Publish in OALib Journal
APC: Only $99
Winner determination is one of the main challenges in combinatorial auctions. However, not much work has been done to solve this problem in the case of reverse auctions using evolutionary techniques. This has motivated us to propose an improvement of a genetic algorithm based method, we have previously proposed, to address two important issues in the context of combinatorial reverse auctions: determining the winner(s) in a reasonable processing time, and reducing the procurement cost. In order to evaluate the performance of our proposed method in practice, we conduct several experiments on combinatorial reverse auctions instances. The results we report in this paper clearly demonstrate the efficiency of our new method in terms of processing time and procurement cost.
The optimizations of constrained layered damped (CLD) laminated structures are discussed in this study. Genetic algorithms (GAs) are employed as the search tool for optimization because these algorithms are suitable for solving optimization problems involving multiple discrete variable combinations. The numerical computation packages, ANSYS and MATLAB, have been used to estimate the optimum stacking sequence of CLD laminated structures. MATLAB package is used to achieve GAs process, and ANSYS package is used to proceed the structural analysis. This study successfully developed a numerical simulation mechanism for optimizing CLD adhesion efficiency by implementing GAs and the finite element method. The loss coefficients of the CLD damping layer vary with vibration frequency and failure constraints of CLD laminated plates are considered in objective function. In addition, the modified plasticity analysis (MPA) is used to increase the search efficiency of GAs and simply plastic analysis.
is widely used to increase oil well production and to reduce formation damage.
Reservoir studies and engineering analyses are carried out to select the wells
for this kind of operation. As the reservoir parameters have some diffuse
characteristics, Fuzzy Inference Systems (FIS) have been tested for these
selection processes in the last few years. This paper compares the performance
of a neuro fuzzy system and a genetic fuzzy system used for selecting wells for
hydraulic fracturing, with knowledge acquired from an operational data base
to set the SIF membership functions. The training data and the validation
data used were the same for both systems. We concluded that, despite the
genetic fuzzy system being a newer process, it obtained better results than
the neuro fuzzy system. Another conclusion was that, as the genetic fuzzy
system can work with constraints, the membership functions setting kept the
consistency of variable linguistic values.
In this paper we
present a new optimization algorithm, and the proposed algorithm operates in
two phases. In the first one, multiobjective version of genetic algorithm is
used as search engine in order to generate approximate true Pareto front. This
algorithm is based on concept of co-evolution and repair algorithm for
handling nonlinear constraints. Also it maintains a finite-sized archive of
non-dominated solutions which gets iteratively updated in the presence of new
solutions based on the concept e-dominance. Then,
in the second stage, rough set theory is adopted as local search engine in
order to improve the spread of the solutions found so far. The results, provided
by the proposed algorithm for benchmark problems, are promising when compared
with exiting well-known algorithms. Also, our results suggest that our
algorithm is better applicable for solving real-world application problems.
We have developed a
genetic algorithm approach for automatically generating expert advisors,
computer programs that trade automatically in the financial markets. Our
system, known as GenFx or Genetic Forex, evaluates evolutionarily generated
expert advisors strategies using predetermined fitness functions to
automatically prioritize parents for breeding. GenFx simulates several key
factors in natural selection. It employs a multiple generation breeding
population, a notion of gender, and the concept of aging to maintain diversity
while providing many breeding opportunities to highly successful offspring. The
approach is also especially efficient running in a multiple processor, multiple
selection-strategy mode using multiple settings. We found out that a
multi-processor gender-based running of the system outperformed all single runs
of the system. This system is inspired by GenShade, a previous system that we
have developed for evolutionary generating procedural textures. The methods
described in this paper are not limited to the Forex market or financial
problems only but are applicable to many other fields.