This paper presents an augmented Lagrangian methodology with a stochastic population based algorithm for solving nonlinear constrained global optimization problems. The method approximately solves a sequence of simple bound global optimization subproblems using a fish swarm intelligent algorithm. A stochastic convergence analysis of the fish swarm iterative process is included. Numerical result...

In this paper, a novel technique is developed by hybridizing Bacterial Foraging Optimization technique in conjunction with Particle Swarm Optimization and is applied to the expressions developed by M.Kara to calculate accurately the desired resonant frequency of rectangular microstrip antenna of any dimension and of any substrate thickness. Index Terms Bacterial Foraging Optimization (BFO), Par...

This paper presents four optimization algorithms: Bacterial Foraging Optimization (BFO), Particle Swarm Optimization (PSO), Chaos Particle Swarm Optimization (CPSO) and Bacterial Swarm Optimization (BSO), and a neural network compensator: Resource Allocation Network (RAN) to aircraft automatic landing control design. When wind disturbance is beyond the originally scheduled flight condition, the...

Artificial Bee Colony algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. This paper presents Bee Swarm Optimization intended to introduce chaotic sequences and psychology factor of emotion into the algorithm. We define two emotions Bees could have, positive and negative, and correspond to two reaction to perception respectively. For avoiding premature c...

Particle swarm optimization (PSO) is a novel swarm intelligent algorithm inspired by fish schooling and birds flocking. Due to the complex nature of engineering optimization tasks, the standard version can not always meet the optimization requirements. Therefore, in this paper, a new group decision mechanism is introduced to PSO to enhance the escaping capability from local optimum. Furthermore...

This chapter considers the application of swarm intelligence principles to collective robotics. Our aim is to identify the reasons for the growing interest in the intersection of these two areas, and to evaluate the progress that has been made to date. In the course of this chapter, we will discuss the implications of taking a swarm intelligent approach, and review recent research and applicati...

Rapid advancements of both microsystem technology and multi-agent systems have generated a new discipline, arising from the fusion of microrobotics technologies and of swarm intelligence theories. Microrobotics contributes with new capabilities in manipulating objects in the microscale and in developing miniaturized intelligent machines, while swarm intelligence supplies new algorithms allowing...

For a decade swarm Intelligence, an artificial intelligence discipline, is concerned with the design of intelligent multi-agent systems by taking inspiration from the collective behaviors of social insects and other animal societies. They are characterized by a decentralized way of working that mimics the behavior of the swarm. Swarm Intelligence is a successful paradigm for the algorithm with ...

This paper presents the performance of Integrated Bacterial Foraging Optimization and Particle Swarm Optimization (IBFO_PSO) technique in MANET routing. The BFO is a bioinspired algorithm, which simulates the foraging behavior of bacteria. It is effectively applied in improving the routing performance in MANET. In results, it is proved that the PSO integrated with BFO reduces routing delay, ene...