Heuristics based algorithm

Zaidi Q 1998 Identification of illuminant and object colors heuristic-based algorithms. Journal of the Optical Society of America A 15(7), 1767-1776. 

The number of neurons to be used in the input/output layer are based on the number of input/output variables to be considered in the model. However, no algorithms are available for selecting a network structure or the number of hidden nodes. Zurada [16] has discussed several heuristic based techniques for this purpose. One hidden layer is more than sufficient for most problems. The number of neurons in the hidden layer neuron was selected by a trial-and-error procedure by monitoring the sum-of-squared error progression of the validation data set used during training. Details about this proce-... 

A true optimization of decisions based on exact or heuristic optimization algorithms... 

Early applications of MPC took place in the 1970s, mainly in industrial contexts, but only later MPC became a research topic. One of the first solid theoretic formulations of MPC is due to Richalet et al. [53], who proposed the so-called Model Predictive Heuristic Control (MPHC). MPHC uses a linear model, based on the impulse response and, in the presence of constraints, computes the process input via a heuristic iterative algorithm. In [23], the Dynamic Matrix Control (DMC) was introduced, which had a wide success in chemical process control both impulse and step models are used in DMC, while the process is described via a matrix of constant coefficients. In later formulations of DMC, constraints have been included in the optimization problem. Starting from the late 1980s, MPC algorithms using state-space models have been developed [38, 43], In parallel, Clarke et al. used transfer functions to formulate the so-called Generalized Predictive Control (GPC) [19-21] that turned out to be very popular in chemical process control. In the last two decades, a number of nonlinear MPC techniques has been developed [34,46, 57],... 

Synthesis is the first and represents the activity of generating structural designs. When the design specifications are given as an input, the structure is synthesized by heuristic reasoning, algorithmic procedures, or based on the interaction with the user. During the synthesis phase, all the process... 

Heuristic genetic algorithms have been applied to job shop scheduling. In these genetic schemes, problem specific heuristics are incorporated in the recombination operators (such as optimization operators based). 

The meta-heuristic algorithm, namely FA, which idealizes some of the flashing characteristics of fireflies, has been recently developed by Xin-She Yang [30]. Although the FA has many similarities with other swarm intelligence based algorithms, it is indeed much simpler both in concept and implementation [32]. There are already several applications of FA to different optimization problems [2,5,15,20,22,31-33]. The authors reported that the FA is powerful and very efficient novel population-based method and can outperform other meta-heuristics, such as GA, in solving many optimization problems and particularly NP-hard problems. 

Keywords Traveling Salesman Problem, Nature-based Algorithms, Heuristic Algorithms, Ant Colony Optimization Algorithms. 

The optimization of the backtracking algorithm usually consists of an application of several heuristics which reduce the number of candidate atoms for mapping from Gq to Gj. These heuristics are based on local properties of the atoms such as atom types, number of bonds, bond orders, and ring membership. According to these properties the atoms in Gq and Gj are separated into different classes. This step is known in the literature as partitioning [13]. Table 6.1 illustrates the process of partitioning. 

Since the program (DEP) represents a mixed-integer linear program (MILP), it can be solved by commercially available state-of-the-art MILP solvers like CPLEX [3] or XPRESS-MP [4], These solvers are based on implementations of modem branch-and-bound search algorithms with cuts and heuristics. 

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