Overview of Multiobjective Optimization - Multiobjective Optimization Methodology: A Jumping Gene Approach - page 10

Table 2.1
Examples.of.Deterministic.
Optimization.Methods
DeterministicMethods
Greedy.[9,.18,.45]
Hill.climbing.[61]
Branch.and.bound.[11,27,43]
Depth.first.[56]
Breadth.first.[56]
Best.first.[56]
Calculus.based.[2]
Mathematical.programming.[41,53]
optimized. solution. is. located.. Therefore,. if. a. set. of. optimal. or. subopti-
mal.solutions.is.desired,.such.as.when.handling.MOPs,.multiple.runs.are.
needed.
2.1.3 Stochastic Methods
Rather.than.using.enumerative.and.deterministic.methods.for.solving.MOPs,.
stochastic. methods. are. more. useful. [12].. Stochastic. methods. can. acquire.
multiple.solutions.in.a.single.simulation.run.and.have.their.own.schemes.to.
prevent.trapping.in.the.local.optima.[19].
Usually,. an. encode/decode. mapping. mechanism. between. the. problem.
and. algorithm. domains. is. needed.. Furthermore,. a. specific. function. is.
required.to.reflect.the.goodness.of.the.solutions..Production.of.true.Pareto-
optimal. solutions. is. not. guaranteed.. Nonetheless,. they. can. offer. reason-
ably.good.solutions.to.MOPs.for.which.deterministic.methods.fail.[39,44]..
Some.typical.examples.of.stochastic.methods.are.listed.in. Table 2.2 .
Table 2.2
Examples.of.Stochastic.Optimization.Methods
StochasticMethods
Random.search/walk.[64,68]
Monte.Carlo.[34,59]
Simulated.annealing.[49,60]
Tabu.search.[35-38]
Swarm.algorithms.[15-17,46]
Evolutionary.computation/algorithms.[5,6,24,28]