Titlebook: Swarm Intelligence Based Optimization; First International Patrick Siarry,Lhassane Idoumghar,Julien Lepagnot Conference proceedings 2014 S

從容

Metaheuristics for Solving a Hybrid Flexible Flowshop Problem with Sequence-Dependent Setup Times,eriments were performed to compare the performance of the proposed algorithms on different benchmarks from the literature. The algorithms are compared with the best algorithms from the literature. The results indicate that our algorithms generate better solutions than those of the known reference sets.

顯赫的人

安定

Bumptious

Multiple Mobile Target Tracking in Wireless Sensor Networks, process a . according to the intersection points between the trajectories and the sensing ranges of the sensors. The obtained sets of sensors for each time window help us to create . models. These basic problems offer perspectives in performance evaluation of energy-conservation protocols and distributed algorithms in wireless sensor networks.

BOOST

Obvious

Robust Multi-agent Patrolling Strategies Using Reinforcement Learning,ology and number of agents). Moreover, it is observed that such an RL approach is robust as it can efficiently cope with most of the situations caused by the removal of agents during a patrolling simulation.

輕信

Combining PSO and FCM for Dynamic Fuzzy Clustering Problems,FCM) clustering method to find the number of clusters and cluster centers concurrently. Fuzzy c-means can be applied to data clustering problems but the number of clusters must be given in advance. This paper tries to overcome this shortcoming. In the evolutionary process of PSOFC, a discrete PSO is

hereditary

輕觸

Using Particle Swarm Optimization Method to Invert Active Surface Waves,ved directly, requiring an optimization technique to find the most probable solution in a pool of infinite candidates. With the development of data optimization methods, fast and easier approaches can be conducted for inversion of geophysical data. This study proposes Particle Swarm Optimization (PS

NEXUS

A Fuzzy-Controlled Comprehensive Learning Particle Swarm Optimizer, CLPSO (FC-CLPSO), uses a fuzzy controller to tune the probability learning, inertia weight and acceleration coefficient of each particle in the swarm. The FC-CLPSO is compared with CLPSO and SPSO2011 on 11 benchmark functions. The results show that FC-CLPSO generally outperformed CLPSO and SPSO2011