Optimization using variations of simulated annealing

Kalivas, John H.

doi:10.1016/0169-7439(92)80022-v

Cited by 38 publications

(20 citation statements)

References 21 publications

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“…But in SA there is still a probability, p, for the removal of the variables (descriptors) which are not confirmed to be eliminated in previous iterations. Considering of such probability causes to jump out of a local minimum [241,242].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Chemometrics tools in QSAR/QSPR studies: A historical perspective

Yousefinejad

Hemmateenejad

2015

Chemometrics and Intelligent Laboratory Systems

119

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Section: Accepted Manuscriptmentioning

confidence: 99%

Chemometrics tools in QSAR/QSPR studies: A historical perspective

Yousefinejad

Hemmateenejad

2015

Chemometrics and Intelligent Laboratory Systems

119

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“…It is obvious that, because of the restrictions R 1 and R 2 , the result of operation (19) depends on the sequence with which each one of the members of A is operated upon by the corresponding component of m. Therefore, starting always with the first member of A would give an unjustified special preference to agent 1 (k ¼ 1 in Definition 16). For this reason, a starting index for operation (19) is chosen at random from the range f1; 2; .…”

Section: Genetic Algorithms and Cellular Automatamentioning

confidence: 99%

“…. ; P are generated by a repeated application of operation (19), employing successively the vectors m ðtÞ I ; I ¼ 1; 2; . .…”

Section: Genetic Algorithms and Cellular Automatamentioning

confidence: 99%

“…A form of the latter is given by Kalivas [18] and the former is described in numerous references (e.g. [19]). The simulated annealing algorithm implemented in this paper is outlined as follows:…”

Section: A Simulated Annealing Approachmentioning

confidence: 99%

“…An initial configuration A (0) is chosen, along with initial values of the well discharges, collected under an initial discharge vector q ð0Þ ¼ ðq The vector q (0) of the initial discharges is perturbed as follows [19]: A vector of n random numbers r ¼ ðr 1 ; r 2 ; . .…”

Section: A Simulated Annealing Approachmentioning

confidence: 99%

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Genetic algorithms and cellular automata in aquifer management

Sidiropoulos

Tolikas

2008

Applied Mathematical Modelling

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A groundwater management problem is presented involving pumping cost minimization with both well discharges and well locations as decision variables. A grid of candidate well locations is set up and optimal arrangements of wells are sought within this discrete space. A genetic algorithm approach is presented with the following particular features: (a) A suitable scaling is applied to the objective function in order to alleviate its regionally flat behavior. (b) No penalty functions are involved in constraint handling. Instead, the feasible region is transformed into a rectangular domain. The transformation introduced is proved to be bijective. (c) A binary representation of well configurations is presented and compared to a combinatorial one. The binary representation necessitates the introduction of specially designed genetic operators. Besides purely genetic algorithms, the concept of cellular automaton is introduced as the basis of an alternative formulation of the optimization problem. The lattice of the cellular automaton provides the discrete set of candidate well positions. The well configuration is represented by a group of agents occupying an equal number of lattice sites. The agents change positions as dictated by the structure of the automaton and, also, by an associated genetic algorithm, which directs the evolution of the whole scheme toward an optimal configuration. An improved performance of this approach is noted and discussed in comparison to the purely genetic algorithm schemes of the present work. A simulated annealing approach is also applied to the same problem for comparison purposes. Finally, a new and more efficient hybrid annealing-genetic approach is introduced and discussed.

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Cost-optimal replenishment of chemical baths: an application of linear programming

Wieringa

2000

J. Chemometrics

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The concentrations of components in chemical baths used in industrial applications often have to meet certain requirements. Production or evaporation may change the relative amounts of the components so that some of the concentrations fall outside their limits, resulting in output deterioration. Determining how much should be added of each of the components is not as straightforward as it may seem at first sight. In this paper we describe a case where this problem was encountered in a quality improvement project at the factory of Philips Semiconductors Stadskanaal, the Netherlands. A linear programming model is developed that can be used to determine the cheapest set of additions under the restriction that the new concentrations in the bath meet their requirements. The simplex algorithm can then be used to solve this problem. An interesting feature of this application is that a standard basic feasible solution that is needed to start up the algorithm is available for every instance of the problem. The results of the method are compared with those of an old method that was used for computing additions. It turns out that for the cases considered, a cost reduction of about 60% is possible. Copyright © 2000 John Wiley & Sons, Ltd.

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Optimization using variations of simulated annealing

Cited by 38 publications

References 21 publications

Chemometrics tools in QSAR/QSPR studies: A historical perspective

Chemometrics tools in QSAR/QSPR studies: A historical perspective

Genetic algorithms and cellular automata in aquifer management

Cost-optimal replenishment of chemical baths: an application of linear programming

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