposed method is feasible to help owners save charging costs and also meet requirements for driving

A genetic algorithm for the one-dimensional cutting stock problem with setups

This paper investigates the one-dimensional cutting stock problem considering two conflicting objective functions: minimization of both the number of objects and the number of different cutting patterns used. A new heuristic method based on the concepts of genetic algorithms is proposed to solve the problem. This heuristic is empirically analyzed by solving randomly generated instances and also practical instances from a chemical-fiber company. The computational results show that the method is efficient and obtains positive results when compared to other methods from the literature. © 2014 Brazilian Operations Research Society

Cooperative co-evolutionary module identification with application to cancer disease module discovery

none10siModule identification or community detection in complex networks has become increasingly important in many scientific fields because it provides insight into the relationship and interaction between network function and topology. In recent years, module identification algorithms based on stochastic optimization algorithms such as evolutionary algorithms have been demonstrated to be superior to other algorithms on small- to medium-scale networks. However, the scalability and resolution limit (RL) problems of these module identification algorithms have not been fully addressed, which impeded their application to real-world networks. This paper proposes a novel module identification algorithm called cooperative co-evolutionary module identification to address these two problems. The proposed algorithm employs a cooperative co-evolutionary framework to handle large-scale networks. We also incorporate a recursive partitioning scheme into the algorithm to effectively address the RL problem. The performance of our algorithm is evaluated on 12 benchmark complex networks. As a medical application, we apply our algorithm to identify disease modules that differentiate low- and high-grade glioma tumors to gain insights into the molecular mechanisms that underpin the progression of glioma. Experimental results show that the proposed algorithm has a very competitive performance compared with other state-of-the-art module identification algorithms.noneHe, S and Jia, G and Zhu, Z and Tennant, DA and Huang, Q and Tang, K and Liu, J and Musolesi, M and Heath, JK and Yao, XHe, S and Jia, G and Zhu, Z and Tennant, DA and Huang, Q and Tang, K and Liu, J and Musolesi, M and Heath, JK and Yao,

A new hybrid evolutionary algorithm for the huge k

In recent years it has been shown that an intelligent combination of metaheuristics with other optimization techniques can significantly improve over the application of a pure metaheuristic. In this paper, we combine the evolutionary computation paradigm with dynamic programming for the application to the NP-hard k-cardinality tree problem. Given an undirected graph G with node and edge weights, this problem consists of finding a tree in G with exactly k edges such that the sum of the weights is minimal. The genetic operators of our algorithm are based on an existing dynamic programming algorithm from the literature for finding optimal subtrees in a given tree. The simulation results show that our algorithm is able to improve the best known results for benchmark problems from the literature in 60 cases

A survey on metaheuristics for stochastic combinatorial optimization

Metaheuristics are general algorithmic frameworks, often nature-inspired, designed to solve complex optimization problems, and they are a growing research area since a few decades. In recent years, metaheuristics are emerging as successful alternatives to more classical approaches also for solving optimization problems that include in their mathematical formulation uncertain, stochastic, and dynamic information. In this paper metaheuristics such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others are introduced, and their applications to the class of Stochastic Combinatorial Optimization Problems (SCOPs) is thoroughly reviewed. Issues common to all metaheuristics, open problems, and possible directions of research are proposed and discussed. In this survey, the reader familiar to metaheuristics finds also pointers to classical algorithmic approaches to optimization under uncertainty, and useful informations to start working on this problem domain, while the reader new to metaheuristics should find a good tutorial in those metaheuristics that are currently being applied to optimization under uncertainty, and motivations for interest in this fiel

Hyperparameters optimization on neural networks for bond trading

Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementArtificial Neural Networks have been recently spotlighted as de facto tools used for classification. Their ability to deal with complex decision boundaries makes them potentially suitable to work on trading within financial markets, namely on Bonds. Such classifier faces high flexibility on its parameters in parallel with great modularity of its techniques, arising thus the need to efficiently optimize its hyperparameters. To determine the most effcient search method to optimize almost the majority of the Neural Networks hyperparameters, we have compared the results obtained by the manual, evolutionary (genetic algorithm) and random search methods. The search methods compete on several metrics from which we aim to estimate the generalization capability, i.e. the capacity to correctly predict on unseen data. We have found the manual method to present better generalization results than the remaining automatic methods. Also, no benefit was found on the direction provided by the genetic search method when compared to the purely random. Such results demonstrate the importance of human oversight during the hyperparameters optimization and weight training phases, capable of analyzing in parallel multiple metrics and data visualization techniques, a process critical to avoid suboptimal solutions when navigating complex hyperspaces

Inteligencia de enjambres: sociedades para la solución de problemas (una revisión)

En este artículo se presenta una revisión de los conceptos de inteligencia de enjambres, y algunas perspectivas en la investigación con estas técnicas, con el objetivo de establecer un punto de partida para trabajos futuros en diferen-tes áreas de la ingeniería. Para la construcción de esta revisión se llevó a cabo una búsqueda bibliográfica en las bases de datos más actualizadas de los artículos clásicos del tema y de las últimas aplicaciones y resultados publi-cados, en particular en las áreas de control automático, procesamiento de señales e imágenes, y robótica, extra-yendo su concepto más relevante y organizándolo de manera cronológica. Como resultado se obtuvo taxonomía de la computación evolutiva, la diferencia entre la inteligencia de enjambres y otros algoritmos evolutivos, y una vi-sión amplia de las diferentes técnicas y aplicaciones.This paper presents a review of the basic concepts of swarm intelligence and some views regarding the future of re-search in this area aimed at establishing a starting point for future work in different engineering fields. A bibliogra-phic search of the most updated databases regarding classic articles on the subject and the most recent applications and results was used for constructing this review, especially in the areas of automatic control, signal and image pro-cessing and robotics. The main concepts were selected and organised in chronological order. A taxonomy was ob-tained for evolutionary computing techniques, a clear differentiation between swarm intelligence and other evolutio-nary algorithms and an overview of the different techniques and applications

Програмна система автоматизації створення оптимізованих додатків користувача для паралельних вбудованих систем

Магістерська дисертація містить 102 сторінки, 75 рисунків, 14 таблиць, 1 додаток, 30 джерел. Об`єкт дослідження: паралельні вбудовані системи. Мета магістерської дисертації: підвищення ефективності оптимізації додатків методом тайліенгу Предмет дослідження: автоматизована система створення оптимізованих додатків користувача для паралельних вбудованих систем. Наукова новизна одержаних у магістерській дисертації результатів полягає у вдосконаленні ефективності оптимізації додатків методом тайліенгу, а саме – у реалізації пошуку оптимальних розмірів тайлів методом генетичного алгоритму.The master's dissertation contains 102 pages, 75 figures, 14 tables, 1 appendix, 30 sources. Object of research: parallel embedded systems. The purpose of the master's dissertation: to increase the efficiency of optimization of applications by tailing Subject of research: automated system for creating optimized user applications for parallel embedded systems. The scientific novelty of the results obtained in the master's dissertation is to improve the efficiency of optimization of applications by tailing, namely - in the implementation of the search for optimal tile sizes by genetic algorithm.

Multi-vehicle Dispatching And Routing With Time Window Constraints And Limited Dock Capacity

The Vehicle Routing Problem with Time Windows (VRPTW) is an important and computationally hard optimization problem frequently encountered in Scheduling and logistics. The Vehicle Routing Problem (VRP) can be described as the problem of designing the most efficient and economical routes from one depot to a set of customers using a limited number of vehicles. This research addresses the VRPTW under the following additional complicating features that are often encountered in practical problems: 1. Customers have strict time windows for receiving a vehicle, i.e., vehicles are not allowed to arrive at the customer’s location earlier than the lower limit of the specified time window, which is relaxed in previous research work. 2. There is a limited number of loading/unloading docks for dispatching/receiving the vehicles at the depot The main goal of this research is to propose a framework for solving the VRPTW with the constraints stated above by generating near-optimal routes for the vehicles so as to minimize the total traveling distance. First, the proposed framework clusters customers into groups based on their proximity to each other. Second, a Probabilistic Route Generation (PRG) algorithm is applied to each cluster to find the best route for visiting customers by each vehicle; multiple routes per vehicle are generated and each route is associated with a set of feasible dispatching times from the depot. Third, an assignment problem formulation determines the best dispatching time and route for each vehicle that minimizes the total traveling distance. iii The proposed algorithm is tested on a set of benchmark problems that were originally developed by Marius M. Solomon and the results indicate that the algorithm works well with about 1.14% average deviation from the best-known solutions. The benchmark problems are then modified by adjusting some of the customer time window limits, and adding the staggered vehicle dispatching constraint. For demonstration purposes, the proposed clustering and PRG algorithms are then applied to the modified benchmark problems