Most real-world optimization problems include multiple objective functions and several constraints. The Differential Evolution algorithm was initially developed for unconstrained single-objective problems and was shown to be a fast, simple algorithm. In order to utilize these advantages in real-world problems it was adapted for multi-objective optimization recently. In this work a multi-objecti...

this paper presents a new meta-heuristic solution to find the efficient frontier using the mean-variance approach. portfolio optimization problem is a quadratic programming model and, changes to np-hard if the number of assets and constraints has increased, and it cannot be solved using common mathematical methods in a reasonable time. therefore, a heuristic or meta-heuristic algorithm should b...

in this work, harmony search meta-heuristic optimization algorithm has been developed for aerodynamic shape optimization problem for the first time. the aerodynamic shape, which has been investigated, is an airfoil with parces method for its shape parameterization. the problem was minimization of aerodynamic objective function using inverse design method and the objective function has been the ...

Human being is the most intelligent animal in this world. Intuitively, optimization algorithm inspired by human being creative problem solving process should be superior to the optimization algorithms inspired by collective behavior of insects like ants, bee, etc. In this paper, we introduce a novel brain storm optimization algorithm, which was inspired by the human brainstorming process. Two b...

This Chapter summarizes some of the well known stigmergic computational techniques inspired by nature, mainly for optimization problems developed by mimicking social insects’ behavior. Some facts about social insects namely ants, bees and termites are presented with an emphasis on how they could interact and self organize for solving real world problems. We focused on ant colony optimization al...

nowadays, software cost estimation (sce) with machine learning techniques are more performance than other traditional techniques which were based on algorithmic techniques. in this paper, we present a new hybrid model of multi-layer perceptron (mlp) artificial neural network (ann) and ant colony optimization (aco) algorithm for high accuracy in sce called multilayer perceptron ant colony optimi...

augmented downhill simplex method (adsm) is introduced here, that is a heuristic combination of downhill simplex method (dsm) with random search algorithm. in fact, dsm is an interpretable nonlinear local optimization method. however, it is a local exploitation algorithm; so, it can be trapped in a local minimum. in contrast, random search is a global exploration, but less efficient. here, rand...

The quantity of network attacks and the harm from them is constantly increasing, so detection these an urgent task in information security field. In this paper, we investigate approach to building intrusion systems using a classifier based on fuzzy rules. process creating given set input output data can be presented as solution problems clustering, informative features selection, parameters rul...

Ants have always fascinated human beings. What particularly strikes the occasional observer as well as the scientist is the high degree of societal organization that these insects can achieve in spite of very limited individual capabilities (Dorigo et al., 2000). Ants have inspired also a number of optimization algorithms. These algorithms are increasingly successful among researches in compute...