Nowadays, in majority of academic contexts, it has been tried to consider the highest possible level of similarities to the real world. Hence, most of the problems have complicated structures. Traditional methods for solving almost all of the mathematical and optimization problems are inefficient. As a result, meta-heuristic algorithms have been employed increasingly during recent years. In thi...

In this paper, we study various supervised learning methods for training feed-forward neural networks. In general, such learning can be considered as a nonlinear global optimization problem in which the goal is to minimize a nonlinear error function that spans the space of weights using heuristic strategies that look for global optima (in contrast to local optima). We survey various global opti...

Keywords: Extended/enhanced central force optimization (ECFO) Global optimization Convergence analysis Simple central force optimization (SCFO) Gravitational force a b s t r a c t Simple central force optimization (SCFO) algorithm is a novel physically-inspired optimization algorithm as simulating annealing (SA). To enhance the global search ability of SCFO and accelerate its convergence, a nov...

Heuristic optimization provides a robust and efficient approach for extracting approximate solutions of multi-objective problems because of their capability to evolve a set of non-dominated solutions distributed along the Pareto frontier. The convergence rate and suitable diversity of solutions are of great importance for multi-objective evolutionary algorithms. The focu...

no unique method has been so far specified for determining the number of neurons in hidden layers of multi-layer perceptron (mlp) neural networks used for prediction. the present research is intended to optimize the number of neurons using two meta-heuristic procedures namely genetic and hill climbing algorithms. the data used in the present research for prediction are consumption data of water...

Optimization of machining parameters is a vital task for increasing machining efficiency and economics. Selection of optimal machining parameters is often performed by integrating empirical models with traditional mathematical and meta-heuristic algorithms. Ability to deal with complex and multi-dimensional optimization problems resulted in growing interest on the application of meta-heuristic ...

improving mechanical properties of composites based on strength-to-weight ratio has been gained much attention in various applications. having the most resistant and at the same time the lightest and the most economical structure is believed as an aim in these applications. these three factors are usually opposed to each other. so, applying optimization algorithms for improving mechanical prope...

This chapter proposes new heuristic algorithms to solve grid scheduling problem. Two heuristic algorithms, based on Ant Colony Optimization and Particle Swarm Optimization are proposed. The optimization criteria, namely, flowtime and makespan are used to measure the quality of grid scheduling algorithm. Using the simulated benchmark instances, the results of different algorithms are analyzed an...

Today, Robotic is an essential technology from the entertainment to the industry. Thousands of articles have been published on Robotic. There are various types of robots such as parallel robots, industrial robots, mobile robots, autonomous mobile robots, health-care robots, military robots, entertainment robots, nano robots and swarm robots. So, this variety brings a lot of problems in Robotic....

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 effect...