Optimization Techniques for Solving Complex Problems (Wiley Series on Parallel and Distributed Computing)
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Real-world problems and modern optimization techniques to solve them
Here, a team of international experts brings together core ideas for solving complex problems in optimization across a wide variety of real-world settings, including computer science, engineering, transportation, telecommunications, and bioinformatics.
Part One—covers methodologies for complex problem solving including genetic programming, neural networks, genetic algorithms, hybrid evolutionary algorithms, and more.
Part Two—delves into applications including DNA sequencing and reconstruction, location of antennae in telecommunication networks, metaheuristics, FPGAs, problems arising in telecommunication networks, image processing, time series prediction, and more.
All chapters contain examples that illustrate the applications themselves as well as the actual performance of the algorithms.?Optimization Techniques for Solving Complex Problems is a valuable resource for practitioners and researchers who work with optimization in real-world settings.
Hybrid algorithm always provides better results here than the original ones, especially in the case of the more constrained instance ( α = 0 . 25). CONCLUSIONS 379 2 0.1383 BS 0.1268 EA Hybrid EABS 0.1153 0.1037 0.0922 0 0.0807 0.0692 0.0576 3 8 0.0461 3 1 % distance to best solution 7 1 0.0346 1 5 0.0231 7 2 1 0.0115 10 10 10 10 10 10 10 10 10 0 0.25/10/100 0.75/10/100 0.25/10/250 0.75/10/250 0.25/10/500 0.75/10/500 0.25/30/100 0.75/30/100 0.25/30/250.
Computing capacity of each machine (in MIPS). The number of jobs and resources may vary over time. Notice that in the ETC matrix model, information on MIPS and workload of jobs is not included. As stated above, this matrix can be computed from the information on machines and jobs. Actually, ETC values are useful to formulate the optimization criteria, or fitness of a schedule. Also, based on expected time to compute values, the ready times of machines (i.e., times when machines will have.
It is clear how the probability matrix M is updated with α = 0.005 and how it affects the remainder cells. The update operations take care that the sum of all the elements of the array will be 1. This array is very useful for keeping information about the selection frequency of a determined chromosome and therefore for helping the mutation process to evolve toward the preferences of the user. 2. Guided mutation operator. The mutation operator is responsible for the mutation of the.
OPTIMIZATION PROBLEMS a stationary TSP with n cities for the first 10 minutes, and after that it behaves as a different instance of the stationary TSP, with n + 1 cities. 2. Continuous DOPs. At least one source of dynamism is continuous (e.g., temperature, altitude, speed). Example 6.2 (Continuous DOP) In the moving peaks problem [9,10] the objective function is defined as m O(s, t) = max B(s), max P (s, hi(t), wi(t), pi(t)) (6.3) i=1 where s is a vector of real numbers that.
Executions was 1.53. For eight processors the maximum speedup expected will be 10.12; that is, 1.53 × 4(fast processors) +4 (slow processors); see Figure 11.2(b). Comparing the three experiments depicted [Figure 11.2 (c)], we conclude that the algorithm EXPERIMENTAL ANALYSIS 187 does not experiment any loss of performance due to the fact of being executed on a heterogeneous network. Figure 11.3(a) represents the average number of nodes visited, for the experiment labeled “ULL 800– 500.