Swarm Intelligence: Focus on Ant and Particle Swarm OptimizationFelix Chan, Manoj Tiwari BoD – Books on Demand, 1 Des 2007 - 548 halaman In the era globalisation the emerging technologies are governing engineering industries to a multifaceted state. The escalating complexity has demanded researchers to find the possible ways of easing the solution of the problems. This has motivated the researchers to grasp ideas from the nature and implant it in the engineering sciences. This way of thinking led to emergence of many biologically inspired algorithms that have proven to be efficient in handling the computationally complex problems with competence such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), etc. Motivated by the capability of the biologically inspired algorithms the present book on "Swarm Intelligence: Focus on Ant and Particle Swarm Optimization" aims to present recent developments and applications concerning optimization with swarm intelligence techniques. The papers selected for this book comprise a cross-section of topics that reflect a variety of perspectives and disciplinary backgrounds. In addition to the introduction of new concepts of swarm intelligence, this book also presented some selected representative case studies covering power plant maintenance scheduling; geotechnical engineering; design and machining tolerances; layout problems; manufacturing process plan; job-shop scheduling; structural design; environmental dispatching problems; wireless communication; water distribution systems; multi-plant supply chain; fault diagnosis of airplane engines; and process scheduling. I believe these 27 chapters presented in this book adequately reflect these topics. |
Isi
Integration Method of Ant Colony Algorithm and Rough Set Theory | 15 |
A New Ant Colony Optimization Approach for the DegreeConstrained | 37 |
Robust PSOBased Constrained Optimization by Perturbing the PSO Memory | 57 |
Angel Munoz Zavala Arturo Hernandez Aguirre and Enrique Villa Diharce | 77 |
Differential Metamodel and Particle Swarm Optimization | 101 |
Finite Element Mesh | 145 |
Swarm Intelligence and Image Segmentation | 163 |
Stochastic Metaheuristics | 199 |
Power Plant Maintenance Scheduling Using Ant Colony Optimization | 289 |
Particle Swarm Optimization | 321 |
Selection of Best Alternative Process Plan in Automated Manufacturing | 343 |
Particle Swarm Optimization in Structural Design | 373 |
Ruben E Perez and Kamran Behdinan | 395 |
Hybrid Ant Colony Optimization for | 407 |
A CMPSO Algorithm based | 447 |
Ant Colonies for Performance Optimization | 475 |
New Industrial Applications of Swarm Intelligence Techniques | 235 |
Ali T AlAwami Mohammed A Abido and Youssef L AbdelMagid | 263 |
Distributed Particle Swarm | 505 |
Edisi yang lain - Lihat semua
Istilah dan frasa umum
animats ant colony algorithm Ant Colony Optimization ants applied approach ASelite ASrank Assignment Problem attribute behaviour best solution Blob code cluster centers CMPSO colony algorithm Colony Optimization component constraints convergence cost distribution Dorigo Eberhart edges equation evaluations evolutionary algorithms Evolutionary Computation exploration feasible solution Figure formulation genetic algorithm graph heuristic hybrid improve inertia weight initial Kennedy load local search machine maintenance schedule maintenance tasks mesh metaheuristics method minimal MMAS neural network node number of elements number of iterations objective function obtained operation optimisation optimization algorithm optimization problems optimum Particle Swarm Optimization path pbest performance PESO pheromone trails pheromone update power system process plan proposed Prüfer code PSO algorithm random number randomly Research search space selection simulated annealing solve spanning tree Step stochastic strategy swarm intelligence Table tabu search technique tree code vector velocity vertex
