By Lin Zhang, Fei Tao, Yuanjun Laili
Featuring the concept that and layout and implementation of configurable clever optimization algorithms in production platforms, this e-book offers a brand new configuration solution to optimize production techniques. It presents a complete elaboration of simple clever optimization algorithms, and demonstrates how their development, hybridization and parallelization could be utilized to production. additionally, numerous functions of those clever optimization algorithms are exemplified intimately, bankruptcy by means of bankruptcy. The clever optimization set of rules isn't just a unmarried set of rules; in its place it's a basic complex optimization mechanism that's hugely scalable with robustness and randomness. accordingly, this publication demonstrates the flexibleness of those algorithms, in addition to their robustness and reusability so one can resolve mass advanced difficulties in manufacturing.
Since the genetic set of rules used to be offered many years in the past, a number of clever optimization algorithms and their advancements were built. in spite of the fact that, little paintings has been performed to increase their purposes and confirm their competence in fixing complex difficulties in manufacturing.
This booklet will offer a useful source to scholars, researchers, experts and pros attracted to engineering optimization. it is going to even be relatively precious to 3 teams of readers: set of rules novices, optimization engineers and senior set of rules designers. It bargains a close description of clever optimization algorithms to set of rules newcomers; recommends new configurable layout equipment for optimization engineers, and gives destiny developments and demanding situations of the recent configuration mechanism to senior set of rules designers.
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Additional resources for Configurable Intelligent Optimization Algorithm: Design and Practice in Manufacturing (Springer Series in Advanced Manufacturing)
Sample text
Only high efficient control and simulation will guarantee the efficient operation of the whole manufacturing system. It is a kind of system optimization. 1 Introduction 37 System Optimization Control/Simulation Support System Requirement Planning Design Production Marketing Logistics Recycle Product Structure Optimization Maintainance The whole life cycle of manufacturing Process Optimization Fig. 2 Three main optimization objects in manufacturing • Product/element design and analysis: It contains the structure design and modeling and finite element analysis of product.
This algorithm conducts searching according to the pursuit of particles to the best individual in solution space, whose process is simple and easy to implement. PSO has simple parameters without complex adjustments, and its implementation is shown in the following pseudo code: 22 1 Brief History and Overview of Intelligent Optimization Algorithms Begin t := 0; P(t) := InitPopulation(); Evaluate(P(t)); pbest := the best solution founded by each individual gbest := the best solution founded by the whole population V(t) := InitVelocity(); While (stop criteria unsatisfied) V(t + 1) = UpdateVelocity(V(t), p best, gbest ); P(t + 1) = UpdateLocation(P(t), V(t + 1)); Evaluate(P(t + 1)); Update pbest, gbest according to P(t + 1); t = t + 1; End End where t denotes the number of iterations, P(t) is the tth-generation population, pbest and gbest represent the individuals’ current best solution matrix and the global best solution, respectively.
Therefore, the variables of combinatorial scheduling are generally integer. The targets are minimizing the task execution time and energy consumption in global or local workflow, and maximizing the quality (such as maintenance and reliability) and the efficiency of production or calculation. The constraints usually are the limits of resource capability, task size and other QoS indexes. The model is simple, while the solution space is typically huge. In addition, the variances and restrictions of variables are complex, which make the feasible solution space more narrow, so as to make the optimization harder.