Near-optimal solutions

This is a very important target since it corresponds to the maximum energy recovery that can be attained in a feasible HEN for a fixed HRAT. This target allows for the elimination of several HEN structures which are not energy efficient and leads to near-optimal solutions as long as the energy is the dominant cost item compared to the investment cost. 

GAs are probabilistic search methods based on the mechanics of natural selection and genetics. The basic idea in using a GA as an optimization method is to represent a population of possible solutions in a chromosome-type encoding, called strings, and evaluate these encoded solutions through simulated reproduction, crossover, and mutation to reach an optimal or near-optimal solution. The GA starts with the creation of an initial population of... 

By using computer simulation accurate near-optimal solutions can be found with relatively reduced effort quickly by the sequencing of zeotropic mixtures. The following strategy can be applied ... 

For a limited number N of original features, exhaustive search (ES) for the best subset(s) is feasible. For larger N, we have developed a dynamic programming (DP)-based algorithm51 that often produces near-optimal solutions, in feasible computer times. 

Batch copoly (ethylene- polyoxyethylene terephthalate) reactor Minimization of both reaction time and undesired side products. NSGA-II, NSGA-II-JG andNSGA-II- aJG At near-optimal solutions, NSGA-II-JG was observed to be faster than the other two methods. Kachhap and Guria (2005)... 

Solving the VRP determines which vehicle serves which stops and in what sequence. There are different solution methodologies for solving the VRP, either optimally or heuristically. Since optimal algorithms can solve only small problems, emphasis is given to heuristics algorithms that aim at finding near-optimal solutions. 

Methods that find feasible and possibly near optimal solutions to the CAP have also been proposed, see for example Akcoglu et al. [1]. Also any of the approximation algorithms for SPP would apply as well. 

Thus, the present study aimed to develop the algorithm named unrelated parallel machine with sequence-dependent setup times (UPSDST) in order to resolve the massive problem and gain a near-optimal solution. The details of UPSDST and variables and symbols used to create UPSDST are defined below. 

Although we could find an optimal solution for this small example problem in rather short time, any IP solver such as CPLEX as well as LINGO cannot provide an optimal solution for a practical-sized problem in a reasonable CPU time because the problem is known as NP-hard. Hence, we focus on heuristic algorithms including genetic algorithm to obtain near-optimal solutions of the problem in a reasonable CPU time. 

After the candidate downstream gene modules are selected by GA, FSO is proposed to determine the parameters in the NN model. Particle swarm optimization is motivated by the behavior of bird flocking or fish blocking, originally intended to explore optimal or near-optimal solutions in sophisticated continuous spaces (Kennedy and Eberhart 1995). Its main difference from other evolutionary algorithms (e.g., GA) is that PSO relies on cooperation rather than competition. Good solutions in the problem set are shared with their less-fit ones so that the entire population improves. 

The reported BA-MIMO detector views this MDVfO-OFDM symbol detection issue as a combinatorial optimization problem and tries to approximate the near-optimal solution iteratively. 

Increase in system configuration (NtxNr), results in exponential increase of search space, therefore more algorithm iterations are required to converge to near-optimal solution. A trade off between systems BER performance and iterations has to be maintained according to the system requirement and priority. 

The Algorithm was used with different initial population sizes and various parameters and, nine out of ten times, it was capable of finding a near optimal solution in less than a hundred generations. 

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