In this paper, it has
merged two techniques of the artificial intelligent, they are the
ants colony optimization algorithm and the genetic algorithm, to
The recurrent reinforcement learning trading system
optimization. The proposed trading system
is based on an ant
colony optimization algorithm and the genetic algorithm to
select an optimal group of technical indicators, and fundamental
indicators.
In this research, we are studying the possibility of contribution
in solving the multi-objective vehicle Routing problem with time
windows , that is one of the optimization problems of the NP-hard
type , This problem has attracted a lot of attenti
on now because of
its real life applications.
Moreover, We will also introduced an algorithm called hybrid
algorithm (HA) which depends on integrates between Multiple
objective ant colony optimisation (MOACO) and tabu search (TS)
algorithm based on the Pareto optimization , and compare the
presented approach is the developer with standard tests to
demonstrate the applicability and efficiency.
In this paper we study some basic properties of the Moreau-Yosida function with two variables , and generalize the results of related to study of the convergence for sequence of convex-concave functions and the sequence of Moreau-Yosida function corr
esponding , and the basic theorem that we proved is : for any sequence of convex-concave functions , if they are convergent of the Moreau-Yosida distance then the sequence of Moreau-Yosida function corresponding will be convergent to the concept of Mosco-epi/hypo graph convergence .
This paper presents an interactive solution method for treating multi objective mathematical programming problems with fuzzy parameters in the objective functions and in the constraints. Theses fuzzy parameters are characterized by fuzzy numbers. For
such problems, the concept of a-Pareto optimality introduced by extending the ordinary Pareto optimality based on the a-level sets of fuzzy numbers. The proposed solution method is based on cutting planes, which are based on local trade off ratios between the objective functions as prescribed by the decision maker at each iterate generated by the method. An illustrative numerical example is given to clarity this method.
A model has been developed for optimal reservoir operation system of Alfatha dam upstream Samerra
Barrage which is considered as a strategic node in the Tigris river system in Iraq. This model is based on
combining an optimization dynamic programmi
ng model with a flood routing simulation model within an
optimal control framework. The predictive reservoir operation method provides optimal time operation of
flood control system with incorporation of current and predicted flood wave. The model utilizes a
dynamic programming with successive algorithm, interacting with hydrologic routing method.
From the developed optimization model connected with the hydrologic routing model analyses of a
number of predicted flood waves of Tigris river released from Mosul dam, Greater Zab and Dokan dam
on the Lesser Zab have been accomplished. It was concluded that the optimal maximum operation water
level in the reservoir of proposed Alfatha dam realized that all the hydraulic topographic and design
constraints were 164 m.a.s.l. for the two depended predicted operation scenarios of the reservoir. While a
maximum water level of 165 m.a.s.l. is the optimal operation level for the worst operation scenario of the
reservoir. This operation scenario is recommended in the future hydraulic design of the Alfatha dam.
