many real world problems can be modelled as an optimization problem. evolutionary algorithms are used to solve these problems. ant colony algorithm is a class of evolutionary algorithms that have been inspired of some specific ants looking for food in the nature. these ants leave trail pheromone on the ground to mark good ways that can be followed by other members of the group. ant colony optim...

in this paper, trajectory generation for the 4 dof arm of surena iii humanoid robot with the purpose of optimizing energy and avoiding a moving obstacle is presented. for this purpose, first, kinematic equations for a seven dof manipulator are derived. then, using the lagrange method, an explicit dynamics model for the arm is developed. in the next step, in order to generate the desired traject...

in this paper, genetic algorithms (gas) are employed to control simultaneous linear systems in both state and output feedback. first, the similarity transformation is applied to obtain parameterized controllers. this requires solution of a system of equations and also some non-linear inequalities. gas are used to solve these equations and inequalities. therefore, the paper presents an analytica...

Deep learning is one of the subsets of machine learning that is widely used in Artificial Intelligence (AI) field such as natural language processing and machine vision. The learning algorithms require optimization in multiple aspects. Generally, model-based inferences need to solve an optimized problem. In deep learning, the most important problem that can be solved by optimization is neural n...

Deep learning is one of the subsets of machine learning that is widely used in Artificial Intelligence (AI) field such as natural language processing and machine vision. The learning algorithms require optimization in multiple aspects. Generally, model-based inferences need to solve an optimized problem. In deep learning, the most important problem that can be solved by optimization is neural n...

In the last decades, many efforts have been made to solve multimodal optimization problems using Particle Swarm Optimization (PSO). To produce good results, these PSO algorithms need to specify some niching parameters to define the local neighborhood. In this paper, our motivation is to propose the novel neighborhood structures that remove undesirable niching parameters without sacrificing perf...

e-learning model is examined of three major dimensions. and each dimension has a range of indicators that is effective in optimization and modeling, in many optimization problems in the modeling, target function or constraints may change over time that as a result optimization of these problems can also be changed. if any of these undetermined events be considered in the optimization process, t...

Based on introducing two optimization algorithms, group search optimization (GSO) algorithm and particle swarm optimization (PSO) algorithm, a new hybrid optimization algorithm which named particle swarm-group search optimization (PS-GSO) algorithm is presented and its application to optimal structural design is analyzed. The PS-GSO is used to investigate the spatial truss structures with discr...