multiobjective optimization -凯发k8网页登录
pareto sets via genetic or pattern search algorithms, with or without
constraints
when you have several objective functions that you want to optimize simultaneously, these solvers find the optimal tradeoffs between the competing objective functions.
functions
objects
values for optimization problems |
live editor tasks
optimize | optimize or solve equations in the live editor |
topics
problem-based multiobjective optimization
how to set up and evaluate results of multiobjective optimization problems.
this example shows how to create and plot the solution to a multiobjective optimization problem.- plan nuclear fuel disposal using multiobjective optimization
plan the disposal of spent nuclear fuel while minimizing both cost and risks. this example has both continuous and binary variables.
solver-based multiobjective optimization
shows an example of how to create a pareto front and visualize it.- design optimization of a welded beam
shows tradeoffs between cost and strength of a welded beam. - compare paretosearch and gamultiobj
solve the same problem usingparetosearch
andgamultiobj
to see the characteristics of each solver.
solve a simple multiobjective problem using plot functions and vectorization.
shows the effects of some options on thegamultiobj
solution process.
describes cases where hybrid functions are likely to provide greater accuracy or speed.
plot a pareto set in three dimensions.
multiobjective background
describes pareto-optimal sets.
how thegamultiobj
algorithm works.
describes theparetosearch
algorithm.
describes differences between the options forga
andgamultiobj
.- genetic algorithm options
explore the options for the genetic algorithm. - pattern search options
explore the options for pattern search.