It takes the average reader 5 hours and 30 minutes to read Single and Multiple Objective Optimization by Jd Kolo
Assuming a reading speed of 250 words per minute. Learn more
Single and Multiple Objective Optimization covers a wide range of techniques for optimizing functions. Single objective optimization examines functions and identifies the function minima and maxima over a predetermined range. Single objective optimization is often handled using differential calculus. However, in many applications, obtaining extrema using the methods of calculus is intractable. In these cases alternate techniques are required. Many artificial intelligence techniques are based on identifying a global minimum or maximum when multiple extrema exist. Evolutionary algorithms such as genetic algorithms, differential evolution, and particle swarm optimization were developed as modern alternatives to the standard optimization approaches such as differentiation and Newton's method. These evolutionary techniques are examined and compared against a suite of test functions to measure performance of each technique under a variety of operating conditions. Multiobjective optimization does not typically result in a single optimum value. Instead, a set of incomparable points is identified on the Pareto frontier. These points are not comparable to each other, but are superior to other potential solutions. Although a single operating point is not identified, the optimal value must be among the points in the Pareto frontier. Techniques for identifying the Pareto frontier are examined and tested using a suite of test problems. Some of the techniques examined are the weighted sum method, Normal-Boundary Intersection (NBI), Normal Constraint, Strength Pareto Evolutionary Algorithm (SPEA2), Nondominated Sorting Genetic Algorithm (NSGA2), and Directed Search Domain (DSD). These techniques may be compared in terms of the number of function evaluations, the distribution of points on the frontier, the number of frontier points identified, along with many other performance measures.
Single and Multiple Objective Optimization by Jd Kolo is 330 pages long, and a total of 82,500 words.
This makes it 111% the length of the average book. It also has 101% more words than the average book.
The average oral reading speed is 183 words per minute. This means it takes 7 hours and 30 minutes to read Single and Multiple Objective Optimization aloud.
Single and Multiple Objective Optimization is suitable for students ages 12 and up.
Note that there may be other factors that effect this rating besides length that are not factored in on this page. This may include things like complex language or sensitive topics not suitable for students of certain ages.
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