There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Data Mining - Concepts and Techniques.

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A Break in the Clouds: Towards a Cloud Definition

The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. Many problems from various areas have been successfully solved using the firefly algorithm and its variants. In order to use the algorithm to solve diverse problems, the original firefly algorithm needs to be modified or hybridized. This paper carries out a comprehensive review of this living and evolving discipline of Swarm Intelligence, in order to show that the firefly algorithm could be applied to every problem arising in practice. On the other hand, it encourages new researchers and algorithm developers to use this simple and yet very efficient algorithm for problem solving. It often guarantees that the obtained results will meet the expectations.

A comprehensive review of firefly algorithms

Swarm Intelligence (SI) is an innovative distributed intelligent paradigm for solving optimization problems that originally took its inspiration from the biological examples by swarming, flocking and herding phenomena in vertebrates. Particle Swarm Optimization (PSO) incorporates swarming behaviors observed in flocks of birds, schools of fish, or swarms of bees, and even human social behavior, from which the idea is emerged [14, 7, 22]. PSO is a population-based optimization tool, which could be implemented and applied easily to solve various function optimization problems, or the problems that can be transformed to function optimization problems. As an algorithm, the main strength of PSO is its fast convergence, which compares favorably with many global optimization algorithms like Genetic Algorithms (GA) [13], Simulated Annealing (SA) [20, 27] and other global optimization algorithms. For applying PSO successfully, one of the key issues is finding how to map the problem solution into the PSO particle, which directly affects its feasibility and performance. Ant Colony Optimization (ACO) deals with artificial systems that is inspired from the foraging behavior of real ants, which are used to solve discrete

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Swarm Intelligence: Foundations, Perspectives and Applications

Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm

This brief considers the problem of trajectory tracking control for marine surface vessels with unknown time-variant environmental disturbances. The adopted mathematical model of the surface ship movement includes the Coriolis and centripetal matrix and the nonlinear damping terms. An observer is constructed to provide an estimation of unknown disturbances and is applied to design a novel trajectory tracking robust controller through a vectorial backstepping technique. It is proved that the designed tracking controller can force the ship to track the arbitrary reference trajectory and guarantee that all the signals of the closed-loop trajectory tracking control system of ships are globally uniformly ultimately bounded. The simulation results and comparisons illustrate the effectiveness of the proposed controller and its robustness to external disturbances.

A Trajectory Tracking Robust Controller of Surface Vessels With Disturbance Uncertainties

This paper develops an adaptive robust output feedback control scheme for dynamically positioned ships with unavailable velocities and unknown dynamic parameters in an unknown time-variant disturbance environment. The controller is designed by incorporating the high-gain observer and radial basis function (RBF) neural networks in vectorial backstepping method. The high-gain observer provides the estimations of the ship position and heading as well as velocities. The RBF neural networks are employed to compensate for the uncertainties of ship dynamics. The adaptive laws incorporating a leakage term are designed to estimate the weights of RBF neural networks and the bounds of unknown time-variant environmental disturbances. In contrast to the existing results of dynamic positioning (DP) controllers, the proposed control scheme relies only on the ship position and heading measurements and does not require a priori knowledge of the ship dynamics and external disturbances. By means of Lyapunov functions, it is theoretically proved that our output feedback controller can control a ship’s position and heading to the arbitrarily small neighborhood of the desired target values while guaranteeing that all signals in the closed-loop DP control system are uniformly ultimately bounded. Finally, simulations involving two ships are carried out, and simulation results demonstrate the effectiveness of the proposed control scheme.

Adaptive Robust Output Feedback Control for a Marine Dynamic Positioning System Based on a High-Gain Observer

In this paper, we introduce turbulence in the particle swarm optimization (TPSO) and illustrate how this approach could be used for function optimization problems involving high dimensions. The proposed algorithm uses a minimum velocity threshold to control the velocity of particles. TPSO mechanism is similar to a turbulent pump, which supplies some power to the swarm system to explore new search spaces (better solutions). The minimum velocity threshold of the particles is tuned adoptively by using a fuzzy logic controller, which is further called as fuzzy adaptive TPSO (FATPSO). We evaluate and compare the performance of SPSO (Standard PSO), TPSO and FATPSO. Empirical results clearly demonstrate that the performance of SPSO degrades remarkably with the increase in the dimensions of the problem, while the influence is very little in the case of TPSO and FATPSO.

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Hongbo Liu

Papers

Swarm Intelligence: Foundations, Perspectives and Applications

Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm

A Trajectory Tracking Robust Controller of Surface Vessels With Disturbance Uncertainties

Adaptive Robust Output Feedback Control for a Marine Dynamic Positioning System Based on a High-Gain Observer

Fuzzy adaptive turbulent particle swarm optimization