نتایج جستجو برای: Differential evolution

تعداد نتایج: 614694  

Journal: :iranian journal of chemistry and chemical engineering (ijcce) 2015
saied afshari babak aminshahidy mahmoud reza pishvaie

determining the optimal location of wells with the aid of an automated search algorithm is a significant and difficult step in the reservoir development process. it is a computationally intensive task due to the large number of simulation runs required. therefore,the key issue to such automatic optimization is development of algorithms that can find acceptable solutions with a minimum number of...

Journal: :international journal of industrial engineering and productional research- 0
هادی مختاری hadi mokhtari kashanدانشگاه کاشان اشکان مزدگیر ashkan mozdgir k.n. toosiدانشگاه خواجه نصیر الدین طوسی

assembly lines are special kinds of production systems which are of great importance in the industrial production of high quantity commodities. in many practical manufacturing systems, configuration of assembly lines is fixed and designing a new line may be incurred huge amount of costs and thereby it is not desirable for practitioners. when some changes related to market demand occur, it is wo...

Journal: :journal of advances in computer research 0

in recent years, soft computing methods have generated a large research interest. the synthesis of the fuzzy logic and the evolutionary algorithms is one of these methods. a particular evolutionary algorithm (ea) is differential evolution (de). as for any ea, de algorithm also requires parameters tuning to achieve desirable performance. in this paper tuning the perturbation factor vector of de ...

Journal: :international journal of optimaization in civil engineering 0
v. nandha kumar c. r. suribabu

optimal design of cantilever reinforced concrete retaining wall can lead considerable cost saving if its involvement in hill road formation and railway line formation is significant.  a study of weight reduction optimization of reinforced cantilever retaining wall subjected to a sloped backfill using differential evolution algorithm (dea) is carried out in the present research.  the retaining w...

Journal: :international journal of optimaization in civil engineering 0
r. mansouri m. mohamadizadeh

for any agency dealing with the design of the water distribution network, an economic design will be an objective. in this research, central force optimization (cfo) and differential evolution (de) algorithm were used to optimize ismail abad water distribution network. optimization of the network has been evaluated by developing an optimization model based on cfo and de algorithm in matlab and ...

Journal: :journal of agricultural science and technology 2015
o. adekanmbi o. olugbara

this paper presents a model for constrained multiobjective optimization of mixed-cropping planning. the decision challenges that are normally faced by farmers include what to plant, when to plant, where to plant and how much to plant in order to yield maximum output. consequently, the central objective of this work is to concurrently maximize net profit, maximize crop production and minimize pl...

C.R. Suribabu, R. Deepika,

The shape optimization of gravity dam is posed as an optimization problem with goals of minimum value of concrete, stresses and maximum safety against overturning and sliding need to be achieved. Optimally designed structure generally saves large investments especially for a large structure. The size of hydraulic structures is usually huge and thus requires a huge investment. If the optimizatio...

Determining the optimal location of wells with the aid of an automated search algorithm is a significant and difficult step in the reservoir development process. It is a computationally intensive task due to the large number of simulation runs required. Therefore,the key issue to such automatic optimization is development of algorithms that can find acceptable solutions with a minimum numbe...

2010
Magnus Erik Hvass Pedersen

The general purpose optimization method known as Differential Evolution (DE) has a number of parameters that determine its behaviour and efficacy in optimizing a given problem. This paper gives a list of good choices of parameters for various optimization scenarios which should help the practitioner achieve better results with little effort.

2006
Nelson Wu

Created in 1994, Differential Evolution (DE) is relatively new but already has been proven to perform well for optimisation in static environments. But with many realworld optimisation problems having non-static environments, how well can DE perform in these situations? What changes can be done to allow DE to perform better in dynamic environments?

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