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.

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.

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.    

To have both high efficiency and linearity in power amplifiers linearization techniques should be used. One structure with the ability of cancelling the inherent limitations of the LINC linearization techniques is proposed in this article. The gain and phase imbalances in LINC technique has been compensated using Optimization Algorithms. According to the analysis a feedback path has been added in the conventional LINC transmitter that calibrates any phase and gain mismatches adaptively; using Optimization Algorithms. Many simulations have been performed to validate the structure functionality and extracted relations. In this paper M68749 power amplifier is used at 390MHz frequency with output power of 5 watt. Simulations show that power spectral density of proposed architecture has 40dB/Hz improvement in spreading to adjacent channels.