KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース)

Particle swarm optimization (PSO) is a population based meta-heuristic search algorithm that has been widely applied to a variety of problems since its advent. In PSO, the inertial weight not only has a crucial effect on its convergence, but also plays an important role in balancing exploration and exploitation during the evolution. However, PSO is easily trapped into the local optima and premature convergence appears when applied to complex multimodal problems. To address these issues, we present a modified particle swarm optimization with chaos-based initialization and robust update mechanisms. On the one side, the Logistic map is utilized to generate uniformly distributed particles to improve the quality of the initial population. On the other side, the sigmoid-like inertia weight is formulated to make the PSO adaptively adopt the inertia weight between linearly decreasing and nonlinearly decreasing strategies in order to achieve better tradeoff between the exploration and exploitation. During this process, a maximal focus distance is formulated to measure the particle's aggregation degree. At the same time, the wavelet mutation is applied for the particles whose fitness value is less than that of the average so as to enhance the swarm diversity. In addition, an auxiliary velocity-position update mechanism is exclusively applied to the global best particle that can effectively guarantee the convergence of MPSO. Extensive experiments on CEC′13/15 test suites and in the task of standard image segmentation validate the effectiveness and efficiency of the MPSO algorithm proposed in this paper.

MPSO: Modified particle swarm optimization and its applications

Thank you very much for reading swarm intelligence from natural to artificial systems. As you may know, people have search hundreds times for their chosen books like this swarm intelligence from natural to artificial systems, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they juggled with some infectious bugs inside their computer.

Swarm Intelligence From Natural To Artificial Systems

To overcome the shortcoming of poor diversity of Artificial Bee Colony(ABC)algorithm,this paper presents a modified Artificial Bee Colony algorithm.Unlike ABC in which onlooker bees choose employed bees from the colony according to proportional fitness choosing strategy,in MABC,in order to decrease the selection pressure and further improve the diversity,it does not set onlooker bees,as a result of no selection pressure on employed bees(solutions).The simulation results show that the presented algorithm can ensure the colony diversity and improve the performances of ABC.

A Modified Artificial Bee Colony Algorithm

Sumitra Mukhopadhyay

Papers

Structural analysis on spinel (MgAl2O4) for application in spinel-bonded castables

Development of mullite and spinel coatings on graphite for improved water-wettability and oxidation resistance

Significant improvement of refractoriness of Al2O3–C castables containing calcium aluminate nano-coatings on graphite

Nanostructured cementitious sol gel coating on graphite for application in monolithic refractory composites

Improvement of corrosion resistance of spinel-bonded castables to converter slag