this study is investigated the optimum parameters for a tuned mass damper (tmd) under the seismic excitation. shuffled complex evolution (sce) is a meta-heuristic optimization method which is used to find the optimum damping and tuning frequency ratio for a tmd. the efficiency of the tmd is evaluated by decreasing the structural displacement dynamic magnification factor (ddmf) and acceleration ...

response surface methodology is a common tool in optimizing processes. it mainly concerns situations when there is only one response of interest. however, many designed experiments often involve simultaneous optimization of several quality characteristics. this is called a multiresponse surface optimization problem. a common approach in dealing with these problems is to apply desirability funct...

e-learning model is examined of three major dimensions. and each dimension has a range of indicators that is effective in optimization and modeling, in many optimization problems in the modeling, target function or constraints may change over time that as a result optimization of these problems can also be changed. if any of these undetermined events be considered in the optimization process, t...

We modify an artificial bee colony algorithm to (i) make the population size grow over time, and to (ii) apply local search on strategically selected solutions. The modified algorithm obtains very good results on a set of large-scale continuous optimization problems. This is not the first time we see that these algorithmic components make an initially non-competitive algorithm produce state-of-...

this paper presents a relatively new management model for the optimal design and operation of irrigation water pumping systems. the model makes use of the newly introduced particle swarm optimization algorithm. a two step optimization model is developed and solved with the particle swarm optimization method. the model first carries out an exhaustive enumeration search for all feasible sets of p...

Dynamic optimization problems present great challenges to the research community because their parameters are either revealed or changed during the course of an ongoing optimization process. These problems are more challenging than static problems in real-world applications because the latter are usually dynamic, with the environment constantly subjected to change or the size of a problem incre...

Engineering optimization needs easy-to-use and efficient optimization tools that can be employed for practical purposes. In this context, stochastic search techniques have good reputation and wide acceptability as being powerful tools for solving complex engineering optimization problems. However, increased complexity of some metaheuristic algorithms sometimes makes it difficult for engineers t...

There are several variations on gubernatorial economic policy around the world. Given different populations and demographics, economic policy changes. How can we best predict the ramifications of a given policy? Can we produce an optimal policy? Computer simulations and optimization using genetic algorithms may be able to provide policy makers with the data to answer these questions.

A hybrid evolutionary algorithm is applied to the optimization of space missions with multiple impulses and gravity assists. The optimization procedure runs three different optimizers, based on genetic algorithms, differential evolution and particle swarm optimization, in parallel; the algorithms are used synergistically by letting the best individuals, found by each algorithm, migrate to the o...