In this paper a novel non-parametric approach for designing geodetic networks based on  evolutionary ALGORITHMS is proposed. Since conventional analytical OPTIMIZATION methods are based on gradient approaches, they have some drawbacks including approximate parameters requirement and inability to work with discrete objective functions and/or constraints e.g. visibility matter. To prevent from above problems, the standard genetic algorithm method is utilized. Primary tests demonstrate the capability of proposed method to design the optimal network without approximate values and with visibility constraint satisfaction.    

In this paper, ant colony OPTIMIZATION (ACO) ALGORITHMS are proposed for reservoir operation. Through a collection of cooperative agents called ants, the near optimum solution to the reservoir operation can be effectively achieved. To apply ACO ALGORITHMS, the problem is approached by considering a finite horizon with a time series of inflow, classifying the reservoir volume to several intervals, and deciding for releases at each period with respect to a predefined optimality criterion. Three alternative formulations of ACO ALGORITHMS for a reservoir operation are presented using a single reservoir, deterministic, finite-horizon problem and applied to the Dez reservoir in Iran. It is concluded that the ant colony system global-best algorithm provides better and more comparable results with known global optimum results. As with any direct search method, the model is quite sensitive to setup parameters, hence fine tuning of the parameters is recommended.

The fundamental concepts of biogeography-based OPTIMIZATION (BBO), a meta-heuristic algorithm, have been inspired by the geographical distribution of animals. This algorithm does not need a starting point, and performs a random search instead of a gradient-based search. In this article, for the first time, the weights of 2D and 3D trusses with specific geometries and different stress and displacement constraints have been optimized by using the BBO approach. Also, in this work, the numerical results achieved by other researchers through various OPTIMIZATION techniques have been compared with the results obtained from the Particle Swarm OPTIMIZATION (PSO), Differential Evolution (DE) and BBO ALGORITHMS. It has been demonstrated that the search and exploration capability of the BBO algorithm is superior to that of the DE and PSO ALGORITHMS, and that it achieves better results than the other OPTIMIZATION techniques considered in this paper. This superiority is due to the excellent exploration capability of the BBO algorithm and the fact that it achieves a favorable optimal solution in the initial iteration.

The use of lasers is being considered as a modern method for forming process in recent years. This method has been used in various industries, such as aerospace, marine and oil industry. Extensive research has been done in the field of modeling and OPTIMIZATION of direct paths parameters with process of laser forming. Although forming in circular paths can be used for producing complex parts, due to some technical reasons, it is considered less. The main purpose of this paper is to detect the proper estimation model and obtain optimal variables conditions for complete circular paths in perforated circular parts by means of genetic ALGORITHMS. In this process the outer edges are fixed and the inner edges are being formed by laser. At first, the finite element simulation model is studied then the estimation model has been discussed, after that multi-objective functions have been examined with the least error and energy. Furthermore, the OPTIMIZATION results of the internal hole diameters are reported and analyzed in terms of Pareto charts. In conclusion, optimum forming conditions have been reported in terms of accuracy and energy for different diameters of holes. This study shows with acceptable increasing in the error rate, the required energy could be reduced. Also, increasing in the diameter of inside hole cause to increase energy and decrease of accuracy.

Investment decision making is one of the key issues in financial management. Selecting the appropriate tools and techniques that can make optimal portfolio is one of the main objectives of the investment world. This study tries to optimize the decision making in stock selection or the OPTIMIZATION of the portfolio by means of the artificial colony of honey bee algorithm. To determine the effectiveness of the algorithm, its sharp criteria was calculated and compared with the portfolio made up of genes and ant colony ALGORITHMS. The sample consisted of active firms listed on the Tehran Stock Exchange from 2005 to 2015. The sample selected by the systematic removal method. The findings show that artificial bee colony algorithm functions better than the genetic and ant colony ALGORITHMS in terms of portfolio formation.