By Ashish M. Gujarathi, B. V. Babu

ISBN-10: 131536638X

ISBN-13: 9781315366388

ISBN-10: 1771883367

ISBN-13: 9781771883368

ISBN-10: 1771883375

ISBN-13: 9781771883375

Edited via execs with years of expertise, this book provides an creation to the speculation of evolutionary algorithms and unmarried- and multi-objective optimization, after which is going directly to speak about to discover functions of evolutionary algorithms for plenty of makes use of with real-world functions. protecting either the speculation and purposes of evolutionary computation, the e-book bargains exhaustive insurance of numerous themes on nontraditional evolutionary recommendations, information operating rules of latest and renowned evolutionary algorithms, and discusses case experiences on either medical and real-world purposes of optimization

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The temperature and flow rate of each coolant stream entering a reaction/cooling zone is used to control the temperature profile in the reactor. A mixture of ethylene, a free-radical initiator system, and a solvent are injected at the entrance of reactor. Maximization of monomer conversion is one of the objectives to be considered during MOO of LDPE. While maximizing the conversion, the undesirable side chain concentration (sum of methyl, vinyl, and vinylidene) also increases. Thus, minimization of unwanted side products and maximization of monomer conversion gives rise to conflicting set of objectives.

Generally, best 1/10 of the parent chromosomes are denoted as queens in each generation based on their ranking and crowding distance. Thus, the queen chromosomes are updated in every generation. If a chromosome is good, it survives in the process of elitism and is consistently selected as a queen over the generations. In crossover operation, one of the queens selected randomly swaps the genetic content with the chromosome selected randomly from the entire population to produce two offspring. The rest of the procedure is same as that of the original algorithm.

The termination criteria are checked, and if not terminated, the offspring is generated after applying the corresponding genetic operators. Selection is performed based on the objective function values of the parent and the offspring. The algorithm continues until the termination criteria are met. While achieving these two goals of MOO and dealing with two search spaces, the search for the true Pareto front in case of MOO study depends upon the following key issues: • number and type of decision variables (continuous, discontinuous) and nature of decision variable space; • type of objective functions (minimization, maximization) and nature of objective space; • nonlinearity and stiffness of model equations; • type of constraints (equality, inequality); • ability of algorithms to handle the search spaces of objectives and decision variables.

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Evolutionary computation : techniques and applications by Ashish M. Gujarathi, B. V. Babu

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