13,083 research outputs found
Multiobjective genetic algorithm strategies for electricity production from generation IV nuclear technology
Development of a technico-economic optimization strategy of cogeneration systems of electricity/hydrogen, consists in finding an optimal efficiency of the generating cycle and heat delivery system, maximizing the energy production and minimizing the production costs. The first part of the paper is related to the development of a multiobjective optimization library (MULTIGEN) to tackle all types of problems arising from cogeneration. After a literature review for identifying the most efficient methods, the MULTIGEN library is described, and the innovative points are listed. A new stopping criterion, based on the stagnation of the Pareto front, may lead to significant decrease of computational times, particularly in the case of problems involving only integer variables. Two practical examples are presented in the last section. The former is devoted to a bicriteria optimization of both exergy destruction and total cost of the plant, for a generating cycle coupled with a Very High Temperature Reactor (VHTR). The second example consists in designing the heat exchanger of the generating turbomachine. Three criteria are optimized: the exchange surface, the exergy destruction and the number of exchange modules
The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms
open access articleWe present Stochastic Optimisation Software (SOS), a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. SOS reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms and testbed problems, collecting and processing data to display results, measuring algorithmic overhead, etc. SOS provides numerous off-the-shelf methods including: (1) customised implementations of statistical tests, such as the Wilcoxon rank-sum test and the HolmâBonferroni procedure, for comparing the performances of optimisation algorithms and automatically generating result tables in PDF and formats; (2) the implementation of an original advanced statistical routine for accurately comparing couples of stochastic optimisation algorithms; (3) the implementation of a novel testbed suite for continuous optimisation, derived from the IEEE CEC 2014 benchmark, allowing for controlled activation of the rotation on each testbed function. Moreover, we briefly comment on the current state of the literature in stochastic optimisation and highlight similarities shared by modern metaheuristics inspired by nature. We argue that the vast majority of these algorithms are simply a reformulation of the same methods and that metaheuristics for optimisation should be simply treated as stochastic processes with less emphasis on the inspiring metaphor behind them
High-Level Object Oriented Genetic Programming in Logistic Warehouse Optimization
DisertaÄnĂ prĂĄce je zamÄĹena na optimalizaci prĹŻbÄhu pracovnĂch operacĂ v logistickĂ˝ch skladech a distribuÄnĂch centrech. HlavnĂm cĂlem je optimalizovat procesy plĂĄnovĂĄnĂ, rozvrhovĂĄnĂ a odbavovĂĄnĂ. JelikoĹž jde o problĂŠm patĹĂcĂ do tĹĂdy sloĹžitosti NP-teĹžkĂ˝, je vĂ˝poÄetnÄ velmi nĂĄroÄnĂŠ nalĂŠzt optimĂĄlnĂ ĹeĹĄenĂ. MotivacĂ pro ĹeĹĄenĂ tĂŠto prĂĄce je vyplnÄnĂ pomyslnĂŠ mezery mezi metodami zkoumanĂ˝mi na vÄdeckĂŠ a akademickĂŠ pĹŻdÄ a metodami pouĹžĂvanĂ˝mi v produkÄnĂch komerÄnĂch prostĹedĂch. JĂĄdro optimalizaÄnĂho algoritmu je zaloĹženo na zĂĄkladÄ genetickĂŠho programovĂĄnĂ ĹĂzenĂŠho bezkontextovou gramatikou. HlavnĂm pĹĂnosem tĂŠto prĂĄce je a) navrhnout novĂ˝ optimalizaÄnĂ algoritmus, kterĂ˝ respektuje nĂĄsledujĂcĂ optimalizaÄnĂ podmĂnky: celkovĂ˝ Äas zpracovĂĄnĂ, vyuĹžitĂ zdrojĹŻ, a zahlcenĂ skladovĂ˝ch uliÄek, kterĂŠ mĹŻĹže nastat bÄhem zpracovĂĄnĂ ĂşkolĹŻ, b) analyzovat historickĂĄ data z provozu skladu a vyvinout sadu testovacĂch pĹĂkladĹŻ, kterĂŠ mohou slouĹžit jako referenÄnĂ vĂ˝sledky pro dalĹĄĂ vĂ˝zkum, a dĂĄle c) pokusit se pĹedÄit stanovenĂŠ referenÄnĂ vĂ˝sledky dosaĹženĂŠ kvalifikovanĂ˝m a trĂŠnovanĂ˝m operaÄnĂm manaĹžerem jednoho z nejvÄtĹĄĂch skladĹŻ ve stĹednĂ EvropÄ.This work is focused on the work-flow optimization in logistic warehouses and distribution centers. The main aim is to optimize process planning, scheduling, and dispatching. The problem is quite accented in recent years. The problem is of NP hard class of problems and where is very computationally demanding to find an optimal solution. The main motivation for solving this problem is to fill the gap between the new optimization methods developed by researchers in academic world and the methods used in business world. The core of the optimization algorithm is built on the genetic programming driven by the context-free grammar. The main contribution of the thesis is a) to propose a new optimization algorithm which respects the makespan, the utilization, and the congestions of aisles which may occur, b) to analyze historical operational data from warehouse and to develop the set of benchmarks which could serve as the reference baseline results for further research, and c) to try outperform the baseline results set by the skilled and trained operational manager of the one of the biggest warehouses in the middle Europe.
Grammar-based Representation and Identification of Dynamical Systems
In this paper we propose a novel approach to identify dynamical systems. The
method estimates the model structure and the parameters of the model
simultaneously, automating the critical decisions involved in identification
such as model structure and complexity selection. In order to solve the
combined model structure and model parameter estimation problem, a new
representation of dynamical systems is proposed. The proposed representation is
based on Tree Adjoining Grammar, a formalism that was developed from linguistic
considerations. Using the proposed representation, the identification problem
can be interpreted as a multi-objective optimization problem and we propose a
Evolutionary Algorithm-based approach to solve the problem. A benchmark example
is used to demonstrate the proposed approach. The results were found to be
comparable to that obtained by state-of-the-art non-linear system
identification methods, without making use of knowledge of the system
description.Comment: Submitted to European Control Conference (ECC) 201
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