13,163 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
Multiobjective synchronization of coupled systems
Copyright @ 2011 American Institute of PhysicsSynchronization of coupled chaotic systems has been a subject of great interest and importance, in theory but also various fields of application, such as secure communication and neuroscience. Recently, based on stability theory, synchronization of coupled chaotic systems by designing appropriate coupling has been widely investigated. However, almost all the available results have been focusing on ensuring the synchronization of coupled chaotic systems with as small coupling strengths as possible. In this contribution, we study multiobjective synchronization of coupled chaotic systems by considering two objectives in parallel, i. e., minimizing optimization of coupling strength and convergence speed. The coupling form and coupling strength are optimized by an improved multiobjective evolutionary approach. The constraints on the coupling form are also investigated by formulating the problem into a multiobjective constraint problem. We find that the proposed evolutionary method can outperform conventional adaptive strategy in several respects. The results presented in this paper can be extended into nonlinear time-series analysis, synchronization of complex networks and have various applications
Some improved genetic-algorithms based heuristics for global optimization with innovative applications
The research is a study of the efficiency and robustness of genetic algorithm to instances of both
discrete and continuous global optimization problems. We developed genetic algorithm based
heuristics to find the global minimum to problem instances considered.
In the discrete category, we considered two instances of real-world space allocation problems
that arose from an academic environment in a developing country. These are the university
timetabling problem and hostel space allocation problem. University timetabling represents a
difficult optimization problem and finding a high quality solution is a challenging task. Many
approaches, based on instances from developed countries, have been reported in the literature.
However, most developing countries are yet to appreciate the deployment of heuristics and
metaheuristics in handling the timetabling problem. We therefore worked on an instance from a
university in Nigeria to show the feasibility and efficiency of heuristic method to the timetabling
problem. We adopt a simplified bottom up approach in which timetable are build around
departments. Thus a small portion of real data was used for experimental testing purposes. As
with similar baseline studies in literature, we employ genetic algorithm to solve this instance and
show that efficient solutions that meet stated constraints can be obtained with the metaheuristics.
This thesis further focuses on an instance of university space allocation problem, namely the
hostel space allocation problem. This is a new instance of the space allocation problems that has
not been studied by metaheuristic researchers to the best of our knowledge. The problem aims at
the allocation of categories of students into available hostel space. This must be done without
violating any hard constraints but satisfying as many soft constraints as possible and ensuring
optimum space utilization. We identified some issues in the problem that helped to adapt
metaheuristic approach to solve it. The problem is multi-stage and highly constrained. We first
highlight an initial investigation based on genetic algorithm adapted to find a good solution
within the search space of the hostel space allocation problem. Some ideas are introduced to
increase the overall performance of initial results based on instance of the problem from our case
study. Computational results obtained are reported to demonstrate the effectiveness of the
solution approaches employed.
Sensitivity analysis was conducted on the genetic algorithm for the two SAPs considered to
determine the best parameter values that consistently give good solutions. We noted that the
genetic algorithms perform well specially, when repair strategies are incorporated. This thesis
pioneers the application of metaheuristics to solve the hostel space allocation problem. It
provides a baseline study of the problem based on genetic algorithms with associated test data
sets. We report the best known results for the test instances.
It is a known fact that many real-life problems are formulated as global optimization problems
with continuous variables. On the continuous global optimization category therefore, we focus
on improving the efficiency and reliability of real coded genetic algorithm for solving
unconstrained global optimization, mainly through hybridization with exploratory features.
Hybridization has widely been recognized as one of the most attractive approach to solving
unconstrained global optimization. Literatures have shown that hybridization helps component
heuristics to taking advantage of their individual strengths while avoiding their weaknesses. We
therefore derived three modified forms of real coded genetic algorithm by hybridizing the
standard real-coded genetic algorithm with pattern search and vector projection. These are
combined to form three new algorithms namely, RCGA-PS, RCGA-P, and RCGA-PS-P. The
hybridization strategy used and results obtained are reported and compared with the standard
real-coded genetic algorithm. Experimental studies show that all the modified algorithms
perform better than the original algorithm
Ortalama-varyans portföy optimizasyonunda genetik algoritma uygulamaları ĂŒzerine bir literatĂŒr araĆtırması
Mean-variance portfolio optimization model, introduced by Markowitz, provides a fundamental answer to the problem of portfolio management. This model seeks an efficient frontier with the best trade-offs between two conflicting objectives of maximizing return and minimizing risk. The problem of determining an efficient frontier is known to be NP-hard. Due to the complexity of the problem, genetic algorithms have been widely employed by a growing number of researchers to solve this problem. In this study, a literature review of genetic algorithms implementations on mean-variance portfolio optimization is examined from the recent published literature. Main specifications of the problems studied and the specifications of suggested genetic algorithms have been summarized
Constrained Nonlinear Model Predictive Control of an MMA Polymerization Process via Evolutionary Optimization
In this work, a nonlinear model predictive controller is developed for a
batch polymerization process. The physical model of the process is
parameterized along a desired trajectory resulting in a trajectory linearized
piecewise model (a multiple linear model bank) and the parameters are
identified for an experimental polymerization reactor. Then, a multiple model
adaptive predictive controller is designed for thermal trajectory tracking of
the MMA polymerization. The input control signal to the process is constrained
by the maximum thermal power provided by the heaters. The constrained
optimization in the model predictive controller is solved via genetic
algorithms to minimize a DMC cost function in each sampling interval.Comment: 12 pages, 9 figures, 28 reference
Optimum SHE for cascaded H-bridge multilevel inverters using: NR-GA-PSO, comparative study
Selective Harmonic Elimination (SHE) is very widely applied technique in the control of multilevel inverters that can be used to eliminate the low order dominant harmonics. This is considered a low frequency technique, in which the switching angles are predetermined based on solving a system of transcendental equations. Iterative techniques such as NR and Heuristic techniques such as GA and PSO have been used widely in literatures for the problem of SHE. This paper presents a detailed comparative study of these three techniques when applied for a 7-level CHB-MLI
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