This paper presents a modification of the particle swarm optimization algorithm (PSO) intended to combat the problem of premature convergence observed in many applications of PSO. In the new algorithm, each particle is attracted towards the best previous positions visited by its neighbors, in addition to the other aspects of particle dynamics in PSO. This is accomplished by using the ratio of t...

In order to improve the performance of basic bacterial foraging optimization (BFO) for various global optimization problems, a superior attraction bacterial foraging optimizer (SABFO) is proposed in this paper. In SABFO, a novel movement guiding technique termed as superior attraction strategy is introduced to make use of all bacteria historical experience as potential exemplars to lead individ...

Financial portfolio optimization is a challenging problem. First, the problem is multiobjective (i.e.: minimize risk and maximize profit) and the objective functions are often multimodal and non smooth (e.g.: value at risk). Second, managers have often to face real-world constraints, which are typically non-linear. Hence, conventional optimization techniques, such as quadratic programming, cann...

When Genetic Algorithms (GAs) are employed in multimodal function optimization, engineering and machine learning, identifying multiple peaks and maintaining subpopulations of the search space are two central themes. In this paper, an immune system model is adopted to develop a framework for exploring the role of mate selection in GAs with respect to these two issues. The experimental results re...

This paper describes a method of multimodal language processing that reflects experiences shared by people and robots. Through incremental online optimization in the process of interaction, the user and the robot form mutual beliefs represented by a stochastic model. Based on these mutual beliefs, the robot can interpret even fragmental and ambiguous utterances, and can act and generate utteran...

The work presents a new evolutionary algorithm designed for continuous optimization. The algorithm is based on evolution of probability density functions, which focus on the most promising zones of the domain of each variable. Several mechanisms are included to self-adapt the algorithm to the feature of the problem. By means of an experimental study, we have observed that our algorithm obtains ...